How To Improve Power Supply

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1 PSERC Integraton of Asset and Outage Management Tasks for Dstrbuton Applcaton Fnal Proect Report Power Systems Engneerng Research Center Empowerng Mnds to Engneer the Future Electrc Energy System Snce 1996

2 Integraton of Asset and Outage Management Tasks for Dstrbuton Applcaton Fnal Proect Report Proect Team Mladen Kezunovc, Proect Leader Yma Dong Texas A&M Unversty Ward Jewell Vsvakumar Aravnthan Prasad Dongale Wchta State Unversty PSERC Document October 2009

3 Informaton about ths proect For nformaton about ths proect contact: Mladen Kezunovc, Ph.D., P.E. Texas A&M Unversty Department of Electrcal and Computer Engneerng College Staton, TX Tel: Fax: Emal: Power Systems Engneerng Research Center The Power Systems Engneerng Research Center (PSERC) s a mult-unversty Center conductng research on challenges facng the electrc power ndustry and educatng the next generaton of power engneers. More nformaton about PSERC can be found at the Center s webste: For addtonal nformaton, contact: Power Systems Engneerng Research Center Arzona State Unversty 577 Engneerng Research Center Tempe, Arzona Phone: Fax: Notce Concernng Copyrght Materal PSERC members are gven permsson to copy wthout fee all or part of ths publcaton for nternal use f approprate attrbuton s gven to ths document as the source materal. Ths report s avalable for downloadng from the PSERC webste Texas A&M Unversty. All rghts reserved.

4 Acknowledgements Ths s the fnal report for the Power Systems Engneerng Research Center (PSERC) research proect ttled Integraton of Asset and Outage Management Tasks for dstrbuton Applcaton (T-36). We express our apprecaton for the support provded by PSERC s ndustry members. The proect ndustry advsors wth afflatons at the tme of the proect approval were: ABB (James Stoups), AEP (Doug Ftchett), Calforna ISO (Al Choudhury), GE (Devn Van Zandt), NRECA (Robert Sant), PG&E (Ben Tatera) and TVA (Bruce Rogers).

5 Executve Summary Faults n dstrbuton system may cause nterrupton of power supply to customers. Snce dstrbuton systems n general encounter hgh frequency of faults caused by weather, component wear and other reasons, the need to reduce outage tme caused by faults s requred for an mportant reason: better servce to customers. Customer requrements on the qualty of servce are constantly growng. As an example, senstve loads n modern ndustry, such as chp manufactures or ore smelters, are very senstve to nterruptons n power supply. The consequence of falure s more severe nowadays than decades before when such senstve loads were not so promnent. The most drect mpact of faults on the utlty proft s the loss n customer sales as well as the ncrease n mantenance expenses. Relablty ndces, defned n an IEEE standard, are used to evaluate the mpact of faults on dstrbuton system performance. The System Average Interrupton Duraton Index (SAIDI) and System Average Interrupton Frequency Index (SAIFI) are the two most wdely used ndces. Lower values of SAIDI and SAIFI are assocated wth hgher levels of dstrbuton relablty. However, each relablty ndex reflects only one or two aspects of system relablty. When analyzng dstrbuton relablty, several relablty ndces should be used. On the other hand, dfferent types of customers have dfferent relablty requrements. The trade-off between the relablty experenced by ndvdual customers and the overall relablty for the whole system should also be consdered when estmatng outage costs. Ths study looked at two ways to mprove the relablty of a dstrbuton system. One s to mprove the performance of outage management tasks so that the mpact of faults can be mnmzed. The second way s to mprove the performance of asset management tasks so that falures occur less frequently and the fault s prevented. Ths study explores the technologes avalable for both asset management and outage management tasks by addressng the followng ssues: Lack of data. Besde voltage and current measurement at substatons, few montorng devces for measurements are nstalled n a dstrbuton system; ths study develops a methodology on how to correlate mprovement n the measurement nfrastructure wth mprovement n performance, a crucal decsonmakng tool for makng nvestment allocatons. Ineffectve processng of faults and mantenance schedulng caused by neffcent use of operatonal data. The fault locaton practce s currently based on trouble calls and manual swtchng whle mantenance s performed ether wth a run-tofalure strategy or wth a fxed ahead-of-the-tme planned schedule whch does not requre operatonal data but yelds less effcent performance. The study ponts out how operatonal data (.e., measurements from ntellgent electronc devces or IEDs) can be utlzed for mplementng more effcent outage and asset management solutons. Independent plannng and operaton of asset and outage management tasks. Those two functons are planned ndependently, ncludng plannng of budgets even though the equpment that may record and collect relevant data from the

6 feld maybe common to both applcatons. The study tes the two plannng functon together through rsk-based cost analyss, a unque soluton for optmzed plannng of budgets and tasks assocated wth both outage and asset management smultaneously. The approaches mentoned above have been mplemented on the dstrbuton system model connected to a bus n an IEEE relablty test system. The result shows that the frequency and duraton of faults decrease and system relablty mproves as a result of the proposed technology deployment and tool mprovements. Addtonal research ssues need to be studed so that the nvestment n asset management and outage management can provde maxmum return. Further research may nclude: Integrated vew of captal nvestment strategy. Ths effort should answer the queston of how the nvestment n montorng equpment should be allocated among asset management and outage management tasks n a most effcent way,.e., how to gan the largest return for utltes and the greatest mprovement n relablty of the system for the customers. The rsk-based assessment of outage cost can be used as the obectve of the optmzaton problem; Post-fault reconfguraton. The mpact of reconfguraton cost after the fault has been located usng the outage task needs to be addressed. The followng two costs should be compared: o The mplementaton cost of the best scheme for reconfguraton made possble wth mprovements n the technology and tools proposed n ths study o Cost of conventonal practce: solate the faulted area and after the replacement or repar s done restore servce to the customers that lost power due to the fault. Placement of IEDs n a dstrbuton system. Ths topc requres a more comprehensve study of the optmal placement of IEDs. Gven a certan amount of captal funds, the research needs to focus on where the measurements should be taken n the system so that the overall accuracy of fault locaton program s maxmzed.

7 Table of Contents 1. Introducton Background Asset Management n Electrc Utlty Dstrbuton Outage Management n Dstrbuton System Common Issue between Asset Management and Outage Management Report Organzaton Concept of Integraton Introducton Concluson from a Survey Concept of Integraton Concluson Optmzed Fault Locaton Introducton Overvew of Fault Locaton Approaches n Dstrbuton System Impedance-based approaches Model-based approaches Supermposed components based approaches Travelng wave based approaches Constrants of Fault Locaton Approaches Types and volume of data Error n feld-recorded data Fault Locaton Algorthm Capable of Dealng wth Imperfect Data Condton Data requrement Methodology Smulaton results Dspatch of feld crew based on result of fault locaton program and outage cost assessment Problem Defnton Procedure Case study Concluson Condton-based Component Mantenance Introducton Identfyng the Crtera for Equpment Condton Assessment Probablty Dstrbutons for the Crtera Hazard Rate Model for Components Allocate the requred level of mantenance for each component Optmze the Improvement of the Crtera for a Component Mantenance Schedulng Equpment Deratng Rsk-based Assessment for Improved Relablty and Related Benefts from Data Integraton v

8 5.1 Introducton Effect of Asset Management Tasks on Outage propertes Effect of Outage Management Tasks on Outage Propertes Defnton of rsk Concluson Future Research Concluson References Proect Publcatons Appendx: Summary Sheet of the Survey v

9 Lst of Fgures Fgure 2.1 Tradtonal Dstrbuton Utlty Busness Process. 5 Fgure 2.2 Integrated Asset Management and Outage Management Tasks 6 Fgure 3.1 Flow chart Fgure 3.2 Current necton from a fault at node m Fgure 3.3 Flow chart for fault-case smulaton Fgure 3.4 Test system.. 17 Fgure 3.5 Nodes wth smallest γ Fgure 3.6 Flow chart of program for dspatch of feld crew 20 Fgure 3.7 Dstrbuton system for RBTS Bus Fgure 4.1 Hazard Rate Recalculated Usng Matlab 29 Fgure 4.2 Seres-Parallel Relablty Model for Power Transformer 30 Fgure 4.3 Seres-Parallel Model for Transformer Crtera.. 30 Fgure 4.4 PBR Vs. SAIDI.. 32 Fgure 4.5 Radal Dstrbuton System wth Zones.. 32 Fgure 4.6 Sub Optmal Routne.. 34 Fgure 4.7 System wth two zones Fgure 5.1 Assessment of Rsk-reducton v

10 Lst of Tables Table 3.1 Fault scenaros for Case Table 3.2 J and RI 18 Table 3.3 Lst of data condton and result 19 Table 3.4 Scalars 23 Table 3.5 Feeder & load nformaton 23 Table 3.6 Rsk calculated for 3 schemes. 23 Table 3.7 Feeder & load nformaton 24 Table 3.8 Rsk calculated for 3 schemes 24 Table 3.9 Rsk calculated for 3 schemes 24 Table 4.1 Falure Modes of a Power Transformer. 26 Table 4.2 Crtera for a Power Transformer 27 Table 4.3 Outage Duraton Data.. 35 Table 4.4 Swtchng Data. 35 Table 4.5 lmtaton on mnmum achevable SAIDI. 35 Table 4.6 Allocated hazard rates to acheve the requred system SAIDI 36 Table 4.7 Wndmll Smulaton Results of Acheved SAIDI. 36 Table 4.8 Falure Modes of Power Transformer 37 Table 4.9 Relatonshp between current and temperature rse. 39 Table 5.1 Relablty ndces gven n [17] 44 Table 5.2 New numbers 44 v

11 Nomenclature IED Intellgent Electronc Devce σ Standard Devaton J Jacob ndex RI Resdual Index γ Weghted Devaton h Hazard Functon SAIFI System Average Interrupton Frequency Index SAIDI System Average Interrupton Duraton Index ENS Energy Not Served ASIDI Average System Interrupton Duraton Index MAIFI Momentary Average Interrupton Event Frequ MED Maor Event Day AM Asset Management OM Outage Management λ Falure rate MTTF Mean Tme To Falure N Number of Customers LT Total Connected kva d Duraton of Outage v

12 1. Introducton 1.1 Background Faults n dstrbuton system may cause nterrupton of power supply to customers. Snce dstrbuton systems n general encounter hgh frequency of faults caused by weather, component wear and other reasons, the need to reduce outage tme caused by faults s requred for several reasons: Better servce to customers. Customers requrement on the qualty of servce s constantly growng. As an example, senstve loads n modern ndustry such as chp manufacture and ore smelter are very senstve to nterruptons n power supply. The consequence of falure s more severe nowadays than a decade before; Return on nvestment for utlty shareholders. The most drect mpact of faults on the proft s the loss n customer bllng, as well as mantenance expense. The concern s how to reduce the outage and repar tme so that the servce can be restored as soon as possble. Relablty ndces defned n an IEEE standard are used to evaluate the mpact of faults on power dstrbuton performance [1]. The System Average Interrupton Duraton Index (SAIDI) and System Average Interrupton Frequency Index (SAIFI) are two most wdely used ndces. The lower the value of SAIDI and SAIFI, the better the performance n terms of relablty. Accordng to a survey done by the IEEE Workng group on dstrbuton relablty, n the year 2007, the average SAIDI derved from SAIDIs provded by 153 utltes s mn/(customer*year), and the average SAIFI s 3.20 /(customer*year) [2]. Currently the mprovement n dstrbuton performance s hampered by four maor ssues: Lack of data. Besde voltage and current measurement at substatons, few montorng devces for measurements are nstalled n a dstrbuton system; Agng of equpment. Most of the prmary equpment nstalled n the USA dstrbuton system s pretty old, n some nstances over years. Ineffectve fault restorng and mantenance schedulng caused by the lack of data. The fault locaton s currently based on trouble calls and manual swtchng [3] whle mantenance s performed ether wth a run-to-falure strategy or wth a fxed ahead-of-the-tme planned schedule [4]. Nether requre operatonal data; Independent plannng and operaton of asset and outage management. Those two functons are planned ndependently even though the equpment that may record and collect relevant data from the feld maybe common to both applcatons. Technologes have been proposed to reduce the frequency and duraton of faults. For outage management, effort has been made to better process the trouble calls [5], supplement nformaton from trouble calls wth automatc meter readng (AMR) system and other sources [6], and to nvestgate varous methods to locate faults [7]-[9]. For asset management, condton-based mantenance has been proposed to prevent component 1

13 falure and reduce cost by montorng real-tme electrcal quanttes and assessng condton of equpment [10, 11]. 1.2 Asset Management n Electrc Utlty Dstrbuton Today s dstrbuton utltes have to face the challenges of cost, growng demand, envronmental concerns, regulatory ssues, customer satsfacton and relablty ssues. Ths has gven ncreased mportance to the cost effectve and effcent use of physcal assets. Asset management wthn an electrc power dstrbuton utlty nvolves makng decsons about those assets to allow the busness to maxmze long term profts, whle achevng maxmum customer satsfacton wth acceptable and manageable rsks. [12] The goals of asset management are to reduce spendng, mprove performance and effectvely manage rsk, and to fnd an optmal balance among these. Asset management must consder ssues such as agng nfrastructure, asset utlzaton, mantenance plannng, automaton, relablty and rsk management. Asset management can be broadly dvded nto three man areas: Management, Engneerng and Informaton Processng actvtes. Techncal aspects of Asset Management nclude all the Engneerng actvtes mentoned above. Asset Management for large scale complex power systems can be categorzed based on the tme scale as: Short-term asset management: The Man task of short term asset management s to ensure the secure and relable operaton and control of the power system. Ths focuses on real tme system montorng, trackng asset condtons, and performng fault restoraton to mprove system s relablty. System montorng, and trackng of asset condtons, s done through Supervsory Control and Data Acquston (SCADA) system and Geographc Informaton System (GIS). Md-term asset management: Md-term asset management focuses on the mantenance aspects of physcal assets. Mantenance s an mportant part of any asset management actvty. Mantenance polces are selected to satsfy both techncal aspects (to ensure relablty and safety of supply) and fnancal aspects (as cost s nvolved n mantenance actvtes). Mantenance actvtes can be dvded as emergency (correctve), preventve (scheduled) and predctve (condton based). Long term asset management: Long term asset management nvolves strategc plannng actvtes for network growth, takng nto account ncreasng load, relablty, qualty of supply, envronmental, and regulatory ssues. For radal systems, whch make up the maorty of dstrbuton systems, the least relable equpment affects the relablty of the entre system. Hence t s desrable that the decson-makng technques used n strategc plannng must consder the condton and performance of the varous assets of the system. 1.3 Outage Management n Dstrbuton System Outage management focuses on detectng, locatng and clearng of faults. Currently fault locaton method can be classfed nto followng categores: Trouble call-based approach: The frst call mples a potental network falure (fault alert), whle addtonal calls confrm the falure and altogether they form a data base for the outage management tool. Each call s assocated wth a physcal locaton on the network through the customer-network lnk. The outage tool analyzes trouble calls that 2

14 are not assocated wth known or verfed outage, and then group them nto probable outages. Logcal analyss such as fuzzy logc s then appled to provde an outcome based on a set of rules. Impedance-based approach: The mpedance s calculated usng voltage and current phasors recorded from one or both ends of the feeder where the fault occurs. The locaton of fault s then provded n terms of dstance from one end of a branch. Impedance-based approach has been appled successfully n transmsson system, but does not work well n dstrbuton systems, because of the complcated topology and lack of sensors. Travelng wave-based approach: Travelng wave method reles on calculaton of tme for the travelng wave to reach the end of the lne. Determnng accurately the arrval tme of the travelng wave to the sensor s crucal for such methods. As the travelng wave travels at the speed close to speed of lght, a dfference of 1μS n tme wll cause an error of approxmately 150 meters. As a result, wave detecton technque and a hgh samplng rate are requred, whch s not avalable for most dstrbuton systems. Model-based approach: Model-based methods assume faulted node and compare the smulated electrc quanttes (node voltage, lne current, etc.) wth recorded values. Such methods are favored under the condton of sparse measurements,.e. feld-recorded data are not suffcent to support other methods. System model can be obtaned from SCADA and requres no extra nvestment. The fault locaton algorthm developed n ths work s a model-based algorthm. 1.4 Common Issue between Asset Management and Outage Management The new technologes n both asset management and outage management use nonoperatonal data, whch s recorded n the feld by ntellgent electronc devces (IEDs), and reveals the current condton of the system. The overlappng IED database use by outage and condton-based asset management makes ntegraton of outage and asset management possble. Today s software soluton provders are focusng on packagng applcatons that wll work as a sngle tool addressng the busness processes across the multple departments n an electrc utlty. They are provdng an ntegrated set of applcatons that work together n real tme gvng enterprse-wde vsblty, whch helps to mprove busness processes. Most of the leadng software solutons provders for electrc utltes cover applcatons lke Customer Care and Bllng, Asset and Work Management, Outage and Dstrbuton Management, Supervsory Control and Data Acquston (SCADA) systems, Geographcal Informaton Systems (GIS), Moble Work Force Management and Enterprse Busness Intellgence. The expected benefts from the ntegraton nclude: savngs n IED nstallaton expendtures, effcent collecton and use of non-operatonal data, reduced falure cost and better system relablty, and fnally more return on nvestment. 1.5 Report Organzaton After an ntroducton, the report presents the concept of data ntegraton, as well as new solutons for outage management and asset management tasks. The rsk based approach to cost analyss and the benefts of ntegraton of the two tasks are dscussed next. Future research suggestons and concluson are gven at the end. 3

15 2. Concept of Integraton 2.1 Introducton Ths secton focuses on problem defnton for the proect. The most needed nformaton for dstrbuton system operaton was addressed by conductng a survey at the begnnng of the proect. Concept of ntegraton and the beneft are defned afterward. Technology supported by ntegraton that provdes the most needed nformaton s developed n separate sectons. 2.2 Concluson from a Survey To address the actvtes that can mprove dstrbuton operaton, a survey of the mprovements needed for dstrbuton systems was conducted n January, Sx utltes partcpated n the survey. Ten currently not avalable functons/features that had potental use were lsted. Partcpants were asked to mark the proposed functons/features as very useful, not so very useful, useful, maybe useful, of lttle use and already avalable. The potentally useful functons/features are: Automated fault locaton wth hgh accuracy; Fault predcton based on early detecton of ncpent faults; Component falure predcton: next falure, tme to falure, consequences; Estmaton of IEEE 1366 relablty ndces; Mantenance suggestons to mprove relablty, prevent ncpent faults, mtgate power qualty; Lne, transformer, component loadngs; Feeder voltage profles, ncludng sags; Load status: power consumpton, swtchng state; Asset management plannng; Detecton, classfcaton and verfcaton of faults, and automated analyss of related fault clearng sequences. A summary sheet of the survey s provded n Appendx I. Accordng to the results, automated fault locaton wth hgh accuracy was recognzed as the most useful, followed by component falure predcton. Lne, transformer and component loadngs, and fault predcton are also consdered as very useful nformaton by many utltes. Our proect focused on explorng technologes to provde and mprove the fault locaton and component falure predcton by ntegratng the outage and asset management tasks. 2.3 Concept of Integraton A tradtonal dstrbuton utlty busness process approach s llustrated n Fgure 1. In ths approach, outage analyss s prmarly based on nputs from outage detecton, tellng whch customers are connected, and ncdent verfcaton reportng (IVR), tellng whch customers have reported loss of power. Asset management s prmarly based on off-lne data wthout extensve use of operatonal and/or condton based non-operatonal data. 4

16 Wth the development of new technology n fault locaton and mantenance predcton, system falures may be reduced n terms of frequency and duraton. Stakng Dynamc GIS Vewer Statc GIS Data Vewer Fnance & Accountng CRM Customer Bllng GIS SCADA Load Management AMR Engneerng Analyss Outage Analyss Fnance & Accountng Department IVR Engneerng Department Outage Detecton Operatons Department Fgure 2.1 Tradtonal Dstrbuton Utlty Busness Process One of the constrants to mplement those technologes s the avalablty of data. Condton-based mantenance, for nstance, requres real-tme feld-recorded data, for example voltage, load current, etc to perform the condton assessment. On the other hand, to mplement a model-based fault locaton algorthm, an accurate system model s requred, ncludng system topology, on/off status of swtchng devces, parameters of components, etc. [9] Technologes have been proposed to reduce the frequency and duraton of faults. For outage management, effort has been made to better process the trouble calls [5], supplement nformaton from trouble calls wth AMR system and other sources [6], and to nvestgate varous methods to locate faults [7],[8],[9]. For asset management, condton-based mantenance has been proposed to prevent component falure and reduce cost by montorng real-tme electrcal quanttes and assessng condton of equpment [10, 11]. The new technologes n both asset management and outage management use nonoperatonal data, whch s recorded n the feld by ntellgent electronc devces (IEDs), and reveals the current condton of the system. Ths paper consders the overlappng of IED database use by outage and condton-based asset management and proposes the concept of ntegraton of asset management and outage management tasks. The expected benefts from ntegraton nclude: savngs n IED nstallaton expendtures, effcent collecton and use of non-operatonal data, reduced falure cost, better system relablty, and fnally more return on nvestment. From the dscusson above t can be concluded that the flow of data requred to mprove the busness processes s no longer as shown n Fg.1. Outage management and asset management now share the need for certan data and models. It s more effcent to 5

17 generate an ntegrated database. Integratng the outage and asset management tasks through the use of data and models of common nterest should enhance the effcency and effectveness of the overall busness process because t prevents ether duplcaton or lack of nvestment n nstallng montorng devces, and collectng and storng data. Ths strategy of usng extensve feld data provdes two benefts: Due to mproved mantenance, prmary equpment wll fal less frequently, reducng the number of forced outages; Due to more precse locaton of a faults and better predcton of the equpment health, outage restoraton practces wll be far more effcent and effectve. The beneft can be evaluated from two aspects: System relablty. Ths s reflected by the mpact on relablty ndces. Return on nvestment. Ths s measured by optmzaton n captal and operatng expense. The mproved busness process should explore the correlaton of outage management task wth the task of rsk-based management of equpment assets leadng to optmzed equpment mantenance practces. Ths wll reduce the rsk of outages, as measured by relablty ndces, energy not served, cost of falure, or other measures. The optmzaton may be mplemented usng an asset management concept that selects and schedules mantenance tasks to mnmze outage rsk. Budget Automatc Meter Readng Montorng Devces Data Collecton and Comuncaton Non- Operatonal Database Operatonal Database Fault locaton algorthm selecton Fault detecton Fault solaton Mantenance Replacement Reconfguraton Mantenance Cost Mantenance Allocaton Labor Condton Assessment Delta_Rsk Rsk assessment Relablty Indces Fgure 2.2 Integrated Asset and Outage Management Tasks The ntegrated asset and outage management tasks are shown n Fg.2. Fault locaton and condton assessment retreve feld-recorded operatonal and non-operatonal data, as well as system models and confguraton data from a common database. Based on ths data, the reducton n falure cost s evaluated n an ntegrated rsk-based assessment program. 6

18 2.4 Concluson In ths secton, the result from a survey that was conducted at the begnnng of ths proect s reported. The new approach to ntegraton of asset management and outage management s ntroduced and wll be explored n followng sectons. The concept how the ntegraton s realzed and mplemented s also brefly dscussed. 7

19 3. Optmzed Fault Locaton 3.1 Introducton Ths secton focuses on new applcatons n outage management. Computer-based fault locaton program and the practce of nspecton n the system by feld crew are consdered as two parts of a fault locaton task. Stochastc process s ntroduced n fault locaton program to mprove the robustness of the algorthm to mperfect nput data. Schedulng and dspatch of feld crew s determned by the result of fault locaton algorthm and rsk analyss. 3.2 Overvew of Fault Locaton Approaches n Dstrbuton System The outage management ncludes two man aspects: fault locaton and restoraton. Electrcal faults are detected and solated by protectve devces. Outage tme can be reduced f the locaton of fault can be determned quckly. Fault locaton n dstrbuton system s more dffcult than n transmsson system for the followng reasons: The topology of dstrbuton system s more complcated. The structure of the system can be radal, loop or mxed. It s common to have laterals on the lne. Even f the dstance of the fault pont to a node s acqured, t s stll hard to tell at whch lateral t s located. As a result, the fault locaton algorthms may have multple solutons. The data avalable s lmted. Most frequently used are the fundamental frequency voltage and current data obtaned from substatons at feeder supply transformers. Compared to the complexty of the task, the nformaton provded by ths data qute often s not suffcent. The load and fault resstance have maor mpact on fault locaton accuracy. In general, the fault locaton approaches can be classfed nto the followng categores: mpedance-based, model-based, supermposed components based, travelng wave based and artfcal ntellgence based Impedance-based approaches The mpedance-based approaches calculate fault locaton from the apparent mpedance seen lookng nto the lne from one end (sngle-termnal) or both ends (two-termnal). The phase-to-ground voltages and currents n each phase must be measured. The precondton s that the fault resstance s assumed to be zero. In practcal cases the fault resstance can not be gnored. As a result, the accuracy of sngle-termnal method s not optmal. In two-termnal approaches, the effects of fault resstance can be mnmzed or elmnated. The mpact of load current can be mnmzed by measurng pre-fault data. Other mpedance-based approaches nclude negatve and zero sequence mpedance calculaton. Such approaches are devoted to estmatng fault locaton n radal systems. 8

20 3.2.2 Model-based approaches Such approaches are based on the dea of the ntegraton of network nformaton wth dstrbuton automaton nformaton. The prncple of such approaches s fndng the smlarty between smulated and measured fault sgnals. To acheve such a goal, the followng data s needed: Pre-fault and post-fault voltages and currents from the nodes n the system, the topology of the system and status sgnals from the equpment (the state of relays, etc.) The accuracy of the system model determnes the performance of the method. The fault sgnals can be voltage or current. Fault n each lateral of the same fault type s smulated usng the system model, and the fault locaton s assumed to be the one whose smulaton result matches the feld-recorded fault sgnals the best. Such approaches can provde a sngle soluton to the problem,.e. the dstance to the fault pont from a node and lateral. Ther applcaton does not confne to radal systems. It s understandable that the more data from system nodes s avalable, the more accurate the approach. The voltage and current data from the nodes of the system s very lmted, and may not be avalable to the current outage management system Supermposed components based approaches Such approaches frst calculate the supermposed voltage, and then nect t at the assumed fault pont. If the pont s correct, the sound phase nected currents at the actual fault pont wll be around zero. To calculate the supermposed voltage (dfference between pre and post-fault voltage), the pre-fault and post-fault data s needed. Such approaches are confned only to calculaton of the fault locaton on radal dstrbuton lnes Travelng wave based approaches Travelng wave based approaches were frstly utlzed n transmsson lne fault locaton. The prncple of travelng wave approaches s to record the travelng waves (ether the fault wave tself or the wave of the sgnal nected to the lnes) and calculate the dstance of fault from the locator accordng to the tme recorded. The fault locators were classfed as Type A, B, C and D accordng to ther mode of operaton. As the speed of electro-magnetc wave s close to the speed of lght, data acquston devces wth hgh samplng rate s requred. The lne parameters are needed to calculate the accurate wave speed. For the two-ended measurement, the synchronzaton of both data acquston devces s requred. The travelng wave approaches have not been wdely appled n dstrbuton system, because of the cost, naccuracy caused by equpments along the lne, and because t s relatvely easy to used ths technque only on systems wth smple topology and few laterals. 9

21 3.3 Constrants of Fault Locaton Approaches Types and volume of data Except for the model-based approaches, all fault locaton approaches above has the defect of nflexblty of nput data. Very few nformaton other than feld-recorded data s used. For dstrbuton system wth few sensors nstalled and poor data qualty, the accuracy of such approaches s not guaranteed. The model-based approaches are flexble n number of nputs, whch make t better than other approaches. However, selecton of the faulted node s based on errors between smulated values and recorded values. The errors actually come not only from calculaton but also from measurement error, model error, etc. In ths secton a new algorthm s developed to deal wth such problems Error n feld-recorded data As mentoned n the ntroducton, the avalablty of fled data at the dstrbuton level s not as good as at transmsson level. The mperfecton of feld data has two aspects: Insuffcency: sensors placed n dstrbuton systems for protecton and montorng purpose are very few because of the lack of nstrument transformers and communcaton facltes along feeders. In addton, data from avalable sensors are mostly phasors or ust magntudes that are not tme-synchronzed. Inaccuracy: data recorded n the feld s prone to errors due to unrelable communcatons and potental calbraton problems wth the sensors. A fault locaton algorthm mplemented n dstrbuton systems must be able to deal wth the poor data condton. A model-based algorthm may be selected to deal wth the nsuffcency of data, but data processng technology s needed for dealng wth the naccuracy of data. Data error needs to be analyss carefully before any method s proposed to reduce the mpact of error. The data requred for the fault locaton algorthm proposed n ths paper are phasors from the feeder root and scalars from some nodes n the dstrbuton system. Data acqured may be contamnated n two steps: at the sensor and durng transmsson. A/D converson, phasor calculaton and electro-magnetc nterference (EMI) are all possble sources of error. The model of acqured data may be represented as: where: Xˆ s the contamnated data; G s the gan rato; e(x) s the total error nserted; X s the true value of the electrc quantty; D(X) s the offset assocated wth X; X ˆ = X + e( X ) = X + [( G 1) X + D( X ) + x]

22 x s the random error (whte nose). The error conssts of three parts: gan factor G, offset D and random error x. The frst part s proportonal to the true value of data, whch comes from dfferences n the calbraton of measured value, caused by the rato of nstrument transformer, voltage reference n A/D converson, etc. Offset s a constant value ntroduced mostly by the dfference n the ground voltage and random error x may come from varous sources such as nstrument transformer saturaton or EMI. Although t s hard to predct the random error, t s reasonable to assume that t has a normal dstrbuton: where x n s the rated value of X; p (x) s the densty functon of x; 2 σ s the varance of x. x xn 2 ~ N(0, σ ) 2 1 x p( x) exp( ) 2 σ 2π 2σ The approaches for reducng the mpact of data error are: 3.2 = 3.3 Cancel out the gan and offset parts of data error by dong smple processng operatons such as subtracton or dvson; Rely more on accurate data and less on naccurate data; Detect and elmnate bad data when data error exceeds the threshold. Methodology for mplementng such approaches s descrbed n the followng secton. 3.4 Fault Locaton Algorthm Capable of Dealng wth Imperfect Data Condton The merts of the algorthm proposed as a dstrbuton system fault locaton method are as follows: It deals wth the realty of nsuffcent measurements n dstrbuton system, although the accuracy of the algorthm s affected by the number and placement of the measurements. It mnmzed the mpact of fault mpedance on the accuracy by consderng fault as a specal load connected to the faulted node. It takes nto account the characterstcs of dstrbuton system: non-transposed feeders, sngle-phased lne sectons and nodes, and radal topology. 11

23 3.4.1 Data requrement The proposed fault locaton algorthm s based on the one publshed n [16]. Some mprovements are made for handlng nsuffcent and naccurate data. Followng s a detaled descrpton of data requrements. a.) Electrc quanttes Voltage and current phasors from a feeder root and voltage magntudes from sparse measurements at some nodes of the system are needed. Both pre-fault and fault values are requred. b.) Feeder database The topology nformaton s requred to buld the model of the system. The lne parameters, transformer locatons and nomnal power for each transformer must be provded. c.) Load The changes of loads connected to the secondary sde of transformers accordng to voltage varatons are estmated n load modelng. A generc statc load model presented n reference [13] s used n the fault locaton algorthm. where V V S ) S: power consumed by load when voltage magntude s V; P, Q n n : nomnal actve and reactve power; np, nq : actve and reactve power exponents. np nq = Pn ( ) + Qn ( 3.4 Vn Vn d.) Measurement nformaton Locaton of voltage measurements and the standard devaton of measurement error are needed Methodology The flow chart of proposed fault locaton algorthm s shown n Fgure 3.1. The algorthm conssts of four steps: Pre-fault load flow calculaton, estmaton of applcablty, fault smulaton and faulted node selecton. The four steps wll be descrbed separately n the followng sectons. Processng of data takes place n the step of estmaton of applcablty where the data condton s estmated usng J (Xˆ ) detecton test [14]. If the number of recorded data ponts and accuracy cannot satsfy the requrements for mplementng the algorthm, bad data s removed from nput values. The procesure s repeated untl the data s good enough for the algorthm to be executed or no more data can be removed and the program s termnated. 12

24 start Generate lst of fault cases Run power flow Evaluate applcablty Elmnate bad data Y OK to process fault cases? N Bad data detected? Y Smulate fault cases Select faulted node usng WLS crteron N Termnate program Output result Fgure 3.1 Flow chart a). Power flow soluton The load flow algorthm for radal system descrbed n [15] s used to calculate pre-fault voltage magntudes. Fxed-mpedance model s used for load modelng. In the ntal stage, all node voltages are assgned wth voltage recorded at the root of feeders. Backsweepng to update branch currents usng 3.5 and 3.6 and forward-sweepng to update node voltages usng 3.7 s done n each teraton. The stoppng crteron for teratons s defned by 3.8. I = Z V 3.5 ( k ) _ n k b _ 1 L _ n ( k ) b _ p ( k 1) n I = I + I 3.6 V ( k ) _ n = V Z I 3.7 ( k ) ( k ) ( k ) n m b _ b _ where k s the number of teraton; ( k ) ( k 1) max{ V V } < ε, n=1,, N 3.8 n n 13

25 I s the necton current at node n; ( k ) _ n Z _ s the three phase load mpedance matrx at node n; L n (k ) V n s the node voltage of the down-stream node of branch ; I _ s the branch current of branch, whch flows from node m to node n; k b I s the branch current of branch p, whch flows out from node n; ( k ) b _ p Z _ s the three phase lne mpedance matrx for branch ; b ε s the threshold for change n node voltage. N s the total number of nodes. b). Estmaton of applcablty The J (Xˆ ) detecton test from [14] s appled to estmate the condton of data,.e. f the number and accuracy of voltage measurements are good enough for a relable output. Calculated value of voltage magntude at node from pre-fault load flow calculaton s cal meas desgnated as V, whle feld-recorded value s desgnated as V. Weghted, pre dfference J s defned as ( VN s the rated voltage): The J ndex s the summaton of J V = J : cal meas, pre V, pre V N σ = J Relablty ndex of feld-recorded data s defned as: 2, pre 3.9 J 3.10 J m RI = m where m s the number of redundant measurements. For the proposed algorthm, load flow calculaton reles only on voltage and current phasors at feeder roots, m s the total number of voltage measurements. The value of s and RI reveal the condton of data. Large value of ndvdual ndcates that data from measurement s very lkely to be bad data and should be elmnated; Large value of RI ndcates that ether the number of measurements are not enough for a relable output, or bad data exsts, or sever error exsts n system model, such as wrong topology or load nformaton. J and RI are used as double crtera. If J <25 stands for all J s, and f RI<3, the data condton s consdered as acceptable, and the program wll proceed to fault smulaton. If for one or two J >25, data from the correspondng measurements wll be elmnated and 14

26 RI wll be recalculated. If the crtera can not be met by elmnatng bad data, the program s to be consdered not applcable under the current data condton. c). Fault-case smulaton A lst of fault cases s generated accordng to the affected area. All nodes wthn the affected area are consdered as a suspect faulted node. Fault-case smulaton s executed for each case, and the calculated value of node voltage magntudes at nodes wth voltage measurements are recorded. The algorthm for fault case smulaton s smlar to pre-fault load flow algorthm. Fault s consdered as a specal load connected to the faulted node, as s shown s Fgure 3.2. The total necton current s the summaton of fault current and load current. Root node c b a m k Other secton lnes [Zl] [Zl] Raf Rbf Rcf Load1 Ifa Ifb Ifc Load2 Rf Ifa + Ifb + Ifc Fgure 3.2 Current necton from a fault at node m The equvalent mpedance of the fault s not of nterest. The fault current s calculated at the end of every teraton and added as current necton caused by fault at the faulted node usng 3.12 and 3.13: I = I + ( I I ( k ) ( k 1) df, meas df, cal f f rn rn ) 3.12 where (k ) I f s fault current; I = I + I 3.13 df,( k ) _ n df,( k ) _ nl ( k ) f df, meas I rn s the current measured at feeder root; df, cal I rn s the calculated current at feeder root; I s the necton current at faulted node n; df,( k ) _ n I s the necton current from load connected to n. df,( k ) _ nl The flow chart for fault case smulaton s shown n Fgure

27 Get fault case Get durng-fault V and I from feeder rooot Intal node voltage wth V and calculate necton current Do backward-sweepng to update branch current Do forward-sweepng to update node voltage Update fault current N Voltage values converged? Y Record voltage from measurng-nodes end Fgure 3.3 Flow chart for fault-case smulaton d). Faulted node selecton The lkely fault locaton s selected takng nto account all analyzed nodes durng the fault locaton process. Weghted-devaton s used for locatng the fault. For each analyzed node, the durng-fault magntude devaton between measured and calculated voltage sags s computed: where, cal, meas δ k = Vk Vk, k = 1,... m; = 1,... np 3.14 cal V, k s the dfference n three-phase pre-fault and durng-fault voltage magntudes (voltage sags) calculated at node k consderng node as the faulted node; s the three-phase voltage sags measured at node k; meas V, k m s the total number of voltage measurements; np s the total number of fault cases smulated. The weghted-devaton s calculated as m 2 γ = ( δ / σ ) 3.15 k= 1 The faulted node s the one wth the smallest value of γ. k k 16

28 n f = γ = mn{ γ s}, s = 1... np 3.16 e). Descrpton of the error-mpact reducton The algorthm s capable of mnmzng the mpact of offset error and random error. The offset error s removed by the calculaton of voltage sags offset from pre-fault and durng fault data cancels out n subtracton. As the selecton of faulted node reles on the weghted-devatons, the contrbuton of data from less accurate measurements s reduced n proporton wth the varance of the random error, whch means that data more lkely to have hgh random error has a lower mpact on the result. The proportonal error s not consdered n the proposed algorthm Smulaton results a). Descrpton of the test system A 13.8 kv, 134-node, overhead three-phase prmary dstrbuton feeder s used as the test system. Fgure 3.4 shows the topology of the feeder. Root voltage and current are recorded at node 1. Four voltage measurements are placed n the system, at node 30, 48, 103 and 118 respectvely. They are marked as measurement 1-4 respectvely. The algorthm reported n [16] and the algorthm proposed n ths paper are mplemented and the results are compared. b). Case study Case 1: perfect condton Fgure 3.4 Test system In ths case, the feld-recorded data are not contamnated by errors. Fault scenaros are lsted n Table

29 Table 3.1 Fault scenaros for Case 1 Faulted node Fault type Fault resstance (Ω) 17, 36, 42, 107 A-G 1 63, 90 A-G 10 5, 77 A-B-C 5 86 A-B Fgure 3.5 Nodes wth smallest devaton ndex Both the algorthm reported n [16] and the proposed algorthm gve correct result for all scenaros. Fgure 3.5 shows the smallest γ calculated for fault occurrng at node 36. Case 2: Bad data A-G fault at node 36 s smulated, but pre-fault and durng-fault voltage magntude recorded by measurement 2 (node 48) are added wth errors of 20% and 15%. Varances of random error σ for all voltage measurements are The faulted node selected by the algorthm proposed n [16] s node 48, whch s ncorrect. J and RI calculated by the proposed algorthm are lsted n Table 3.2. J 2 s very large, ndcatng that data from measurement 2 are bad data and should be elmnated. RI after bad data elmnaton s less than 3, and the program contnues wth data from measurement 1, 3 and 4. J Table 3.2 J and RI RI Before data removal: After data removal: γ 18

30 The faulted node selected by the proposed algorthm after bad data elmnaton s node 36. The proposed algorthm selected the rght node agan. Case 3: Data wth errors A-G fault at node 36 s smulated. Errors wth densty functon from equaton 3.3 s generated accordng to σ of the measurement and added to the true value of the measured pre-fault and durng-fault quanttes. Four data condtons are desgned and ten sets of data are generated and fed to fault locaton program for each data condton. Data condton and the tmes that the fault locaton program provdes correct output are lsted n Table 3.3. Measureme nts Table 3.3 Lst of data condton and result Correct tmes σ 1, 2, 3, , 0.01, 0.01, , 2, 3, , 0.1, 0.01, , 2, , 0.01, , 3, , 0.1, The outputs for the two cases where the selected node s wrong are 39 and Dspatch of feld crew based on result of fault locaton program and outage cost assessment Problem Defnton Usng the model-based fault locaton algorthms, the nodes are ranked by the probablty of beng the faulted node. Under some condton (naccurate data, hgh fault resstance, unsatsfyng placement of measurements, etc.) the frst node may not be the nearest node to the faulted pont (Smulaton Result n [16]). But usually the correct node s among the top n nodes. Sometmes these top n nodes are n two areas. The queston s: How do we dspatch feld crew to nspect along the feeders? Other thngs beng equal, the area wth hgher probablty of fault should be nspected frst, so that the tme to locate fault s mnmzed. However, temporary faults take a large proporton n faults occurrng n dstrbuton system, and the probablty of temporary faults developng nto permanent faults (component falure) ncreases exponentally wth tme needed to clear the fault, whch means that customers to the downstream of fault pont may experence longer outage f fault s not cleared n tme. In that case, other thngs beng equal, area wth more loads or wth mportant customers connected should have a prorty n fault nspecton. Schedulng the feld crew to nspect fault should consder the condtons mentoned above. An optmzaton problem s formed accordngly, wth rsk formed as the 19

31 summaton of probablty that fault s n the area multpled by outage cost of ths area durng fault locaton perod: Rsk = P Cost 3.17 Formulaton of optmzaton problem: where Obectve: mn {Rsk} 3.18 s t. N labor Nlabor.max N labor s the labor assgned to fnd the faulted node Procedure Index s used to desgnate suspcous n the followng dscusson. The procedure s shown n Fgure 3.6. Run fault locaton algorthm Take the top 5 nodes Nodes are n one area? Y Form 2 susceptve areas and calculate probabltes Calculate outage cost for 3 scenaros N Identfy the scenaro wth mnmum outage cost Schedule feld crew to nspect on the area End Fgure 3.6 Flow chart of program for dspatch of feld crew Step 1: Form 2 suspcous areas and calculate probablty for each area that the fault s wthn ths area. The processng starts wth runnng voltage-measurement-based fault locaton algorthm. The top 5 nodes are extracted and classfed. If they belong to two physcally separated areas, the possblty of fault happenng n each area s calculated usng the followng equaton: 20

32 n e 1 k 4 k = = 1 k I n 1 k 4 P 1 e Here the probablty s calculated as the summaton of the confdence the nodes are among top n and are n area. Confdence of the nodes s consdered decreasng exponentally wth the rankng. P s the probablty that fault s n area, and k s the kth node determned by the program as beng n area. Step 2: Estmate tme to locate fault. The functon of estmatng the tme needed to locate a fault n area s: D, Nlabor ) a1 ( D / Nlabor ) t ( = a 3.21 Step 3: Calculate Rsk for dfferent scenaros. 1). Go to area I and f fault s not found, go to area II. N1 L1 L1 ' N Rsk = P1 { β1 t1 + β2 t1 + β3 t1} + P2 { β1 N LT LT N L2 L2 ' + β2 ( t1 + t2) + β3 ( t1 + t2)} L L T The formulaton of rsk functon wll be dscussed n Secton 5. β ~ β 1 3are coeffcents of 3 components that form the rsk functon; N, N 1 2 are numbers of customers connected to area I and II; N s the total number of customers; L, L 1 2 are connected kva from area I and II; L1 ', L2 ' are connected kva from area I and II by prortzed customers; LT s total connected kva; T t,t 1 2 are estmated tme to locate fault n area I and II. The prortzed customers (senstve customers) refers to the customers that demand unnterrupted power supply more than the ordnary ones. Specal contracts are sgned between utltes and these customers (e.g. hosptals, schools, electron chp manufactures, etc.) so that when the qualty of servce does not meet the requrements (too many tmes of nterruptons or hours of outage) penalty to utltes s added. 2). Go to area II and f the fault s not found, go to area I. The calculaton of cost n ths case s smlar to case 1 and s not descrbed n detal. 3). Assgn N labor. 1 to area I and N labor. 2 to area II at the same tme. 2 ( t 1 + t 2 )