Study and Analysis of Distribution Equipment Reliability Data

Transcription

1 Study and Analysis of Distribution Equipment Reliability Data Datastudier och analys av tillförlitlighetsdata på komponentnivå för eldistributionsnät Elforsk rapport 10:33 Ying He Mars 2010

2 Study and Analysis of Distribution Equipment Reliability Data Datastudier och analys av tillförlitlighetsdata på komponentnivå för eldistributionsnät Elforsk rapport 10:33 Ying He Mars 2010

3 Förord Denna rapport är framtagen av projektet Tillförlitlighets data på komponentnivå för riskanalys på eldistributionsnät - Del 2: Data studier och analys, som är en fortsättning av det tidigare utfört projektet Tillförlitlighets data på komponentnivå för riskanalys på eldistributionsnät inom FoUprogrammet Riskanalys som drivs av Elforsk. Projektet har genomförts av Ying He, Vattenfall Research and Development AB. Syftet med projektet är att utföra fördjupande studier och analys av felstatistiken på komponentnivå, jämföra befintlig statistik, undersöka felkällor och faktorer som påverkar felstatistiken, och analysera uppdelningen av statistiken med avseende på felorsaker, samt ge ett branschgemensamt underlag till dataanvändningen för riskanalys på eldistributionsnät. Programmets styrgrupp består av följande ledamöter: Arne Bergström, Vattenfall Eldistribution AB Horst Blüchert, Elsäkerhetsverket Mikael Bohjort, E.ON Elnät Sverige AB Leif Boström, Fortum Distribution Håkan Jarer, Svenska Kraftnät Sven-Åke Polfjärd, Föreningen Industriell Elteknik, FIE Sven Jansson, Elforsk AB Bertil Wahlund, Elforsk AB. Finansiärer i programmet är: Vattenfall Eldistribution AB E.ON Elnät Sverige AB Fortum Distribution AB Svenska Kraftnät Göteborg Energi AB Skellefteå Kraft AB Mälarenergi Elnät AB Tekniska Verken i Linköping AB Öresundskraft AB Jämtkraft AB Umeå Energi Elnät AB Jönköping Energi Nät AB

4 Eskilstuna Energi & Miljö AB Gävle Energi AB Energiverken i Halmstad Sundsvall Elnät AB Växjö Energi Elnät AB Borlänge Energi AB Nacka Energi AB Föreningen Industriell Elteknik, FIE Elsäkerhetsverket Lunds Energi AB. Projektet har genomförts i huvudsak av Vattenfall Research and Development AB med medverkan av Svensk Energi och hjälp av följande personer: Matz Tapper, Svensk Energi Gerd Kjølle, SINTEF, Norway Jørn Heggset, SINTEF, Norway Peter Hansen, Danish Energy Association Morten Møller, Danish Energy Association Lisbeth Petersson, Danish Energy Association Elina Lehtomäki, Finnish Energy Industries Przemysław Karaś, Vattenfall Distribution Poland S.A. Anders Holm, granskning, Vattenfall Research and Development AB. Bertil Wahlund Överföring & Distribution Elforsk AB

5 Sammanfattning Kvantitativ riskanalys kräver tillförlitlighetsdata för olika elektriska komponenter. Trovärdiga data har en fundamental betydelse för riskanalys. Utan trovärdiga data kommer resultaten av riskanalys att vara meningslös. I del 1 av projektet genomfördes grundläggande arbete. Arbetet fokuserades på förbättringar av befintligt dataunderlag med datainsamling och kompletteringar från ett antal nätbolag. Det visade sig att det fanns stora variationer och spridningar i datastatistiken. Det var oklart varför och vad som orsakade sådana variationer och det saknas analys av statistiken och jämförande studier. För att få bättre förståelse i tillförlitlighetsdata, har projektet utfört fördjupande studier och analyser kring befintlig statistik. Arbetet har fokuserats på jämförande studier, felorsak, analys och kartläggning av underliggande faktorer som påverkar statistiken. Studierna visar att ett stort antal företag och organisationer världen över har etablerat datorsystem och databaser för felrapporteringar, störningsanalys och bearbetning av statistiken. Det finns dock inga standarder generellt accepterade för datainsamlingar och behandlingar. Undersökningen tyder på att betydande inkonsekvenser och skillnader finns i datakategorisering och insamlingar i olika länder och organisationer. Trots svårigheter i datajämförelser på grund av skillnaderna i datainsamlingar, skillnaderna i system- och statistikklassificeringar, har projektet försökt identifiera gemensamma data och kategorier för att möjliggöra jämförelserna. Projektet studerade den befintliga statistiken på komponentnivå från olika aspekter, inklusive systemkarakterisering, datastruktur, metod för datainsamlingar, klassificering av felstatistik, typiska parametrar och värde som beskriver tillförlitlighet hos komponenter, felorsakskategorier och anläggningsklassificeringar. Resultatet från de jämförande studierna beskrivs i rapporten. Jämförelser av statistiken från olika dataskällor med avseende på datastruktur och komponentklassificering presenteras också i rapporten. Värden av anläggningsstatistiken från del 1 av projektet har jämförts med statistik från ett antal länder. Sammanställningar av dess värde för olika typer av anläggningar presenteras i bilaga till rapporten. Tillförlitlighetsdata hos elnätkomponenter reflekterar stokastiska egenskaper hos komponentfel. Resultaten av projektet visar att betydande skillnader finns i statistik i olika länder. Det är viktigt att förstå denna dynamiska karaktär hos tillförlitlighetsdata. Om möjligt bör felstatistik hos anläggningar studeras regelbundet. Uppdelning på felorsaker till komponentfel har analyserats i projektet utgående från felrapporteringar i DARWin hos Svensk Energi. Analyserna utfördes både på nationell nivå och på enskilda företag. Analyserna visar att felorsaker kan vara olika för olika typer av elnätkomponenter. Huvudorsaker till fel på luftledningar är väder- och omgivningsrelaterade fel, medan dominerande fel på jordkablar är material- och grävningsrelaterade. Det visar

6 sig att åska, materialfel, och bristande underhåll är huvudorsaker till fel på distributionstransformatorer. Tillförlitlighetsdata kan påverkas av olika faktorer. För att ge en bild på felkällor och inverkande faktorer, har projektet studerat olika påverkande faktorer. Resultatet visar att tillförlitlighetsdata kan påverkas av ett antal faktorer, t.ex. tidsperiod, geografi, underhåll, anläggningsålder, tillverkning, spänningsnivå, konstruktion och storlek. En kartläggning av påverkande faktorer bakom statistiken presenteras i rapporten. Hur dessa faktorer kan påverka tillförlitlighetsdata hos olika typer av elnätkomponenter beskrivs också i rapporten. Extrema händelser, t.ex. stormen Gudrun, kan ha förödande effekter på elnät och drabba nätkunder mycket negativt. För att modellera och analysera risker relaterade till Gudrun-stormen krävs tillförlitlighetsdata för elektriska komponenter. Den här typen av statistik har också analyserats i projektet. Projektresultatet visar att felfrekvensen för elnätkomponenter som utsätts för väder kan dramatiskt öka med upp till 80 gånger av det genomsnittliga värdet under extremt väder. Denna dramatiska förändring av tillförlitlighetsdata för komponenter bör tas hänsyn till vid riskanalys av distributionssystem.

7 Summary Meaningful risk and reliability analysis requires reasonable and acceptable component reliability data. The quality of the data has fundamental importance for the risk analysis of a system. If the quality of the data cannot be guaranteed, the results of the reliability analysis will not make sense. In the project performed previously, Component Reliability Data for Risk Analysis of Distribution Systems within the Elforsk R&D program Risk Analysis 06-10, main effort has been expended in developing component reliability data and fundamental work with data collection and data processing. However, the component reliability data produced show that the significant variations in data statistics exist. It was unclear why and by what these data variations were caused, and the statistics needs to be further analyzed. In order to better understand reliability data and get confidence in data use, the project performed detailed study and analysis of existing component failure statistics. The main activities of the project were focused on comparative data study and failure cause and influencing factor analyses. The studies shows that many utilities and organizations throughout the world have established systems and procedures for collecting data and assessing the performance of their electric power equipment. Failure statistics have been topics for many years and failure and interruption data have been collected in various countries. However, there are currently no standards generally accepted for data recording and collections. The survey of recorded information and data scheme used for deducing reliability data indicates that significant inconsistencies exist in the data categorization and collection processes in different countries and organizations. Despite the fact that the comparisons may be difficult to make due to the differences in data collection methods employed, differences in system design and operation, and differences in the environments, the project tried to identify common data set and categories necessary to allow the comparison. In the comparative studies of the project, the differences between the collection methods and the differences in system and data items were studied. The issues and items studied include power system characterization, data categorization structures, data collection methods, classification of failure statistics and outage events, typical parameters and key figures calculated to describe component reliability, definitions of interruptions and outages, failure cause categories, and component classifications. The results of the comparative studies are described in the report. The comparative overviews of data scheme characteristics and structures are provided in the report. The values of failure statistics from different data sources were also compared in the project. The comparative data studies are made on major categories of distribution equipment, and the comparison of the Swedish statistics with the data from other sources are presented in the report and the detailed values are summarized in the Appendix of the report.

8 Component reliability data reflects stochastic nature of component failures and impact of influencing factors. The results of project show that the reliability data can vary widely from system to system, from year to year, and from area to area. It is important to recognize this dynamic feature of reliability data. If possible, study and analyze component failure statistics regularly and produce confident data for risk analysis. To understand the causes of component failures the cause analyses were carried out in the project. The analyses were based on the data from the DARWin database of Svensk Energi. In addition to the nationwide analyses the study of faults reported at individual companies was also performed in the project. The analyses show that the failure causes were different for different types of power components. The analyses confirm that the dominant causes for faults on overhead network components are weather and environment related, while faults on underground distribution equipment are mainly material defect and digging related. The results of analysis indicate that thunderstorm, manufacturer and material defects, and inadequate maintenance were responsible for the majority of distribution transformer failures. The reliability of distribution equipment can be influenced by various factors. In order to understand this influence the project work is devoted to studying and analyzing these factors. The report describes and illustrates how equipment reliability data can be affected by manufacture, voltage level, construction, size, time period, geography, age, operation condition and maintenance. It is shown by the project that due to different influencing factors, component reliability data can exhibit significant variations. It is therefore important to understand this influence and to consider the influencing factors when estimating and using the reliability statistics. In the extreme weather condition, distribution systems could be affected very negative. For this reason, this effect should be considered in the risk analysis, and the project studied how extreme weather affects component reliability. The project analyzed the statistics of component failures during the hurricane Gudrun in January 2005 based on the fault reports from all Sweden and from the power company, E:ON Elnät Sverige AB. The results indicate that the failure rate of distribution equipment exposed to weather could dramatically increase by up to 80 times of the average value during extreme weather condition. This dramatic change of the component reliability data should therefore be taken into account in risk analysis of distribution systems.

9 Innehåll 1 Introduction Background Purpose Data Sources and Collection Data Sources Norwegian Fault Statistics Danish Fault Statistics Finnish Fault Statistics Norstat Statistics Fault Statistics from Other Data Sources Data Structure and Categorization System Characterization Equipment Classification Measures of Component Reliability Data Collected and Data Processing Interruption Causes Comparison of Reliability Data Distribution Lines Distribution Cables Transformers Distribution Substations Circuit Breakers, Switches, and Other Devices Failure Cause Analysis Overhead Lines Underground Cables Distribution Substations Distribution Transformers and Devices Influencing Factors Manufacture, Voltage Level, and Construction Time Period Geographical Location Age Other Factors Impact of Extreme Weather 8 Conclusions 9 References Appendix 1: Summary of Data Comparison 75 Table A1-1: Comparison of Failure Rates for Overhead Lines Table A1-2: Comparison of Failure Rates for Underground Cables Table A1-3: Comparison of Failure Rates for Transformers Table A1-4: Comparison of Failure Rates of Substation Table A1-5: Comparison of Failure Rates for Circuit Breakers, Switches and Other Devices

10 11 Appendix 2: Failure Causes for Distribution Equipment 81 Table A2-1: Failure Causes for Overhead Lines, All Sweden Table A2-2: Failure Causes for MV Underground Cables, All Sweden Table A2-3: Failure Causes for Primary Substation, All Sweden Table A2-4: Failure Causes for Secondary Substation, All Sweden Table A2-5: Failure Causes for Distribution Transformers, All Sweden Table A2-6: Failure Causes for Fuse or Apparatus Boxes, All Sweden Appendix 3: Summary of Swedish Distribution Equipment Reliability Data on Hurricane Gudrun Day 87 Table A3-1: Failure Rate of kv Network Components on January 8, 2005, All Sweden Table A3-2: Failure Rate of kv Network Equipment on Jan. 9, 2005, All Sweden Table A3-3: Number of Outages > 3 Minutes During Jan on kv Networks, All Sweden Table A3-4: Variation of Failure Rate of kv Bare Overhead Lines on Jan. 8, 2005, All Sweden Table A3-5: Variation of Failure Rate of kv Insulated Lines in Air on Jan. 8, 2005, All Sweden Appendix 4: Summary of Statistics on Hurricane Gudrun Day of E.ON Elnät Sverige AB 92 Table A4-1: Failure Statistics of kv Network Equipment on Jan. 8, 2005, E.ON Elnät Sverige AB Table A4-2: Number of Outages > 3 minutes During January 2005 on kv Networks, E.ON Elnät Sverige AB Table A4-3: Variation of Failure Rate of kv Bare Overhead Lines on Jan. 8, 2005, E.ON Elnät Sverige AB Table A4-4: Variation of Failure Rate of kv Aerial Insulated Lines on Jan. 8, 2005, E.ON Elnät Sverige AB

11 1 Introduction 1.1 Background A distribution system consists of network components. Failures of the components can lead to a system failure. The component outages are the root cause of a system failure. In the risk analysis of a distribution system, the system performance can be estimated from the knowledge of the reliability data of individual system components. These data describe the component reliability and serve as input data for risk analysis of distribution systems. These data are valuable because they establish the feedback of data from the network operation field, help to identify network weak areas, and allow this feedback information to be used in system analysis and predicting the values in future. Meaningful risk analysis requires reasonable and acceptable reliability data at component level. The quality of the data has fundamental importance for the risk analysis of a system. If the quality of the data cannot be guaranteed, the results of the reliability analysis will not make sense. In the project performed previously, Component Reliability Data for Risk Analysis of Distribution Systems within the Elforsk R&D program Risk Analysis 06-10, main effort has been expended in developing component reliability data and fundamental work with data collection and data processing of a large sample size of distribution outages reported by more than a hundred power utilities in Sweden during , [1]. However, the results of the component reliability data produced indicate that the significant variations in data statistics exist from company to company and from year to year. There was no detail analysis of this statistics and it was unclear why and by what these data variations were caused. In order to aid in using the statistics and to understand the influencing factors behind the statistics, it needs analysis of the statistics and comparative studies. It is therefore intended that this project carries out such analysis and investigates differences in existing statistics from different data sources. 1.2 Purpose The overall purpose of this project is to perform detailed study and analysis of existing component failure statistics, compare the results obtained from the previous project with the existing statistics in other countries, examine the influence factors, and analyze the major causes of component failures. In order to gain better understanding and achieve a certain level of confidence in the use of component reliability data, it is expected that through the in-depth data analyses this project could provide more knowledge about 1

12 component failure statistics and the relevant issues as influence factors, data variances, and causes of failures. The objectives of the project are: Perform comparative study. Compare component failure statistics from different countries, such as Norway, Denmark, Finland, and Germany, as well as from IEEE publications. Compare the data with respect to data structure, component classification, and values of the statistics. Investigate influential factors. Study what factors that may affect the statistics and how. Identify failure sources that exist in the data. Deduce failure statistics of extreme events. Deduce component failure statistics of hurricane Gudrun based on outage reports from Analyze major causes of component failures. Analyze failure distributions with respect to failure causes. 2

13 2 Data Sources and Collection Meaningful component performance statistics are en essential part of risk analysis of distribution systems. Many utilities and organizations throughout the world have established systems and procedures for collecting data and assessing the performance of their electric power equipment. Failure statistics have been topics for many years and failure and interruption data have been collected in various countries. However, there are currently no standards generally accepted for data recording and collections. The survey of recorded information and data scheme used for deducing reliability data indicates that significant inconsistencies exist in the data categorization and collection processes in different countries and organizations. Despite the fact that the comparisons may be difficult to make due to the differences in data collection methods employed, differences in system design and operation, and differences in the environments, the project tried to identify common data set and categories necessary to allow the comparison. When performing comparative studies, the differences between the collection methods and the differences in system and data items were studied. The issues and items studies are listed below: Power system characterization and voltage levels; Data categorization structures; Data collection methods and data collected; Classification of failure statistics and outage events; Typical parameters and key figures calculated to describe component reliability; Definitions of interruptions and outages; Interruption cause categories; Component classifications. 2.1 Data Sources Electrical component reliability data are normally obtained from field outage records and equipment failure reports. The field data are first collected by operational and maintenance personnel together with the associated outage details. The field data are then analysed to create the statistical reliability data. Most utilities store interruption data in large computer databases. These databases normally have different functions for data processing. Some databases may be better organized than others for analyzing reliability data. Some countries have established comprehensive computer systems for recording and assessing the performance of their power systems and have 3

14 created national organizations for facilitating data exchanging. These organizations formulate procedures and manage centralized data collection and processing, and reporting reliability and outage statistics for electric networks and equipment. In Sweden power utilities record and document the outages together with the associated details. Their outage statistics are annually reported to the national database, DARWin, maintained by Swedish energy industry organization (Svensk Energi AB). The DARWin is a comprehensive operation interruption and outage reporting computer system. This database is used to generate the national outage statistics, and gives annual reliability indices such as SAIFI, SAIDI for Sweden as a whole. Over a hundred electrical power utilities in Sweden participate in the data collection and reporting of the national interruption and outage statistics. The database covers all system voltages, and for distribution systems includes voltages of kv. The data identify the major distribution equipment categories, including distribution lines and cables, transformers, fuse boxes, apparatus cabinets, and stations. The database provides fundamental statistics for the practical measure of the equipment performance. It is therefore used as an important Swedish data source in the comparative studies of the project. An overview on the data sources is presented in Figure 2-1. Table 2-1 summaries outage occurrences and records collected in different sources. More details about other individual data sources are described in the subsequent subsections. Svensk Energi, EMI Norway Denmark Finland Poland Project data-base Vattenfall Fortum E.ON Elnät BCP IEEE, USA Figure 2-1: Overview of the data sources. 4

15 Table 2-1: Summary of data sources and outage records. Sustained outages > 3min Outage occurrences Souce Svensk Energi DARWin Fortum Distribution Vattenfall Eldistribution AB Eon Elnät Sverige AB Norwegian FACIT system: outage statistics 1-22 kv, Danish national outage statistics 6-22 kv, Finnish national statistics 2005 Vattenfall Distribution Poland, transformer statistics BCP data: based on statistics in Germany and Switzerland before 2006 IEEE equipment surveys from USA data: based on average statistics before Norwegian Fault Statistics The Norwegian power industry has long traditions in recording and utilisation of fault and interruption data. The need for national co-ordination of this work was early put into focus. To meet the quality requirements a concept for national collection and reporting of component fault and delivery point interruption data was developed in the 1990 s. This concept is denoted as FASIT (Fault And Supply Interruption information Tool) and has been in use since This system has become a national standard for collection, calculation and reporting of reliability data for all voltage levels above 1 kv and is in use by all network companies in Norway. The main purpose of FASIT is to establish a nation-wide coordinated and standardized system for registration, reporting and analysis of failure and interruption data on all voltage levels. It consists of basic requirements, guidelines for data collection, and reporting schemes divided in three voltage levels (< 1 kv, 1-22 kv and > 33 kv). FASIT is mainly designed for collecting power system performance, but it is coordinated with a component-oriented system. Information stored about components and delivery point records can be used together with the FASIT reports to generate useful statistics. The FASIT system enables to record information about: Faults on electric equipment and related information about the failure events (date, type, fault description, component, cause etc.). Delivery point interruptions. Restoration times. 5

16 Fault consequences (affected delivery points, ENS etc.) Various time intervals during the restoration procedure following an outage can be recorded in FASIT. From 2006 reporting of short interruptions ( 3 minutes) became mandatory in Norway. Typical statistics related to restoration times comprise distribution of turnout times for different events and districts, average sectioning time per outage, distribution of fault localisation times for different component types, and average repair time per component. Typical fault statistics produced by the FACIT are the number of faults on various components per year, distribution of fault causes, the number of faults on different sub-components, types, manufacturers, etc, and distribution of fault detection methods. The FASIT system has provided the Norwegian power industry with a practical tool for recording and reporting faults on both power systems and network components. It supports various analyses related to planning and operation of the power system. This standardized system makes it possible for all Norwegian power utilities to collect data on the same basis and facilitates centralized data processing and reporting. The registered interruption and failure data in FACIT serve as foundation for deducing component statistics and calculating key figures. 2.3 Danish Fault Statistics The Danish Regulatory Authority pays great attention to the quality of supply and has thus implemented a reporting scheme. Most Danish DNO s have reported faults and outages to the DEFU for more than 30 years. All customer interruptions caused by incidents are registered from high voltage down to 10 kv. This registered information can be combined with similar information for the low voltage networks and hence make it possible to estimate fault statistics. In order to provide statistical figures and descriptions to illustrate the structure and most recent development in Danish electricity supply, statistical reports on performance of power networks and equipment are regularly published in Denmark. The predominant part of the data basis was collected and processed by Danish Energy Association from the energy companies. According to the general principles in the guidelines for recording interruptions to supply of the Danish Energy Regulatory Authority, every DNO must record information about interruptions to supply lasting one minute or longer. All voltage levels are to be included. From 1 st of January 2006 every fault in the high voltage grid must be recorded, and this is extended to include the low voltage grid from 1 st of January Once a year the DNO s must report network performance key figures to the Danish Regulatory Authority. For each fault that causes an interruption of supply to one or more customers for one minute or longer the DNO has to register the following as a minimum: 6

17 Date and time; Duration; Number of disconnected customers; Disconnected HV points; Voltage level; Whether the interruption was a planned or forced; Cause of the fault. At low voltage level outages causes by faults are registered at feeder level. In case of a fault that results in disconnection of the low voltage feeder, the feeder designation, the outage duration and the number of customers must be recorded. In Demark the network operators record information about every fault that causes interruption to supply. Since the middle of 60ies faults and interruptions in the Danish networks have been subject to statistical treatment. Distribution and transmission network companies carry out statistical survey on a voluntary basis and most of the companies have actually participated in this survey since the very beginning. From the early 70ies interruptions and failures in the kv networks have been treated statistically. In the year 2004, about 80% of the kv networks were represented in the statistical survey. Danish power utilities update continuously their data collection and failure statistics. All voltage levels (i.e. 0.4, 10-20, 30-60, and 400 kv) are now included. In the end of 2007 the statistics cover nearly 90% of the country s electric supply on 6-25 kv networks. This results in significant data material for various statistical analyses. At component level the failure statistics can be deduced for 10 year s periods. The two types of statistics are generated and reported regularly: Component failure statistics, including main causes of the recorded component failures and the number of faults in various types of components. Interruption statistics, which describes the number and duration of customer outages at various power delivery points. 2.4 Finnish Fault Statistics The electricity distribution utilities in Finland have collected national interruption statistics from the beginning of the 70 s and an annual summary report has been published by the Finnish Energy Industries association(et). The network companies deliver annually to the Energy Market Authority ten key indicators of interruptions. These indicators relate to the reliability of electricity distribution and include the duration and number of unexpected and planned interruptions as well as a number of auto-reclosings and interruptions 7

18 on low-voltage networks. Some of the key indicators are weighted with the annual energy fed from secondary substations. In order to produce more detailed data and enable better classification of interruption causes, from 2005 the publications of interruption statistics have changed quite radically. The new method for fault reporting is based on the examination of interruptions in medium voltage networks. The network utilities register and report to ET about information of each individual interruptions. Special attention has been focused on which kind of network components (overhead line, cable etc.) the interruption has occurred on. This makes the information about networks types, and the number and duration of interruptions to be obtained in more detail. In Finland the fault statistics concentrate on the interruptions experienced by the customers. Attention has been paid to the network structures, i.e. overhead line or cable networks, and to the causes of the interruptions. Finnish distribution networks consist mainly of overhead lines. At 0,4 kv level about 4% of the lines are overhead lines, 66% aerial cables and 30% underground cables. At 1 45 kv level 84,6% of the lines are overhead lines, of which 5,4% with covered conductors, 0,4% aerial cables, and 9,6% underground cables. The Finnish fault statistics are based on the reports from 59 (out of 90) distribution network companies, which cover approximately about 82% of the whole networks in proportion to the total length of the medium voltage networks. 2.5 Norstat Statistics In the Nordic countries there has been a co-operation on the transmission level for many years to standardise fault statistics. On the distribution level there is work going on to standardise the fault collection and reporting as well. A joint Nordic working group, named Norstat, was established in 2005 to maintain the guidelines and to publish annual interruption statistics of each country. The published Norstat statistics cover a significant part of the 1-70 kv networks in he Nordic countries. The figures are broken down into two intervals, 1-39 kv and kv. However the fault and interruption statistics published in the Norstat report are for very limited time of periods. Only the data for the year 2005 are available. The Norstat statistics cover approximately 80-90% the Nordic 1-39 kv networks and 80-95% the kv networks in Denmark and Norway. Due to limited data comparability the data are used at a more aggregated level than actually described in the guidelines. Fault frequencies are deduced for the selected major components. The fault frequencies are at a comparable level in the four countries, but some minor differences can be observed. The Norstat component failure statistics for a single year must be used with great caution. 8

19 The Norstat report [22], which is the first annual statistics published by the working group, presents some of the available statistical figures, these statistics are used as a data source for the project. 2.6 Fault Statistics from Other Data Sources Canadian Fault Statistics IN 1975, the Canadian Electrical Association (CEA) created a facility for centralized collection, processing and reporting of reliability and outage statistics for electrical generation, transmission and distribution equipment. To coordinate the development of this Equipment Reliability Information System CEA constituted the Consultative Committee on Outage Statistics. The outage statistics made available through this collection and analysis process are sufficient for evaluating the reliability of generation, transmission and distribution systems. All the major electric power utilities in Canada participate in a single data collection and analysis system called the Equipment Reliability Information System (ERIS). CEA started firstly collecting data on generation equipment in The second stage was on transmission equipment in The third stage, dealing with distribution equipment, was completed in 1993, and Canadian utilities are now supplying data based on agreed procedures. The ERIS system identifies the states of power equipment and provides continuous records of the operating and outage history of power equipment. The ERIS database contains over hundreds of thousand components and consists of six related entities: utility, manufacturer, outage cause, equipment, configuration and event. One can gain access to the database by utility or manufacturer or equipment or cause and of course retrieve events according to these parameters. Distribution equipment reporting system deals with seven major components of distribution equipment: lines, cables, power and distribution transformers, switching devices, regulators and capacitors. The database contains design information for all components as well as details on all forced outages that occurred. This is collected for each participating utility. Every change in the equipment state is monitored and thus a wealth of statistics can be produced. The following statistics can be determined for each component and for each voltage classification: Number of outages (incl. number of forced outages); Equipment failure rate (incl. forced outage rate); Total outage time; Mean duration; Unavailability. The top causes of outages are identified in each equipment categories. The above statistics can be compiled by primary cause, failure mode, interruption 9

20 medium, and system structure. The equipment status report containing the failure statistics is published annually. Fault Statistics from IEEE and USA The IEEE Society conducts surveys on the reliability of electrical equipment in industrial and commercial installations. Each survey has a defined objective of obtaining field data on electrical equipment failure characteristics. The failure characteristics of individual pieces of electrical equipment are described by the following basic reliability statistics: Failure rate, expressed as failures per year per component (failures per unit-year). Downtime to repair and replace a component after it has failed in service, expressed in hours per failure. In some cases, probability of operating. The statistics from IEEE publications and reports available for the project consist of two parts, Part 1, most recent equipment reliability surveys conducted between 1976 and 1989 and Part 2, equipment surveys conducted prior to The reliability data collected by the IEEE surveys cover the data for major equipment categories of lines and cables, transformers, motors, circuit breakers, generators, switchgear bus, switches, bus ducts, cable joints, cable terminations, inverters, rectifiers. The equipment reliability data from USA are mainly based on the publications of the results of nationwide surveys of recorded information and equipment failure statistics. The reliability data presented and suggested for use in reliability analysis in some papers from USA are also used for the comparative studies of the project. Fault Statistics from Vattenfall Distribution Poland Vattenfall Distribution Poland collects data on equipment performance. The data contains interruptions that occur on distribution networks due to failures on, among other equipment, distribution lines and cables, as well as power and distribution transformers. The company has performed a number of studies on equipment reliability. Associated with these studies, the characteristics and variations of the reliability data were examined. The significant results obtained from the data studies were used in the project. BCP Fault Statistics Over recent years, there has been a significant development in software for reliability analysis of power systems. Busarello Cott Partner Inc. (BCP) in Switzerland is one of the developers of such computer programs. NEPLAN, 10

21 developed by BCP has become one of the important tools for power network reliability analysis. In order to provide the NEPLAN program with the default reliability input data, BCP studied and collected equipment failure statistics required for reliability assessment mainly from European countries. The BCP databank contains fault statistics for MV networks including major component categories: station, transformer, overhead-line, cable, and switch devices. Since the BCP statistics are based on equipment performance records in European countries and are relevant for the comparison. They are therefore used as a data source in the project. 11

22 3 Data Structure and Categorization Reliability of electric power systems is an important issue for the society. Security and service interruptions at the distribution level are the primary concern of the end-use customers. Much effort has been expended in developing procedures and methods in order to assess uniformly and consistently the reliability of distribution systems based on past performance. However, the results of survey of recorded information used for deducing equipment failure statistics and for calculating distribution reliability in different countries indicate that significant inconsistencies exist in the data, in the collection processes, and in the data categorization. Although many questions have been answered about the component reliability, there are still no generally accepted standards for recording or measuring interruptions and component failure events. Presently there are no standardized ways to compare and track component reliability among different utilities and different countries. This Chapter gives an overview of data characteristics and categorization structures in different data sources necessary for data comparison. The similarities and consistence that exist among different data sources are given in the subsequent sub-sections. The characteristics of overall data scheme and structure presented include: System characteristics; Equipment categories; Reliability data measurement; Data collected and process; Interruption causes and durations; Restoration methods. 3.1 System Characterization Network categorization is necessary for data comparison of distribution equipment and is an important issue for data collection system. The characterizations of utility distribution systems are usually categorized by voltage level, customer density or type of distribution lines. IEEE Task Force on Interruption Reporting Practices defined the characterizations of the utility system by the customer density per kilometre [2]. The networks are broken into the three categories as below: Rural networks (less than 31 customers/km); Suburban networks (31 though 93 customer/km); Urban (greater than 93 customers/km). 12

23 In Norwegian fault reporting system FACIT, the distribution networks are classified into three groups for calculation of key figures: Overhead line distribution network (with more than 90% overhead line length); Cable distribution network (with more than 90% cable length); Mixed distribution network (with less than 90% overhead line and cable length). Voltage level is generally used to categorize the networks. Two voltage levels are normally utilized: low-voltage (LV) (0,4 kv, < 1kV), and medium-voltage (MV). Table 3-1 summarizes the network categorizations used in different countries. Table 3-1: Network categorizations used in different countries. Sweden Norway Finland Distribution LV network: 0,4-1 kv; Distribution MV network: kv. Distribution overhead line network (with more than 90% overhead line). Cable network (with more than 90% cable), Mixed network (with less than 90% overhead line and cable). < 1kV, 1-22 kv. Distribution LV network: < 1 kv; Distribution MV network: 1-35 kv. Denmark Distribution LV network: 0,4 kv; Distribution MV network: 6-25kV. BCP Distribution network: 10 kv, 20kV IEEE Task Force suggestion Rural system (less than 31 customers/km), Suburban system (31-93 customer/km), Urban system (more than 93 customers/km). LV, 5 kv; 15 kv; 25 kv; 35 kv. 3.2 Equipment Classification In order to provide the means for recording the outages, distribution equipment are divided into a number of component categories, thus the distribution systems can be treated as component-oriented. Several major component categories are normally used for this purpose, each of which is 13

24 further divided into sub-categories in order to identify more precisely which part of the major component was involved or damaged in the outage. From fault data, outage statistics can be calculated and the reliability data for each type of component can be made. In most of the data collection systems the following distribution equipment are selected as major components: Distribution line; Distribution cable; Power and distribution transformer; Switching device; Protection device. All major components may be classified according to their distinguishing characteristics, such as voltage class, manufacturer and rating, so that each major component is grouped sufficiently well for describing its performance. Two kinds of sub-components are generally recognized: those that are integral to the major component and those that are peripheral subcomponents. The peripheral subcomponents may cover such items as control and protection equipment, surge arrester, busbar, voltage and current transformer. In 2006 IEEE Task Force on Interruption Reporting Practices provided a recommended list of equipment classification for examining equipment performance [2]. This equipment is usually failed network component that initiated the customer interruption. Cable; Wire; Control; Interrupting device; Lightning/surge arrester; Other equipment; Structural support; Switch; Transformer. In this IEEE classification, the cable category includes all cable that is direct buried or encased in pipe or conduit. Wire refers to overhead strung conductors and jumpers. The control category contains control equipment. The interrupting device category consists of circuit breakers, re-closers, and fused equipment. The structural support category includes poles, etc. The switch category contains disconnect or isolation device, load break switch, etc. 14

25 The category transformer can include current, distribution, grounding, voltage and power transformers. In Norway the equipment is structured in a three level hierarchy: unit, major component and sub-component. The unit is highest level in the hierarchy and may contain a group of component as transformer station. Table 3-2 gives an overview of the component categories used in different countries and databases. It can be seen that some use broad categories only, and some classifications contain more items for fault reporting. Table 3-2: Overview of equipment classification. Sweden Norway Finland Denmark Open wire bare conductor line; Open wire insulated line; Aerial cable; Aerial spiral cable; Underground cable; Distribution transformer; Circuit breaker (indoors, outdoors); Switch (indoors, outdoors); Load switch; Fuse and apparatus box; T-junction cable cabiet; Secondary substation; Unknown. Line; Cable; Power transformer; Distribution transformer; Circuit breaker; Load-switch; Fuse; Busbar; Secondary substation; Surge arrester; Voltage transformer; Current transformer; Control; Others. Bare conductor line; Covered conductor line; Underground cable; Transformer station; Circuit breaker; Disconnector. Overheadline; Surge arrester; Underground cable; Cable equipment; Circuit breaker; Switch devices; Transformer. Norstat BCP Canada Overhead line (uninsulated, insulated); Cable (underground cable, oil-paper cable, XLPE cable); Secondary substation; Power transformer; Circuit breaker; Load break switch; Disconnectors; Unknown. Overheadline; Cable; Transformer; Circult breaker; Disconnector switch; Busbar; Compact station; Inhouse station; Tower station. Distribution line; Distribution cable; Distribution transformer; Power transformer; Switching device. IEEE Task Force suggestion Cable; Wire; Connector; Control; Interrupting device; Lightning/surge arrester; Other equipment; Structural support; Switch; Transformer. 3.3 Measures of Component Reliability Component reliability data describe reliability of distribution equipment. The reliability data have been defined to monitor frequency and duration of component failures. There are two basic categories of the reliability data: frequency-oriented data and duration-oriented data. From component outage data collected the statistics can be calculated to provide both a measure of component past performance and a forecast of 15

26 expected performance in the future. Equipment failure rate and repair time are the two main categories of parameters that would be calculated from the collected data. By combining a wide variety of information on the performance of distribution equipment, various parameters may be deduced to describe component reliability, which include: Momentary component failure rate: the frequency of faults that will clear themselves if the fault site is de-energized and then reenergized. Sustained failure rate: the frequency of faults that requires repair. Repair time/outage time/downtime: the time it takes to repair a component outage/failure. Unavailability: the production of failure rate and downtime in year expressed as a percentage. The statistics are normally calculated on the basis of component unit-years or kilometre-years. Since the major components are classified into types, it will be possible to obtain these statistics for each classification. Almost all the countries consider sustained outages. Some of them consider also transient or momentary outages. However the definition of a sustained outage may vary from country to country. Many power utilities consider any outage longer than one minute to be permanent while many define outages longer than three or five minutes as sustained. In practice, there may be little difference in the results because of the different durations used for deducing statistics. In Norway and Finland several interruption durations are considered in deducing failure statistics, including transient outages of 0-3 minutes, and sustained outages longer than three minutes. 3.4 Data Collected and Data Processing The reliability data are basically calculated from historical statistics of component performance. Collecting suitable data is therefore essential for deducing failure statistics. The data collected must be sufficiently completed to ensure to produce desired statistics, but restrictive enough to ensure no to collect unnecessary data items. The basic information collected is data items related to system failure events, which are component outages or customer interruptions. Each failure event is taken into consideration and recorded according to causes of failure, duration of outage, and area of the system affected, etc. The Data collected are normally by number of interruption events, number of customers affected, or by duration of the interruption. Results from this data may reveal rates of failure for various types of equipment. The completeness of data items collected varies from country to country. However most of the countries established the basic form as standard for 16

27 utilities to collect failure data. In their data base the emphasis is on system and component failures. For every failure the data collected in the basic form generally include: Date of fault occurrence; Fault start time and end time; Total time (in minutes or hours) until the last customers are restored; Number of MV/LV power stations involved; Cause of the fault; On what equipment did the fault occur; Components affected; Maximum unavailable power; Description of the event in order to understand what happened. Data collection systems range from manual collection system to computer based outage management system. Typically, majority of power utilities and most of countries have implemented computer outage management system for data collection and processing. The various types of computer software for the processing of the data collected on power equipment were developed. In some countries the software developed for processing of the data collected on distribution equipment uses the transmission system software as a model and meet the same basic system requirements. For data processing the computer database normally contain several related entities, such as utility, manufacturer, outage code (cause, time duration, etc.), equipment, network configuration, etc. One can gain access to the database by utility or manufacturer or equipment or outage code to generate relevant statistics. Although there are similarities among the data processing systems, the differences exist between the data collection methods and in system design that could make the comparison difficult. Examples of the types of items that may be relevant are ability to collect interruption data, use of step restoration when collecting interruption data, determination of the start time and end time, definition of sustained interruption (may ranging from greater than 1 minute to greater than 5 minutes), definition of a customer, etc. 3.5 Interruption Causes The historical data is very useful when analyze failure statistics to ascertain what went wrong in the past. To increase distribution reliability it is necessary to understand the causes of outages. Knowing the cause of an outage can help to reduce the number of specific types of outages. For example, if lightning is a major cause of outages, then lightning arresters can be installed. Tracking outage causes allows power utilities to correct a known 17

28 problem. When recording failure events, it is useful to record some specific causes. Classification of interruption causes has been an important issue in data collection. A component forced outage has been defined in terms of the following primary causes in some data collection systems: Defective equipment; Adverse weather; Adverse environment; Human factors; Foreign interference; Unknown. The IEEE Task Force on Interruption Reporting Practices suggested ten general interruption cause categories for comparison in benchmarking studies [2]. These are intentionally broad categories that will make possible more precise comparisons between different distribution utilities. There are numerous categories that could be chosen, but with the goal of uniformity for comparison purposes, the Task Force recommended the following categories: Equipment; Lightning; Planned; Power supply; Public; Vegetation; Weather (other than lightning); Wildlife; Unknown; Other. The IEEE Working Group on System Design conducted three surveys on distribution reliability index usage. The surveys gave the most commonly used cause codes [3], and the top ten of them are listed below: Tree; Animal; Unknown; Lighting; Equipment; Error; 18

29 Overload; Storm; Dig in; Vandalism. The IEEE Interruption Reporting Practices Task Force suggested weather related sub-cause as below, and recommended to include interruptions due directly to a weather phenomenon where the weather caused the interruption. Wind; Snow; Ice; Hail; Rain. Table 3-3: Overview of cause classification. Sweden Norway Denmark Weather related (tree falls, snow, wind, ice, thunder, rain); External influences (environment related, animal, digging, sabotage, salt, traffic); Material defect; Technique related; Dimensioning failure; Operation and people related (lack of maintenance, overloading, improper method, improper montage, faulted operation). Environment related (thunderstorm, vegetation, wind, snow, salt, animal); Human being related; Operating stress; Construction/montage; Technical equipment, Not classified cause, Others. Weather related (thunder, wind, storm, ice, salt, and rain); Other outside influence; Operation and maintenance; Staff error; Montage fault; Equipment failure; Material, construction and dimensioning failure; Others; Unknown. Norstat Wind, storm; Ice, snow; Lightning; Other nature related; Animals; Digging/excavations; Work/testing; Fire/explosion; Staff; Maloperation; Other people/extenal; Insufficient maintenance; Construction error; Ageing; Wear and tear; Corrosion; Leakage; Other technical equipment; Impact due to other faults; Unknown. Canada Defective equipment, Weather; Adverse environment; Human element; Foreign interference; Tree contacts; Personnel error; Unknown. IEEE Task Force suggestion Equipment; Lightning; Power supply; Public; Vegetation; Weather (other than lightning); Wildlife; Unknown; Other. 19

30 Table 3-3 gives an overview of interruption causes used by different countries and organizations. In Denmark the failure causes include both immediate causes and underlying causes [4]. The immediate causes include thunderstorms, other nature-related events, other external influences, staff fault, operating, and unknown. The underlying causes include material failure, montage failure, operation and maintenance, and others. The weather related outages are classified further to sub-causes: thunder, wind, storm, ice, salt, and rain. In Norway the outage causes are divided into environment related, human being, operating stress, construction/montage, technical equipment, not classified cause, others [5]. The major part of the outages was normally environment related. The sub-causes for environment related outages include thunderstorm, vegetation, wind, snow, salt, animal, not specified, and others. 20

31 4 Comparison of Reliability Data Tracking information about equipment failure can allow utilities to monitor equipment performance and improve system reliability. In order to supply detailed information about component failure statistics, this Chapter give a comparative overview of the statistics from different sources available to the project. Although there is a considerable variance in statistics and in the way to produce the statistics among the different databases, the project attempts the comparison based on the similarities and consistency existing in the different data sources. The comparative studies are made on major categories of distribution equipment including: Distribution lines; Distribution cables; Transformers; Sub-stations; Circuit breakers, switch devices, and other devices. The subsequent sub-sections describe the studies of each type of components. 4.1 Distribution Lines Distribution lines are important distribution equipment. Because this type of equipment is directly exposed to weather, vegetation, and animals, it typically has higher failure rates than distribution underground cables. Failure rates of distribution lines are normally very system specific due to their dependence on environment, and therefore could vary considerably. Figure 4-1 and 4-2 show the average reliability values of all Sweden for LV and MV overhead lines. It is seen that the values of the failure rates have large variations and the values of 2005 are very high. Figures 4-3, 4-4, and 4-5 show the data from Norway. Figures 4-3 and 4-4 show the development of the average fault rates for MV overhead lines in the period Figure 4-6 presents the interruptions caused by failures on overhead lines in Denmark. The failures were from reports registered on overhead lines and associated construction equipment, as insulators. As the figure indicates, the number of interruptions due to failures on the overhead line construction in is above the average of 10-year period. The number of failures on the overhead line network is heavily dependent on the weather. 21

32 Failure rates of LV overhead lines (sustained outages), Sweden Failure rates (f/100km.yr) Bare conductor Inslulated Overhead lines Ave Figure 4-1: Failure rates of LV overhead lines, Sweden. Failure rates of MV overhead lines (sustained outages), Sweden Failure rates (f/100km.yr) Bare conductor Inslulated Overhead lines Ave Figure 4-2: Failure rates of MV overhead lines, Sweden. 22

33 Figure 4-3: Failure rates of MV overhead lines, Norway. From [6]. Figure 4-4: Failure rates of MV overhead lines, Norway. From [5]. 23

34 Number or hours per year Annual number and duration of interruptions at the distribution level, Overhead network Cable network Number of interruptions Annual interruption duration Norway Figure 4-5: Average reliability values of distribution networks, From [8]. Figure 4-7 compares the values of Sweden with the statistics of Norway and Denmark. It is seen that the average values of Sweden is approximately twice as high as the values of Norway and Denmark. Table A1-1 in Appendix 1 summarizes the statistics from all the sources. The comparison of the statistics shows that there are large variations in failure rates reported from different sources. This may be due to that the failure rates of overhead lines are very dependence on weather and environment. An important reliability parameter of overhead distribution systems is the temporary failure rates of overhead lines. Unfortunately available data are scare. The number of observed failures was relatively too small in Sweden to allow acceptable estimations of transient failure rate to be made. The project could not therefore make comparison of reliability data for transient failures. 24

35 Figure 4-6: Failure rates of overhead lines, Denmark. From [4]. Failure rates (f/100km.yr) Failure rates of distribution overhead lines (sustained outages) Ave kv lines, Sw eden 1-22 kv lines, Norw ay 6-25 kv lines, Denmark Figure 4-7: Comparison of failure rates of overhead lines. 25

36 4.2 Distribution Cables Although the majority of distribution systems are overhead, underground distribution networks are steadily increasing in population. Underground equipment is sheltered from weather and vegetation, and usually has lower failure rates than corresponding overhead equipment. Figure 4-8 gives an overview of Swedish statistics of underground cables. The values were quite stable during and are less than one fifth of the failure rates of overhead lines in average. Failure rates (f/100km.yr) Failure rates of underground cables (sustained outages) Sweden LV underground cables MV underground cables Ave Figure 4-8: Failure rates of distribution cables of Sweden. Figures 4-9 and 4-10 present an overview of the statistics from Norway. The failure rates for the cables have been deduced for both permanent and transient failures. In annual average of the period of , approximate 96% of all registered cable faults are permanent, while transient faults are only 4%. The data of Denmark are shown in Figures 4-11, 4-12, and As Norwegian statistics, very little bit of total cable faults is transient. The number of interruptions caused by failure of oil/paper-insulated APB-cables is shown in Figure The statistics are expressed in per 100 km APB cable and include only faults on the cable itself. The number of disruptions caused by failures of PEX-cables is shown in Figure 4-12, and has remained at the same level as the 10-year s average during the last three years. Historically, the failures of per 100 km PEX-cable are 4-5 times lower than the number of failures per 100 km APB-cable. The faults caused by defects in the cable equipment are calculated for two common types of cables. The failures on the cable equipment, including cable joints, etc. are compiled together for both APB and PEX cables and are summarized in Figure The failure rates of cable equipment were recorded per 100 km cable due to that the number of the equipment is usually not fully known. 26

37 Figure 4-9: Failure rates of underground cables, Norway. From [5]. Figure 4-10: Failure rates of underground cables, Norway, From [7]. 27

38 Figure 4-11: Failure rates of distribution cables, Denmark. From [4]. Figure 4-12: Failure rates of distribution cables, Denmark. From [4]. 28

39 Figure 4-13: Failure rates of cable equipment, Denmark. From [4]. The Swedish values are compared with the data from other sources. Table A1-2 in Appendix 1 summarizes the results. Compared the Swedish data with the statistics from Norway and Denmark in Figures 4-14 and 4-15, it is seen that the failure frequencies of all types of cables are in principle at the same level in the three countries. The PEX-cables however show a significantly lower failure rate. The analysis of the data in the table A1-2 reveals that the variations in the data collected from different sources exist, but they are not as large as that in the data of overhead lines. 29

40 Failure rates (f/100km.yr) 3,0 2,5 2,0 1,5 1,0 0,5 0,0 Failure rates of underground cables (sustained outages) Average Cables, kv, Sweden APB-cables, 6-25 kv, Denmark Cables, 1-22 kv, Norway PEX-cables, 6-25 kv, Denmark Figure 4-14: Comparison of failure rates of underground cables. Failure rates (f/100km.yr) Failure rates of underground cables (sustained outages) 3,0 2,5 2,0 1,5 1,0 0,5 0, Ave Cables, kv, Sw eden Cables, 1-22 kv, Norw ay Cables (APB+PEX), 6-25 kv, Denmark Figure 4-15: Comparison of failure rates of underground cables. 4.3 Transformers Reliability data on transformers are necessary for risk analysis of power system reliability. Results of studying transformer failure statistics are presented in Figures from 4-16 to 4-19 and Table A1-3 in Appendix 1. The Norwegian statistics are shown in Figure 4-16 where both transient and permanent faults are included. The statistics confirm the fact that most of transformer failures have long durations. 30

41 Figure 4-16: Failure rates of distribution transformers, Norway. From [5]. Figure 4-17: Failure rates of distribution transformers, Denmark. From [4]. 31

42 The number of faults per 100 distribution transformers at 6-25 kv from Denmark can be seen in Figure The failure rate of transformers for 2007 is slightly below the average for the latest ten-year s period. Transformer faults, however, like the failures of disconnectors and switches, are relatively rare. Vattenfall Distribution Poland studied the faults on their MV transformers recorded during the period The results of the study are given in Figure The failure rates vary within 0,5-0,8 failure/100unit.month. Failure rates of VDP 1,0 0,9 0,8 0,80 0,7 0,64 0,64 0,73 0,68 failures / 100* pcs 0,6 0,5 0,4 0,59 0,55 0,3 0,2 0,1 0,0 Jan-04 Feb-04 March-04 Apr-04 May-04 June-04 July-04 Aug-04 Sep-04 Oct-04 Nov-04 Dec-04 Jan-05 Feb-05 March-05 Apr-05 May-05 June-05 July-05 Aug-05 Sep-05 Oct-05 Nov-05 Dec-05 Jan-06 Feb-06 March-06 Apr-06 May-06 June-06 July-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 March-07 Apr-07 May-07 June-07 July-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 March-08 Apr-08 May-08 June-08 July-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09 March-09 Apr-09 May-09 June-09 July-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 MV transformers failure rate Figure 4-18: Failure rates of MV distribution transformers during , Vattenfall Distribution Poland. Lund University in Sweden conducted a study of Distribution System Component Failure Rates and Repair Times [10]. The study presents the results of a literature search for reported failure rates and repair times, based on operational experience, for distribution system components. As important observations it was concluded that the sustained failure rates for MV/MV transformers published during the recent decade indicate a variation of the average transformer failure rates in the range of failures/100units.year. As a comparison, Figure 4-19 and Table A1-3 in Appendix 1 give an overview of the statistics from different sources. It is observed that the statistics from major sources indicate the failure rates of distribution transformers lower than 1 failure/100units.year. 32

43 Failure rates of distribution transformers (sustained outages) 1,2 0,96 1,0 0,79 0,8 0,63 0,6 0,4 0,29 0,12 0,2 0,0 Sweden, All 1Norway, All Denmark, Poland, Failure rates (f/100units.yr) Germany, Switzerland, before 2006 Figure 4-19: Comparison of average failure rates of distribution transformers. 4.4 Distribution Substations Distribution stations like other network components may experience reliability problems associated with weather and environment. Vattenfall Distribution Sweden estimated the reliability of their substations based on the failure statistics before These data are given in Table 4-1 below. Table 4-1: Failure rates of substations, Vattenfall Distribution Sweden. Station Failure rates (f/100units.yr) Pole station kv 2,00 Metal-plate station kv 1,60 Concrete station kv 1,50 Cable cabinet 0,4 kv 0,60 Figure 4-20 shows the trend in the number of faults on the 6-22 kv distribution delivery and customer connection points of Danish power systems in the period of As an average in Denmark, 2,85 sustained interruptions occur per 100 stations per year. A comparison of Swedish data with Danish statistics is made in Table A1-4 in Appendix 1. Since there is a lack of the statistics of substations from other sources, the comparison of Swedish data with other statistics cannot be conducted. 33

44 Figure 4-20: Failure rates of 6-25 kv stations, Denmark. From [4]. 4.5 Circuit Breakers, Switches, and Other Devices In Sweden there are not so much statistics available for describing reliability of circuit breakers and switch devices. Some data from Swedish power utilities are therefore used in the comparative study. In Norway a comprehensive data basis on component faults is established continuously over the years from 1989 for all network voltage levels. Through the database it is possible to extract fault statistics for individual types of components and to produce the distributions of failure rates over the years. Figures 4-21 and 4-22 present the Norwegian failure statistics for circuit breakers, load-disconnectors, and switch devices over ten-year s period. Figure 4-23 shows the development of frequencies caused by failures on the circuit breakers and load disconnectors in Denmark. The number of faults on the circuit breakers is stable in the 10-year s period. Only minor variations from year to year were observed. During the latest three years, the number of permanent faults has been roughly constant with approximately 0.1 failures per 100 units per year. To maintain the quality level of power supply, Denmark has put continuously efforts on monitoring the continuity of supply. In their power system outage data collection and reporting database the distribution equipment units were defined to include even the disconnectors, switch devices, and surge arresters. This allows good statistics in ten-year s period for these devices produced. Figures 4-24 and 4-25 summarize the values for these components. 34

45 Figure 4-21: Failure rates of circuit breakers, Norway. From [6]. Figure 4-22: Failure rates of load-disconnectors and switch devices, Norway. From [6]. 35

46 Figure 4-23: Failure rates of circuit breakers and load-disconnectors, Denmark. From [4]. Figure 4-24: Failure rates of disconnectors and switch devices, Denmark. From [4]. 36

47 As indicated in Figure 4-24 that the number of faults on switch devices is quite small in normal years, giving the frequencies approximately 0,2 failures/100units.year. The number of outages caused by failures of surge arresters can be seen in Figure 4-25, which shows an average rate of 0,2-0,25 failures/100units.year. It is important to note that not all power companies are completely clear about their number of installed surge arresters. The failure rates should be treated with care. Generally, there is very limited number of events with failures on surge arresters nationwide. Table A1-5 in Appendix 1 summarizes the values from all the data sources for the comparison of failure rates for circuit breakers, switches and other devices. Although the differences in values can be observed, the variations are not so significant. Figure 4-25: Failure rates of surge arresters, Denmark. From [4]. 37

48 5 Failure Cause Analysis Outages in distribution systems caused by different factors significantly impact the reliability of the systems. It is important to investigate these outages. Specifically, analysis of reliability performance of the distribution system over the past year related to outages caused by different factors is very useful for the utilities. Significant rise from the trend in outages caused by a specific factor would help the utility to take remedial actions. Power utilities use normally a computerized system to record the outages. The cause codes are then used to classify interruptions. Tree interruptions, lighting, wind, animal, and unknown are some of the typical codes used. This coded information makes analysis of root causes possible. However it should be aware that the variability in the data source and quality of data collected based upon field experience may affect the accuracy of the analysis. In the following sub-sections the cause analyses rely on the data collected in the computerized database systems. For analyzing nationwide outages in Sweden the data from the Darwin database of Svensk Energi were used. In addition to the analysis by primary causes nationwide, analysis of failures reported at individual companies that fail on the distribution equipment was also performed. Historical data summarize the actual performance of a distribution system. The basic data item is a system failure, which is a component outage or a customer interruption. The customer interruption has been defined and recorded in terms of the primary causes of the interruption. In the outage cause analysis each failure event recorded was taken into consideration and sorted according to causes of failure and duration of outage. In the analysis the contributions of the dominant causes to the equipment failures are calculated and compared between different years. The results for different types of equipment are presented in the subsequent subsections. 5.1 Overhead Lines Overhead distribution lines constitute a very large part of distribution grids. This type of equipment is directly exposed to weather, vegetation, and animals. It typically has failure rates that are higher than corresponding underground cables. Major failures of overhead distribution lines are normally due to environment and weather they are exposed to. Figure 5-1 and Table A2-1 in Appendix 2 show the division of faults by causes on overhead lines reported in whole Sweden during The customer interruptions resulting from overhead line failures confirm by the figure that the dominant causes are weather and environment related. 38

49 Figures 5-2 and 5-3 present the results of analysing the data from Vattenfall Distribution Sweden and Fortum Distribution AB. These figures give similar dominant causes as those shown in Figure 5-1. Percentage (%) Failure causes of overhead distribution lines, Sweden Tree fall, wind Thunderstrom 2005: 81% 2004: 70% Wind Tree fall, snow Tree felling Snow, ice load Animal Material failure Lack of maintenance Fuse blow Unknown Other Figure 5-1: Failure causes for overhead lines, Sweden. Percentage (%) Failure causes of overhead distribution lines, Vattenfall : 84% 2004: 76% Tree fall, wind Wind Tree felling Tree fall, snow Thunderstrom Animal Snow, ice load Material failure Fuse blow Lack of maintenance Dig in Other Figure 5-2: Failure causes for overhead lines, Vattenfall Distribution Sweden. 39

50 Percentage (%) Failure causes of overhead distribution lines, Fortum Unknown Tree fall, wind Thunderstrom Wind Animal Tree fall, snow 2005: 75% 2004: 62% Tree felling Lack of maintenance Traffic Salt Faulted operation Other Figure 5-3: Failure causes for overhead lines, Fortum Distribution. Figure 5-4: Failure causes for distribution equipment, Norway. From [5]. 40

51 The major causes indicated in these figures reflect the customer interruptions resulting from extreme weather conditions, as the hurricane Gudrun in January 2005, as well as from winds, lightning, rain, snow, and storms. Comparing the Swedish statistics with Norwegian data shown in Figure 5-4 and Table A2-7 in Appendix 2, it is seen that environment related faults on distribution lines were dominant in both countries. As shown in figures the majority of the causes have weather and environment related characteristics. 5.2 Underground Cables Outage data collected for cable failures over a period of were analyzed both nation-wide and company-wide. Figures from 5-5 to 5-7 and Table A2-2 in Appendix 2 present the results. It is noted that underground cables have much less weather related faults compared with overhead lines. The factors that cause the cable failures, as shown in the figures, are mainly material defects, digging, and inadequate maintenance. Swedish power utilities use the same classification of interruption causes as that defined in Svensk Energi DARWin for outage data collection and reporting. The analysis of the cable faults of Vattenfall Distribution gives the similar results to that from analyzing the data Sweden-wide. However the statistics of Fortum Distribution show that the approximate 30-50% cable faults were due to lack of maintenance during Percentage (%) Material defects Digging Failure causes of underground cables, Sweden Lack of maintenance 2005: 67% 2004: 63% Unknown Fuse blow Tree fall, wind Traffic Thunderstrom Faulted operation Overloading Sabotage Other Figure 5-5: Failure causes for underground cables, all Sweden. 41

52 Percentage (%) Unknown Material defects Failure causes of underground cables, Vattenfall Digging Lack of maintenance 2005: 71% 2004: 56% Tree fall, wind Fuse blow Thunderstrom Tree felling Traffic Animal Wind Other Figure 5-6: Failure causes for underground cables, Vattenfall Distribution Sweden. Failure causes of underground cables, Fortum 60 Percentage (%) : 66% 2004: 65% Unknown Lack of maintenance Digging Material defects Dimensioning failure Animal Thunderstrom Tree fall, wind Traffic Wind Overloading Other Figure 5-7: Failure causes for underground cables, Fortum Distribution. Figures 5-8 and 5-9 present the results of interesting studies in Denmark reported in [4], where the distributions of faults on underground APB- and PEX-cables by deterioration from age and excavation were calculated. 42

53 Figure 5-8: Major failure causes for APB-cables, Denmark. From [4]. Figure 5-9: Major failure causes for PEX-cables, Denmark. From [4]. 43

54 The Danish studies indicate that main causes of cable failures are due to deterioration from age and excavation. For APB-cables, the ten-year s average value shows that the aging-related faults are at almost the same level as the faults caused by excavation, i.e. 50% and 50% division. However for PEX-cables, much more faults were due to underground work. From Figure 5-8 it is seen that the aging-related failure rates of APB-cables were about 1-1,4 failures per 100 km per year during a five-year s period. 5.3 Distribution Substations Figures from 5-10 to 5-13 summarize the causes which initiate and contribute to the failures of distribution substations both Sweden nation-wide and at an individual power company. Tables A2-3 and A2-4 in Appendix 2 list the values of the calculations. The figures 5-10 and 5-11 reveal that a large percentage of primary substation failures was initiated by weather and manufacturer and material defects. A comparison of the cause distributions shown in these two figures reveals the similar major causes both at the national level and at a company level. For secondary substations it is shown that thunderstorm and manufacturer and material defects were responsible for the majority of failures (i.e., about 53-81%) both the nation-wide and company-wide. Thunderstorm was a more significant cause of secondary substation failures than material defects. In Denmark from the early 1970 interruptions and failures in the distribution networks have been subject to statistical treatment. Distribution network companies carry out the statistical survey. In the year 2004, about 80% of the kv networks were included in the statistics. Based on the central statistical analyses the development in interruptions at distribution stations is reported in [4] with regard to interruption causes. This is shown again in Figure The figure presents an overview of causal distribution of the power outages on 6-25 kv networks. It can be seen that the particularly causes "other natural" and "external influence" were historically significant influential factors and contributed to approximately 50% of the substation failures according to ten-year s statistics. Other natural cause has influenced the substations especially by heavy snowfall and hurricanes in Dec and Jan

55 Percentage (%) Unknown Failure causes of primary substations, Sweden Material defects Thunderstrom 2005: 61% 2004: 56% Tree fall, wind Animal Wind Fuse blow Lack of maintenance Faulted operation Overloading Test Other Figure 5-10: Failure causes for primary substations, all Sweden. Percentage (%) Failure causes of primary substations, Vattenfall 2005: 83% 2004: 72% Material defects Thunderstrom Tree fall, wind Animal Wind Unknown Lack of maintenance Fuse blow Traffic Snow, ice load Dimensioning failure Other Figure 5-11: Failure causes for primary substations, Vattenfall Distribution Sweden. 45

56 Percentage (%) Unknown Thunderstrom Failure causes of 2:nd substations, Sweden Material defects 2005: 53% 2004: 56% Animal Fuse blow Wind Overloading Lack of maintenance Tree fall, wind Rain, water Faulted operation Other Figure 5-12: Failure causes for secondary substations, all Sweden. Failure causes of 2:nd substations, Vattenfall Percentage (%) : 71% 2004: 81% Material defects Thunderstrom Lack of maintenance Animal Wind Overloading Tree fall, wind Dimensioning failure Faulted method Improper montage Traffic Other Figure 5-13: Failure causes for secondary substations, Vattenfall Distribution Sweden. 46

57 Figure 5-14: Outage causes for distribution power delivery stations, Denmark. From [4]. 5.4 Distribution Transformers and Devices Table A2-5 in Appendix 2 and Figure 5-15 summarize the results of cause analysis of distribution transformer failures nation-wide. These failures were registered as kapslad transformator fel in the Darwin database. The analysis indicates that thunderstorm, manufacturer and material defects, and inadequate maintenance were responsible for the majority of distribution transformer failures (i.e., 59-66%). Figures from 5-16 to 5-19 and Table A2-6 in Appendix 2 summarize the failure-contributing causes for fuse or apparatus boxes based on the faults reported nationwide and at the distribution utilities, Vattenfall and Fortum. Thunderstorm, manufacturer and material defects, inadequate maintenance, and wind caused tree falls were reported to have contributed to a large number of fuse or apparatus box failures according to the national statistics. The analysis at company level confirms that a large percentage of fuse or apparatus box failures was initiated by either thunderstorm or wind caused tree falls. However Vattenfall s statistics indicate manufacturer and material defects being one of dominant causes, while Fortum s data show inadequate maintenance being one of major causes. 47

58 An examination of the results of Fortum s statistics shown in Figures 5-18 and 5-19 reveals that the causes for MV fuse or apparatus box failures were almost the same as for LV fuse or apparatus box failures during Percentage (%) Failure causes of distribution transformers, Sweden 2005: 66% 2004: 59% Unknown Thunderstrom Material defects Lack of maintenance Animal Fuse blow Tree fall, wind Revert load Sabotage Wind Dimensioning failure Other Figure 5-15: Failure causes for distribution transformers, all Sweden. Percentage (%) Failure causes of MV fuse or apparatus boxes, Sweden 2005: 69% 2004: 67% Unknown Thunderstrom Material defects Lack of maintenance Tree fall, wind Fuse blow Animal Wind Overloading Snow, ice load Tree fall, snow Other Figure 5-16: Failure causes for fuse or apparatus boxes, all Sweden. 48

59 Failure causes of MV fuse or apparatus boxes, Vattenfall Percentage (%) : 87% 2004: 84% Unknown Thunderstrom Material defects Tree fall, wind Fuse blow Animal Wind Snow, ice load Overloading Lack of maintenance Tree fall, snow Other Figure 5-17: Failure causes for fuse or apparatus boxes, Vattenfall Distribution Sweden. Failure causes of MV fuse or apparatus boxes, Fortum Percentage (%) : 84% 2004: 74% Unknown Lack of maintenance Thunderstrom Tree fall, wind Wind Animal Salt Material defects Faulted method Sabotage Traffic Other Figure 5-18: Failure causes for MV fuse or apparatus boxes, Fortum Distribution. 49

60 Failure causes of LV fuse or apparatus boxes, Fortum Percentage (%) Unknown Lack of maintenance 2005: 65% 2004: 47% Thunderstrom Tree fall, wind Wind Animal Overloading Digging Traffic Dimensioning failure Improper montage Other Figure 5-19: Failure causes for LV fuse or apparatus boxes, Fortum Distribution. 50

61 6 Influencing Factors The reliability of distribution equipment can be influenced by various factors. These include environmental conditions, operating conditions, maintenance, material, construction and design, etc. Table 6-1 gives an overview of the influencing factors. Due to these influencing factors, component reliability data can exhibit significant variations. It is therefore important to understand this influence and to consider the influencing factors when estimating and using the reliability statistics. This Chapter is devoted to explaining some of these factors for the variance in the statistics. In the subsequent subsections some of the influencing factors are illustrated by statistics either deduced from the fault records or obtained from available publications. Table 6-1: Overview of influencing factors. Influencing factors Voltage Manufacture, construction Time period Age, deterioration Geography, area Maintenance Operational environment Loading Inaccuracy in data reporting 6.1 Manufacture, Voltage Level, and Construction Equipment reliability data can be affected by its design, manufacture, voltage level, construction, and size. Products from different manufacturers may differ in their inherent characteristics, and these variations may make the reliability of the products different. The reliability data for the same type of products by different manufacturers, different sizes, and different voltage levels can have significant variations. These influences need to be considered in use of equipment reliability data. Vattenfall Distribution Poland analyzed how failure rates of the distribution transformers of the company vary with voltage and size (in power rating 51

62 kva). Figure 6-1 presents the results of the analyses. It was observed that the small transformers (<= 100 kva) have much higher failure rates than large ones with power rating of 400 kva. Distribution transformer failur rates regarding to power and voltages, Vattenfall Poland Failure rate (f/100units, yr) 0,030 0,025 0,020 0,015 0,010 0,005 6kV 15kV 21kV 0,000 <= Power [kva] kV 15kV Voltage 6kV Figure 6-1: Influence of voltage and size on failure rates of distribution transformers, Vattenfall Distribution Poland. 6.2 Time Period A notable character of component reliability data is its variation with time. Power network component behaviour is stochastic in nature. Component failures occur randomly. The reliability data of the components reflect this stochastic behaviour and have dynamic feature. The average failure frequency and repair time for a piece of equipment change from year to year. Figures from 6-2 to 6-5 show the variations of fault occurrences on distribution equipment over a year as well as from a year to another in Sweden and Norway. The statistics confirm the high variation for overhead lines that are more easily affected by environment and weather they are exposed to. Norwegian statistics in Figures 6-3 and 6-4 show that fault occurrences on all types of equipment vary more or less over time. The variation for overhead lines is much more larger than that for underground cables. Figures 6-2 and 6-5 reflect the nature of the failure occurrence s changing over years. In 2005 the largest portion of line-related outages occurred in 52

63 January, while in 2004 occurred in November. However in a similar fashion, in both years the variation for overhead lines is larger than that for underground cables. Distribution of equipment failures over year 2005, Sweden Equipment failures (%) Overhead lines Primary substations Underground cables Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 6-2: Fault occurrences on distribution equipment over the year 2005, all Sweden. Figure 6-3: Fault occurrences on distribution equipment over the year 2005, Norway. From [5]. 53

64 Antall feil (%) Fordeling av antall feil over året Lastskillebryter Skillebryter Siklastbryter Sikring Nettstasjon 0 Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des Figure 6-4: Fault occurrences on distribution equipment over the year 2005, Norway. From [5]. Distribution of equipment failures over year 2004, Sweden Equipment failures (%) Overhead lines Primary substations Underground cables Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 6-5: Fault occurrences on distribution equipment over the year 2004, all Sweden. 54

65 Outage duration of MV distribution equipment over year 2005, Sweden Outage duration (h/f) Ave Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Overhead lines 45,1 102,317,11 7,65 5,48 4,15 2,30 4,93 3,28 3,21 3,29 5,97 3,07 Prim. substations 6,57 31,74 2,67 1,71 0,89 2,66 2,54 3,63 2,83 2,38 3,66 2,82 3,5 Underg. cables 5,44 8,32 13,5 4,98 3,1 5,48 4,48 5,83 2,8 5,14 3,24 8,36 3,11 Figure 6-6: Outage durations of distribution equipment over the year 2005, all Sweden. Variasjon i midlere reparasjonstid over året (varige feil) [min] Midlere reparasjonstid (min) Kraftledning Fordelingstransf. Kabel Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des Figure 6-7: Outage durations for distribution equipment over the year 2005, Norway. From [5]. Figures 6-6 and 6-7 show the monthly variation effect on the outage durations of equipment failures. A considerable variation over a year in both Sweden and Norway can be observed. This variation could be due to several factors, such as operation and restoration conditions, and repair staff resource problems associated with a large number of simultaneous fault occurrences in an extreme weather condition. From these graphs, the outage durations were quite short during summer. For overhead lines the repair time was higher in winter months. 55

66 6.3 Geographical Location The geographical location of distribution equipment is another factor that could influence the reliability data of the equipment. Figures 6-8 and 6-9 show the geographical effect on the number of fault events on distribution equipment of Vattenfall Distribution Sweden during the years 2005 and It is shown by the figures that there were more fault events on overhead lines in the west region during the years The failure rate of overhead lines was high in the east region in 2005, but was much lower in 2006 compared with the year Failure rate (f/yr.100km) Failure rates of MV distribution equipment, Vattenfall Distribution Sweden, 2005, (sustained outages) Reg. Middle Reg. North Reg. West Reg. East Overhead lines Aerial cables Underground cables Figure 6-8: Failure rates of distribution equipment by regions, 2005, Vattenfall Distribution Sweden. Failure rate (f/yr.100km) Failure rates of MV distribution equipment, Vattenfall Distribution Sweden, 2006, (sustained outages) Reg. Middle Reg. North Reg. West Reg. East Overhead lines Aerial cables Underground cables Figure 6-9: Failure rates of distribution equipment by regions, 2006, Vattenfall Distribution Sweden. 56

67 Figure 6-10: Failure rates of overhead lines by areas of the country, Norway. From [5]. Figure 6-11: Failure rates of underground cables by areas of the country, Norway. From [5]. 57

68 Figure 6-12: Failure rates of distribution transformers by areas of the country, Norway. From [5]. Comparing the Swedish data with Norwegian statistics, the variations of failure events on distribution equipment by geographical location can also be observed. Figures 6-10, 6-11, and 6-12 from Norway illustrate the variations of failure frequencies for distribution equipment with areas. It is noted that the fault occurrences on cables were also influenced by geography. However, the geographical variation effect on the failure rates of underground cables was lower than that for overhead lines. 6.4 Age Age is generally considered to be an important factor that influences the failure frequency of distribution equipment. During equipment s entire lifetime, when in operating condition, the equipment underlies electrical, mechanical and thermal stresses, which may lead to a deterioration of its functionality. Furthermore, the deterioration also occurs due to the environment of the equipment operation. This deterioration occurs over time and causes the equipment ageing and decrease in reliability and functional ability. The failure rates of almost all types of power components increase with age, but the degree of increase may be low for certain types of components and high for others. Figure 6-13, from a study in Siemens AG, Germany, gives an age model of failure rate for MV oil cables. The characteristic of the age model is adjusted to the failure statistic for oil cables. This statistical model is believed giving a better representation of general reliability of MV oil cables due to its large database. A study of how failure rates of distribution transformers vary with age was performed recently at Vattenfall Distribution Poland. The result is presents in Figure The failure data were broken down into several age groups. The groups of the transformers over 40 years have much higher failure rates than the other groups. The failure rates for transformers were approximately in 58

69 same level for the age groups of less than 40 years old. A slightly higher failure rates were observed for the units aged greater than 40 years in 2007 than in 2008, but slightly lower for the unit groups aged between years in 2007 compared with Figure 6-13: Failure rate for MV oil cables Vs age. From [11]. In Norway the failure rate of PEX-cables as a function of age has been investigated in order to support decision making for cable replacement. The study indicates that cables in certain age groups have considerable higher values than the rest of the population. As shown in Figure 6-15 there is a quite noticeable difference in failure frequency. The cables used before 1979 have considerable increase in failure frequency and these cable groups become candidates for replacements. Another study of how component failure rates vary with age was carried out for distribution transformers in Norway. The results of the study shown in Figure 6-16 reveals that the breakdown frequency began increasing when the transformers became more than 20 years old. The transformer units approach the end of their life when they are greater than 40 years. 59

70 Failure rates of distribution transformers, Vattenfall Poland 8 Failure rate (f/100units.yr) > Age <10 Average Figure 6-14: Failure rates of distribution transformers Vs age, Vattenfall Distribution Poland. Figure 6-15: Failure frequency of PEX-cables as function of age, Norway. From [12]. 60

71 Figure 6-16: Breakdown frequency of distribution transformers as function of age, Norway. From [12]. 6.5 Other Factors Operation condition and maintenance Another relevant influencing factor on component reliability is the applied maintenance. Maintenance plays a vital role in extending lifetime of a repairable network component. It affects the component's reliability and cost of operation. There are two different kinds of maintenance: corrective maintenance and preventive maintenance. Preventive maintenance, unlike the corrective maintenance, means the care and servicing for the purpose of maintaining equipment and facilities in satisfactory operating condition. It provides, in an early stage, systematic inspection, detection, and correction of incipient failures before they occur or develop into major defects, and hence reduces the failure rates of components. Preventive maintenance consists of the actions including tests, measurements, adjustments, and parts replacement performed specifically to prevent faults from occurring. Its schedule is based on observation of past system behaviour, component wear-out mechanisms and knowledge of component s importance. 61

72 Operation environment Environmental factors may have a major contribution to equipment reliability. Bad environment may accelerate equipment deterioration and shorten its life span and hence cause higher failure rate of the equipment. In the case of transformers, for cooling purposes, they are usually installed outside the substation. This makes them subject to heat, cold, rain and wind. Heat and humidity are especially hazardous as they can cause the degradation of transformer s insulation layer and slowly turn transformer s oil tank into rust and consequently making leakages. All external parts of the transformer are affected. The tap changer will corrode leading to corrosion of the contacts that in turn causes jamming. Variations of environmental temperature may cause moisture level of a transformer to go up. With frequent fluctuations, the transformer paper s ability to reabsorb the moisture decreases. Over time, the permanent moisture level will increase that can lead to transformer breakdown and result in higher failure occurrences. Inaccuracy in data reporting Inaccuracy in fault reporting may be another influencing factor. Most of power utilities use computerized systems to register equipment failures. The input codes, such as cause codes, used to record details of the interruptions by utilities may be a major source of uncertainty. The variability and inaccuracy in the data source as well as quality of the data may in practical affect the qualitative analysis of component failures and thus influence reliability statistics based upon field experience. 62

73 7 Impact of Extreme Weather Distribution networks are exposed to varying weather conditions. The failure statistics show that the failure rates of most power network components vary with weather conditions. In bad weather conditions, the failure rate of a network component can be many times greater than that found in the favourable weather condition. During extreme adverse weather period the failure rate can be very large, much greater than the average value, and would significantly increase the probability of simultaneous failures. In 2005 the extreme weather, as hurricane Gudrun in January, hit Sweden and had a very negative local impact and caused a lot of faults on power networks. The Gudrun storm was assumed to be the worst natural catastrophe and caused many very long outages up to more than one month for the worst affected customers. Although such a situation is rare, but has high negative impact on power systems. For this reason, its effect should be considered in the risk analysis, and corresponding reliability data need to be studied. Figure 7-1: Impact of Gudrun storm in Jan In order to analyze the outages caused by extreme-weather, the distribution outage data collected in 2005 Sweden-wide and at the company E.ON Elnät Sverige AB were studied. Tables 7-1 and 7-2 and Figures 7-2 and 7-3 present monthly outage occurrences on MV distribution networks of Sweden and at the company. It is clear from the figures that a large portion of outages, approximately 30% of total outages in 2005, occurred during January. 63

74 Table 7-1: Sustained outage occurrences on MV networks, 2005, all Sweden. Month Number of outages Jan Feb March 1201 April 1018 May 1355 June 1769 July 2839 Aug Sep Oct Nov Dec Total Table 7-2: Sustained outage occurrences on MV networks, 2005, E.ON Elnät Sverige AB. Month Number of outages Jan 3634 Feb March 606 April 467 May 616 June 817 July 1000 Aug. 406 Sep. 363 Oct. 350 Nov. 609 Dec. 290 Total

75 Number of outages > 3min on kv networks, 2005, Sweden Jan Feb. March April May June July Aug. Sep. Oct. Nov. Dec. Figure 7-2: Number of outages (>3 minutes) on MV networks, 2005, Sweden. Number of failures Number of sustained outages > 3 min on kv networks, 2005, E.ON Elnät Sverige Jan Feb. March April May June July Aug. Sep. Oct. Nov. Dec. Figure 7-3: Number of outages (>3 minutes) on MV networks, 2005, E.ON Elnät Sverige AB. 65

76 The outage records in Jan were further examined and counted by each day. Figures 7-4 and 7-5, Table A3-3 in Appendix 3, and Table A4-2 in Appendix 4 present the results. The analysis reveals that 48% of all the outages in January in Sweden occurred on 8-9 January. The similar situation can be observed with the fault data from E:ON Elnät Sverige AB of total 3634 outages in January occurred on 8-9 January. Number of outages on kv networks, Jan. 2005, Sweden Number of failures Date Figure 7-4 : Number of outages on MV networks, in January 2005, Sweden. Number of failures Number of sustained outages > 3 min on kv networks, Jan. 2005, E.ON Elnät Sverige Date Figure 7-5: Number of outages on MV networks, in January 2005, E:ON Elnät Sverige AB. 66

77 The failure intensity of the hurricane Gudrun event affecting distribution equipment on Jan was analyzed both nationwide and for the company E.ON Elnät Sverige AB. Tables A3-1 and A3-2 in Appendix 3 and Table A4-1 in Appendix 4 summarize the values. Figures from 7-6 to 7-9 show the hourly variation of failure intensity for overhead lines. The hourly extreme failure intensity for bare overhead lines could go up to as high as 0,5-2 failures per 100 km as shown in Figures 7-6 and 7-7. Figures 7-8 and 7-9 expresses the same kind of variation for aerial insulated lines, which show hourly extreme value of about 0,3-0,6 failures per 100 km. Failure rate of kv bare overhead lines, Sweden, on Jan 8, ,6 0,5 0,4 0,3 0,2 0,1 0,0 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Failure rate (f/hour.100km) Time Figure 7-6: Failure rate of MV bare overhead lines on Jan. 8, 2005, Sweden. Failure rate (f/hour.100km) 2,50 2,00 1,50 1,00 0,50 0,00 00:00 02:00 Failure rate of kv bare overhead line, on Jan 8, 2005, E.ON Elnät 04:00 06:00 08:00 10:00 12:00 Time 14:00 16:00 18:00 20:00 22:00 Figure 7-7: Failure rate of MV bare overhead lines on Jan. 8, 2005, E.ON Elnät Sverige AB. 67

78 Failure rate (f/hour.100 km) Failure rate of kv insulated lines in air, Sweden, on Jan 8, 2005, 0,35 0,30 0,25 0,20 0,15 0,10 0,05 0,00 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 Time 16:00 18:00 20:00 22:00 Figure 7-8: Failure rate of MV insulated lines on Jan. 8, 2005, Sweden. Failure rate of kv insulated lines in air, on Jan 8, 2005, E:ON Elnät 0,8 Failure rate (f/hour.100 km) 0,6 0,4 0,2 0,0 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time Figure 7-9: Failure rate of MV insulated lines on Jan. 8, 2005, E.ON Elnät Sverige AB. 68

79 The failure rates caused by adverse weather are compared with the average values deduced in the report [1], and the results are summarized in Figure It is important to note that the failure frequency in extreme weather condition is very significantly different with the average value. The hurricane Gudrun could drive the failure intensities up to approximately times high as the average values for overhead lines. This emphasizes the need to consider this effect when doing risk analysis. Average value, (outage > 3 min) All Sweden Overhead lines, kv: Bare conductor lines: f/km.yr f/km.day Aerial inslulated lines: f/km.yr f/km.day Underground cables, kv: f/km.yr f/km.day E.ON Bare conductor lines: f/km.yr f/km.day Aerial inslulated lines: f/km.yr f/km.day Underground cables: f/km.yr f/km.day Jan. 8, 2005, (outage > 3 min) All Sweden Overhead lines, kv: Bare conductor lines: f/km.day = 73 * f/km.day Aerial inslulated lines: f/km.day = 50 * f/km.day Underground cables, kv : f/km.day = 2 * f/km.day E.ON Bare conductor lines: f/km.day = 64 * f/km.day Aerial inslulated lines : f/km.day = 83 * f/km.day Underground cables: 0.0 f/km.day = 0 * f/km.day Figure 7-10: Comparison of the failure rates in extreme weather condition with the average values. 69

80 8 Conclusions This project performed distribution reliability data related studies and analyses. The conclusions from the project work are summarized as follows: Project performance Component reliability data has fundamental importance for distribution system risk and reliability analysis. Without good such data the risk analysis would be baseless. In order to better understand component reliability data and get confidence in data use, the project performed detailed study and analysis of existing component failure statistics. The project activities were focused on: Comparative study. The component failure statistics from different sources and different countries, such as Norway, Denmark, Finland, and Germany, are studied and compared with respect to data structure, component classification, and values of the statistics. Major cause analyses for component failures. Influential factor analyses. The different factors and failure sources that may affect the statistics were studied and analysed. Failure statistics analyses for extreme events. The component failure statistics of hurricane Gudrun based on outage reports from were deduced in the project. Comparative study The project studied the data characteristics and categorization structures in different data sources. The overview of similarities and consistence in data structure and categorizations that exist among different data sources are given in the report. The comparison of data scheme characteristics and structures are summarized in the report with respect to system characteristics, equipment categories, and interruption causes. The values of failure statistics from different data sources were collected and compared. The comparative data studies are made on major categories of distribution equipment including: Distribution lines; Distribution cables; Transformers; Sub-stations; Circuit breakers, switch devices, and other devices. 70

81 The comparative overviews of the Swedish statistics with the data from other sources are presented in the report and the detailed values for different types of components are summarized in the Appendix of the report. The project shows that much effort in many countries has been expended in developing methods and computer programs to uniformly and consistently quantify the reliability of distribution equipment based on electric system performance. However, the results of the worldwide study in the project indicate that significant inconsistencies exist in the data, in the data categorization, and in the data collection processes in different countries. A set of differences found in data categorization and structure among different data sources is also presented in the report. Component reliability data reflects stochastic nature of component failures and impact of influencing factors. The results of project show that the reliability data can vary widely from system to system, from year to year, and from area to area. It is important to recognize this dynamic feature of reliability data. If possible, study and analyze component failure statistics regularly and produce confident data for risk analysis. Failure cause analyses To understand the causes of component failures the cause analyses were carried out in the project. The analyses were based on the data from the DARWin database of Svensk Energi. In addition to the nationwide analyses the study of faults reported at individual companies was also performed in the project. In the analysis the contributions of the dominant causes to the failures of major categories of distribution equipment were calculated and compared between different years. The distribution of component failures over different causes is presented in the report. The analyses show that the failure causes were different for different types of power components. The analyses confirm that the dominant causes for faults on overhead network components are weather and environment related, while faults on underground distribution equipment are mainly material defect and digging related. The results of analysis indicate that thunderstorm, manufacturer and material defects, and inadequate maintenance were responsible for the majority of distribution transformer failures (approximately 59-66%). Influential factor analyses The reliability of distribution equipment can be influenced by various factors. These include environmental conditions, operating conditions, maintenance, material, construction and design, etc. In order to understand this influence the project work is devoted to studying and analyzing these factors. The report describes and illustrates how equipment reliability data can be affected by manufacture, voltage level, construction, size, time period, geography, age, operation condition and maintenance. 71

82 The results of the study show that due to different influencing factors, component reliability data can exhibit significant variations. It is therefore important to understand this influence and to consider the influencing factors when estimating and using the reliability statistics. Failure statistics analyses for extreme events Distribution networks are exposed to varying weather conditions. The failure rates of most power network components vary with weather conditions. In the extreme weather condition, distribution systems could be affected very negative. For this reason, this effect should be considered in the risk analysis, and corresponding reliability data need to be studied. In order to understand how extreme weather affects component reliability data, the project analyzed the statistics of component failures during the hurricane Gudrun in January 2005 based on the fault reports from all Sweden and from the power company, E:ON Elnät Sverige AB. The results indicate that the failure rate of equipment exposed to weather could dramatically increase by up to 80 times of the average value during extreme weather condition. This dramatic change of the component reliability data should therefore be taken into account in risk analysis of distribution systems. 72

83 9 References [1] Y. He, Distribution Equipment Reliability Data, Elforsk rapport 07:59, Sep [2] V. G. Werner, et al., Collecting and Categorizing Information Related to Electric Power Distribution Interruption Events: Data Consistency and Categorization for Benchmarking Surveys, IEEE Transactions on Power Delivery, vol. 21, no. 1, January [3] IEEE Power Engineering Society, 1366 IEEE Guide for Electric Power Distribution Reliability Indices, IEEE Std , 14 May [4] R. Olsen og P. Hansen, Dansk Energi, Rapport 547, Fejl- og afbrudsstatistik, Dansk landsstatistik for årene for statistikområdet 6-25 kv, September [5] J. Heggset, et al., FASIT 2005 Feil og avbrudd i høyspennings fordelingsnett tom 22 kv, Publikasjon nr.: SINTEF Energiforskning AS. [6] G. Kjølle, et al., Analyser av feil og avbrudd i kraftnettet , SINTEF Energiforskning AS. [7] Statnett, Årsstatistikk 2007 Feil og avbrudd i 1-22 kv nettet. [8] G. Kjølle, Fault statistics in distribution network, SINTEF Energy Research. [9] J. Heggset, et al., FASIT 2006 Feil og avbrudd i høyspennings fordelingsnett tom 22 kv, SINTEF Energiforskning AS. [10] F. Roos and S. Lindahl, Distribution System Component Failure Rates and Repair Times An Overview, Lund University, Sweden. [11] M. Schwan, et al., Reliability Centered Asset Management in Distribution Networks - Process and Application Examples, CIRED [12] J. Heggset, Bedre utnyttelse av feil-ogavbruddsdata, SINTEF Energiforskning AS. [13] G. Kjølle, et al., Incorporating Short Interruptions and Time Dependency of Interruption Costs in Continuity of Supply Regulation, CIRED paper 0494, Prague, June [14] J. Heggset, G. H Kjølle, and Olve Mogstad Fasit the Norwegian Standard for Collection, Calculation and Reporting of Reliability Data. [15] Danish Energy Association, Danish Electricity Supply 2008, Statistical Survey, ISSN , April [16] Association of Danish Energy Companies, Danish electricity Supply Statistical Survey 2004, ISSN [17] M. M. Jensen, DEFU, Rapport 520, Indsamling af leveringssikkerhedsdata, December

84 [18] Dansk Energi, Dansk Elforsyning Statistik 2004, ISSN , Juni [19] Dansk Energi, Rapport 525, Indsamling af leveringssikkerhedsdata, Juni [20] J. Heggset and G. H Kjølle, Experiences with the FASIT Reliability Data Collection System, SINTEF Energy Research, Norway. [21] J. Heggset, et al., Fasit A Tool for Collection, Calculation and Reporting of Reliability Data, CIRED paper 0716, Prague, June [22] Norstat working group, Faults and Interruptions in the Nordic 1-70 kv Network, Ststistics 2005, Report 530, December [23] C. A. Warren, et al., A Nationwide Survey of Recorded Information Used for Calculating Distribution Reliability Indices, IEEE Transactions on Power Delivery, vol. 18, no. 2, April [24] Publications at the following Web-sites: SAKS/Feilstatistikk/

85 10 Appendix 1: Summary of Data Comparison 75

86 Table A1-1: Comparison of Failure Rates for Overhead Lines Failure rates of overhead lines (f/100km.yr) (sustained outages) For data from Equipment Average 9,63 67,1 15,72 40,57 33,25 ( ) All Sweden All Norway All Denmark Friledning oisolerad 0,4-1 kv Luftledning isolerad 0,4-1 kv Luftledning 0,4-1 kv Friledning oisolerad kv Luftledning isolerad kv Luftledning kv Kraftledning blank 1-22 kv Kraftledning belagt (BLX), 1-22 kv Kraftledning 1-22 kv Luftledningsanlæg 6-25 kv 3,56 5,68 2,61 4,24 5,43 15,81 4,78 9,99 8,06 16,53 8,51 12,88 7,57 9,55 7,65 12,35 8,00 15,45 8,38 12,79 7,00 8,40 5,50 2,30 2,20 1,90 3,63 4,13 2,85 4,00 2,15 5,76 4,54 4,41 4,02 ( ) 9,00 ( ) 11,49 ( ) 9,28 ( ) 11,15 ( ) 5,90 ( ) 1,90 ( ) 3,72 ( ) 3,79 ( ) Based on average statistics to 2005 All Finland Overhead lines, uninsulated, 1-39 kv 4,77 Overhead lines, insulated, 1-39 kv 0,21 Based on average statistics in Germany and Switzerland before Overhead line, compensation (earthing), 10 kv 2,56 BCP Overhead line, compensation (earthing), 20 kv 12,42 Overhead line, low impedance earthing, 10 kv 11,48 Overhead line, low impedance earthing, 20 kv 19,99 Based on average statistics before 1998 Overhead 11 kv feeders 13,00 USA Overhead lines 5-35 kv, primary trunk: low value = 1,24 typical value = 6,22, high value = 18,65 Overhead lines 5-35 kv, lateral tap: low value = 1,24 typical value = 9,94, high value = 18,65 Based on equipment surveys from IEEE Open wire < 15 kv 6,20 Open wire > 15 kv 2,46 76

87 Table A1-2: Comparison of Failure Rates for Underground Cables. Failure rates of underground cables (f/100km.yr) (sustained outages) For data from Equipment Average Jordkabel 3,01 3,11 2,82 3,22 2,89 All Sweden 0,4-1 kv ( ) Jordkabel 1,97 1,96 1,78 2,22 1, kv ( ) All Norway kabel (alle typer) 2,44 2,34 2,19 1,9 1, kv ( ) PEX-kabel 2,1 2, kv ( ) All Denmark APB-kabler 2,38 2,3 2,52 2,68 2, kv ( ) PEX-kabler 0,63 0,48 0,64 0,66 0, kv ( ) All Finland Based on average statistics to 2005 Underground cables (all types), 1-39 kv 0,85 Based on average statistics in Germany and Switzerland before XLPE cables, compensation (earthing), 10 kv 0,76 XLPE cables, compensation (earthing), 20 kv 0,65 BCP XLPE cables, low impedance earthing, 10 kv 4,90 XLPE cables, low impedance earthing, 20 kv 0,90 PE cables, compensation (earthing), 10 kv 2,69 PE cables, compensation (earthing), 20 kv 3,50 PE cables, low impedance earthing, MV 3,83 Based on average statistics before 1998 USA Underground distribution cable, primary cable: low value = 1,86, typical value = 4,35 Underground distribution cable, secondary cable: low value = 0,31 typical value = 6,22, high value = 9,32 Based on equipment surveys from IEEE Underground cables, < 0,6 kv 1,28 Underground cables, 0,6-15 kv 1,97 Underground cables, > 15 kv 0,98 77

88 Table A1-3: Comparison of Failure Rates for Transformers. Failure rates of transformers (f/100units.yr) (sustained outages) For data from Equipment Average Utility average, Sweden Distributions transformator 0,4/10-24 kv 0,96 All Norway All Denmark Poland Krafttransformator 10-20/ KV Fordelingstransformator 0,23-0,4/1-22 kv 0,76 0,54 0,85 0,61 Distributionstransformere 0,12 0,09 0,13 0, kv Based on average statistics from Vattenfall Distribution Poland ( ) 0,30 ( ) 0,79 ( ) 0,12 ( ) MV transformers 0,7 0,6 0,60 0,63 ( ) Based on average statistics in Germany and Switzerland before Transformer, compensation (earthing), 10 kv 0,12 Transformer, compensation (earthing), 20 kv 0,35 BCP Transformer, low impedance (earthing), 10 kv 0,31 Transformer, low impedance (earthing), 20 kv 0,18 Transformer, underground system, 10/0,4 kv 0,48 Transformer, underground system, 20/0,4 kv 6,76 LV/MV transformers 0,1-0,2 Study in [10] MV/MV transformers 1,0-1,3 MV/HV transformers 1,4-2,5 Based on average statistics before 1998 Pole mounted transformers 5-35 kv: low value = 0,4 USA typical value = 1,00, high value = 1,50 Pole mounted distribution transformers: low value = 0,10, typical value = 1,00 Based on equipment surveys from IEEE Power transformators, liquid filled, all 0,62 Power transformators, liquid filled, 0,3-10 MVA 0,59 Power transformators, liquid filled, > 10 MVA 1,53 78

89 Table A1-4: Comparison of Failure Rates of Substation. Failure rates of distribution substation (f/100units.yr) (sustained outages) For data from Equipment Average Based on statistics from Vattenfall Distribution AB before 2006 Stolpstation kv 2,00 Sweden Plåtstation kv 1,60 Betongstation kv 1,50 Kabelskåp 0,4 kv 0,60 All Denmark Leveringspunkter 6-25 kv 2,26 2,79 2,71 2,6 2,85 ( ) 79

90 Table A1-5: Comparison of Failure Rates for Circuit Breakers, Switches and Other Devices. Failure rates of circuit breakers, switches, and other devices (f/100units.yr)(sustained outages) For data from Equipment Average Based on statistics from Swedish utilities before 2006 Sweden Effektbrytare inomhus 1,10 All Norway All Denmark Frånskiljare kv utomhus 1,30 Frånskiljare kv inomhus 0,50 Effektbryter 0,25 0,19 0,21 0,18 0, kv ( ) Lastskillebryter, Skillebryter, 0,15 0,17 0,16 0,18 0,17 Siklastbryter, ( ) 1-22 kv Overspændingsafledere 0,22 0,19 0,22 0,13 0, kv ( ) Afbrydere 6-25 kv 0,1 0,09 0,09 0,09 0,10 Adskillere 6-25 kv 0,09 0,06 0,13 0,13 ( ) 0,13 ( ) Based on average statistics in Germany and Switzerland before BCP Circuit breakers 10 kv 0,04 Circuit breakers 20 kv 0,18 Based on average statistics before 1998 Switch 11 kv 1,00 Disconnect switch 5-35 kv: low value = 0,40 USA typical value = 1,40, high value = 14,00 Padmount switch: low value = 0,10 typical value = 0,30, high value = 0,50 Reclosers 11 kv 1,00 Fuse cutout 5-35 kv: low value = 0,40 typical value = 0,90, high value = 3,00 Based on equipment surveys from Circuit breakers, fixed type, all 0,50 Circuit breakers, fixed type, < 0.6 kv 0,40 IEEE Circuit breakers, fixed type, > 0.6 kv 1,76 Circuit breakers, metalclad drawout type 0,30 Disconnect switches, enclosed 0,61 Disconnect switches, open 0,29 80

91 11 Appendix 2: Failure Causes for Distribution Equipment Table A2-1: Failure Causes for Overhead Lines, All Sweden Cause Number Percentage (%) Number Percentage (%) Bristande underhåll 237 1, ,12 Dimensioneringsfel 5 0,03 8 0,10 Djur 251 1, ,01 Fabr- eller mtrlfel 903 5, ,82 Felaktig metod/instruktion 26 0, ,34 Felaktig mont/förläggning 21 0, ,39 Felmanöver 27 0, ,16 Grävning 34 0, ,34 Okänd , ,32 Provning 8 0,05 8 0,10 Regn, vatten 52 0, ,82 Sabotage 9 0,06 7 0,09 Salt 19 0, ,12 Snö, islast 257 1, ,61 Säkringsbrott 165 1, ,18 Trafik 116 0, ,01 Trädfall, snö 568 3, ,76 Trädfall, vind , ,45 Trädfällning 310 1, ,14 Vind 745 4, ,72 Åska 971 6, ,97 Återvändande last 2 0,01 3 0,04 Överbelastning 36 0, ,39 Total

92 Table A2-2: Failure Causes for MV Underground Cables, All Sweden. Cause Number Percentage (%) Number Percentage (%) Bristande underhåll 83 7, ,80 Dimensioneringsfel 7 0, ,38 Djur 6 0,54 4 0,37 Fabr- eller mtrlfel , ,74 Felaktig metod/instruktion 7 0,63 3 0,28 Felaktig mont/förläggning 7 0,63 8 0,74 Felmanöver 13 1,16 6 0,55 Grävning , ,51 Okänd , ,19 Provning 1 0,09 6 0,55 Regn, vatten 2 0,18 2 0,18 Sabotage 10 0,90 4 0,37 Salt 0 0,00 0 0,00 Snö, islast 2 0,18 9 0,83 Säkringsbrott 46 4, ,72 Trafik 28 2,51 5 0,46 Trädfall, snö 0 0,00 6 0,55 Trädfall, vind 31 2,78 9 0,83 Trädfällning 8 0,72 6 0,55 Vind 10 0,90 5 0,46 Åska 26 2, ,57 Återvändande last 0 0,00 0 0,00 Överbelastning 11 0, ,38 Total

93 Table A2-3: Failure Causes for Primary Substation, All Sweden. Cause Number Percentage (%) Number Percentage (%) Bristande underhåll 14 1,86 9 1,43 Dimensioneringsfel 6 0,80 4 0,64 Djur 61 8, ,63 Fabr- eller mtrlfel , ,24 Felaktig metod/instruktion 3 0,40 6 0,95 Felaktig mont/förläggning 5 0,66 3 0,48 Felmanöver 14 1,86 7 1,11 Grävning 5 0,66 4 0,64 Okänd , ,30 Provning 12 1, ,54 Regn, vatten 2 0,27 4 0,64 Sabotage 0 0,00 1 0,16 Salt 0 0,00 0 0,00 Snö, islast 7 0,93 6 0,95 Säkringsbrott 28 3, ,43 Trafik 4 0,53 3 0,48 Trädfall, snö 1 0,13 7 1,11 Trädfall, vind , ,41 Trädfällning 7 0,93 7 1,11 Vind 34 4, ,86 Åska , ,83 Återvändande last 1 0,13 0 0,00 Överbelastning 13 1, ,07 Total

94 Table A2-4: Failure Causes for Secondary Substation, All Sweden. Cause Number Percentage (%) Number Percentage (%) Bristande underhåll 70 3, ,57 Dimensioneringsfel 2 0,10 5 0,23 Djur 125 6, ,22 Fabr- eller mtrlfel , ,22 Felaktig metod/instruktion 5 0,26 5 0,23 Felaktig mont/förläggning 5 0,26 9 0,41 Felmanöver 10 0, ,60 Grävning 3 0,15 3 0,14 Okänd , ,67 Provning 1 0,05 1 0,05 Regn, vatten 11 0, ,87 Sabotage 1 0,05 7 0,32 Salt 1 0,05 0 0,00 Snö, islast 7 0, ,64 Säkringsbrott 96 4, ,28 Trafik 8 0,41 6 0,27 Trädfall, snö 3 0, ,64 Trädfall, vind 66 3, ,66 Trädfällning 5 0,26 4 0,18 Vind 89 4, ,02 Åska , ,95 Återvändande last 1 0,05 2 0,09 Överbelastning 73 3, ,75 Total

95 Table A2-5: Failure Causes for Distribution Transformers, All Sweden. Cause Number Percentage (%) Number Percentage (%) Bristande underhåll 17 8, ,67 Dimensioneringsfel 1 0,49 2 1,33 Djur 16 7,80 6 4,00 Fabr- eller mtrlfel 37 18, ,00 Felaktig metod/instruktion 1 0,49 1 0,67 Felaktig mont/förläggning 0 0,00 1 0,67 Felmanöver 0 0,00 4 2,67 Grävning 0 0,00 0 0,00 Okänd 21 10, ,33 Provning 1 0,49 0 0,00 Regn, vatten 0 0,00 0 0,00 Sabotage 2 0,98 0 0,00 Salt 0 0,00 0 0,00 Snö, islast 1 0,49 0 0,00 Säkringsbrott 12 5, ,00 Trafik 1 0,49 0 0,00 Trädfall, snö 0 0,00 0 0,00 Trädfall, vind 9 4,39 2 1,33 Trädfällning 0 0,00 0 0,00 Vind 2 0,98 0 0,00 Åska 81 39, ,00 Återvändande last 3 1,46 1 0,67 Överbelastning 0 0,00 7 4,67 Total

96 Table A2-6: Failure Causes for Fuse or Apparatus Boxes, All Sweden. Cause Number Percentage (%) Number Percentage (%) Bristande underhåll , ,10 Dimensioneringsfel 0 0,00 4 0,32 Djur 51 3, ,21 Fabr- eller mtrlfel , ,27 Felaktig metod/instruktion 2 0,15 0 0,00 Felaktig mont/förläggning 3 0,23 9 0,72 Felmanöver 4 0,31 2 0,16 Grävning 2 0,15 5 0,40 Okänd , ,00 Provning 0 0,00 0 0,00 Regn, vatten 6 0,46 5 0,40 Sabotage 4 0,31 0 0,00 Salt 4 0,31 0 0,00 Snö, islast 12 0, ,92 Säkringsbrott 51 3, ,07 Trafik 2 0,15 3 0,24 Trädfall, snö 7 0, ,96 Trädfall, vind 108 8, ,29 Trädfällning 3 0,23 9 0,72 Vind 40 3, ,21 Åska , ,63 Återvändande last 0 0,00 1 0,08 Överbelastning 29 2, ,29 Total

97 12 Appendix 3: Summary of Swedish Distribution Equipment Reliability Data on Hurricane Gudrun Day Table A3-1: Failure Rate of kv Network Components on January 8, 2005, All Sweden. Sustained outage > 3 minutes, on 8 Jan. 2005, Sweden Equipment, station Number of Number of Number of Failure rate units or km, 2005 failures, 2005 failures, on (f/day.unit), 8 Jan (f/day.km), on 8 Jan ANNAN LEDNING ANNAN STATIONSTYP 48 0 AVGRENINGS- / KABELSKÅP 26 0 BETONGSTN INOMHUSMAN 66 0 BETONGSTN. UTOMHUSMAN 35 0 FRILEDNING, ISOLERAD FRILEDNING, OISOLERAD ,0248 FÖRDELNINGSSTATION HÄNGKABELLEDNING 66 3 HÄNGSPIRALKABELLEDNING 22 1 INHYST STATION 7 0 KABEL I MARK ,0001 KABEL I VATTEN 9 0 KAPSLAD TRANSFORMATOR KOPPLINGSSTATION 25 0 LUFTLEDNING ISOLERADE ,0120 OKÄND PLÅTSTATION SATELLITSTATION 22 0 STOLPSTATION SÄKRINGS- ELLER APPARATLÅDA UTGÅTT ANNAN LEDNING MARK 16 0 UTGÅTT AVGRENINGSSKÅP HSP 2 0 UTGÅTT MARKSTATION 38 0 Total

98 Table A3-2: Failure Rate of kv Network Equipment on Jan. 9, 2005, All Sweden. Sustained outage > 3 minutes, on 9 Jan. 2005, Sweden Equipment, station Number of Number of Number of Failure rate units or km, 2005 failures, 2005 failures, on (f/day.unit), 9 Jan (f/day.km), on 9 Jan ANNAN LEDNING ANNAN STATIONSTYP 48 0 AVGRENINGS- / KABELSKÅP 26 0 BETONGSTN INOMHUSMAN 66 0 BETONGSTN. UTOMHUSMAN 35 1 FRILEDNING, ISOLERAD FRILEDNING, OISOLERAD ,0084 FÖRDELNINGSSTATION HÄNGKABELLEDNING 66 4 HÄNGSPIRALKABELLEDNING 22 2 INHYST STATION 7 0 KABEL I MARK ,0002 KABEL I VATTEN 9 0 KAPSLAD TRANSFORMATOR KOPPLINGSSTATION 25 0 LUFTLEDNING ISOLERADE ,0056 OKÄND PLÅTSTATION SATELLITSTATION 22 0 STOLPSTATION SÄKRINGS- ELLER APPARATLÅDA UTGÅTT ANNAN LEDNING MARK 16 0 UTGÅTT AVGRENINGSSKÅP HSP 2 0 UTGÅTT MARKSTATION 38 0 Total

99 Table A3-3: Number of Outages > 3 Minutes During Jan on kv Networks, All Sweden. Date Number of Outages Total

100 Table A3-4: Variation of Failure Rate of kv Bare Overhead Lines on Jan. 8, 2005, All Sweden. Sustained outage > 3 minutes on 8 Jan. 2005, Sweden Time Number of failures Failure rate (f/hour.100km) 00:00-01:00 3 0, :00-02:00 1 0, :00-03:00 4 0, :00-04:00 0 0, :00-05:00 2 0, :00-06:00 2 0, :00-07:00 4 0, :00-08:00 2 0, :00-09:00 9 0, :00-10:00 7 0, :00-11:00 4 0, :00-12: , :00-13: , : , :00-15: , :00-16: , :00-17: , :00-18: , :00-19: , :00-20: , :00-21: , :00-22: , :00-23: , :00-24: ,1630 Total

101 Table A3-5: Variation of Failure Rate of kv Insulated Lines in Air on Jan. 8, 2005, All Sweden. Sustained outage > 3 minutes on 8 Jan. 2005, Sweden Time Number of failures Failure rate (f/hour.100km) 00:00-01:00 0 0, :00-02:00 0 0, :00-03:00 0 0, :00-04:00 0 0, :00-05:00 0 0, :00-06:00 0 0, :00-07:00 0 0, :00-08:00 0 0, :00-09:00 2 0, :00-10:00 0 0, :00-11:00 0 0, :00-12:00 1 0, :00-13:00 3 0, : , :00-15:00 6 0, :00-16: , :00-17: , :00-18: , :00-19:00 6 0, :00-20: , :00-21: , :00-22:00 9 0, :00-23: , :00-24: ,1136 Total

102 13 Appendix 4: Summary of Statistics on Hurricane Gudrun Day of E.ON Elnät Sverige AB Table A4-1: Failure Statistics of kv Network Equipment on Jan. 8, 2005, E.ON Elnät Sverige AB. Sustained outage > 3 minutes, on Jan. 8, 2005, E.ON Elnät Equipment, station Number of Number of Number of Failure rate units or km, 2005 failures, 2005 failures, on (f/day.unit), 8 Jan (f/day.km), on 8 Jan ANNAN LEDNING 62 1 ANNAN STATIONSTYP 41 0 AVGRENINGS- / KABELSKÅP 10 0 BETONGSTN INOMHUSMAN 16 0 BETONGSTN. UTOMHUSMAN 17 0 FRILEDNING ISOLERAD (inkl. hängkabelledning, 0,0165 hängspiralkabelledning) FRILEDNING, OISOLERAD ,0830 FÖRDELNINGSSTATION HÄNGKABELLEDNING 22 0 HÄNGSPIRALKABELLEDNING 4 0 INHYST STATION 2 0 KABEL I MARK KABEL I VATTEN 3 0 KAPSLAD TRANSFORMATOR 37 0 KOPPLINGSSTATION 9 0 OKÄND 60 0 PLÅTSTATION 41 0 SATELLITSTATION 1 0 STOLPSTATION SÄKRINGS- ELLER APPARATLÅDA UTGÅTT ANNAN LEDNING MARK 14 0 UTGÅTT AVGRENINGSSKÅP HSP 16 0 UTGÅTT MARKSTATION 0 Total

103 Table A4-2: Number of Outages > 3 minutes During January 2005 on kv Networks, E.ON Elnät Sverige AB. Date Number of Outages Total

104 Table A4-3: Variation of Failure Rate of kv Bare Overhead Lines on Jan. 8, 2005, E.ON Elnät Sverige AB. Sustained outage > 3 minutes on 8 Jan. 2005, E.ON Elnät Time Number of failures Failure rate (f/hour.100km) 00:00-01:00 1 0, :00-02:00 1 0, :00-03:00 4 0, :00-04:00 0 0, :00-05:00 2 0, :00-06:00 2 0, :00-07:00 4 0, :00-08:00 2 0, :00-09:00 7 0, :00-10:00 3 0, :00-11:00 4 0, :00-12:00 9 0, :00-13: , : , :00-15: , :00-16: , :00-17: , :00-18: , :00-19: , :00-20: , :00-21: , :00-22: , :00-23: , :00-24: ,3915 Total

105 Table A4-4: Variation of Failure Rate of kv Aerial Insulated Lines on Jan. 8, 2005, E.ON Elnät Sverige AB. Sustained outage > 3 minutes on 8 Jan. 2005, E.ON Elnät Time Number of failures Failure rate (f/hour.100km) 00:00-01:00 0 0, :00-02:00 0 0, :00-03:00 0 0, :00-04:00 0 0, :00-05:00 0 0, :00-06:00 0 0, :00-07:00 0 0, :00-08:00 0 0, :00-09:00 2 0, :00-10:00 0 0, :00-11:00 0 0, :00-12:00 1 0, :00-13:00 2 0, : , :00-15:00 6 0, :00-16:00 9 0, :00-17: , :00-18: , :00-19:00 3 0, :00-20:00 3 0, :00-21:00 6 0, :00-22:00 3 0, :00-23:00 1 0, :00-24:00 0,0000 Total 95 95

106 2