EA Technology (UK) EA Technology (UK) NEDL (UK)

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1 Optimising Customer Information and the Fault Management Process D C Newis L Roberts John Barnett EA Technology (UK) EA Technology (UK) NEDL (UK) SUMMARY Electricity companies are under increasing pressure to deliver high quality customer service. Ever tightening regulatory regimes require fault restoration performance to be improved and maintained at a high level. In particular the management of information under emergency conditions and its delivery to customers has been identified as an area in which performance has to be improved. During periods of bad weather a company can find itself moving from a low number of faults to potentially many hundreds in a matter of hours. This is combined with an avalanche of calls to the distribution company for information about likely restoration times. In such emergencies, forecasts of when the customer supplies will be restored have historically relied heavily on human estimates and as such have a tendency to be subjective. There is a clear and necessary opportunity for improvement in this area. EA Technology in conjunction with NEDL have developed a decision support tool for use in the Network Operations Centre environment to meet this requirement. It provides a systematic way of estimating the time to restore customer supplies with significant improvements in accuracy. Known as eastorm, the system estimates the restoration time of faults under emergency conditions and on normal fault days. This enables enhanced information to be delivered to customers, the media and other interested parties. eastorm gives the user greater confidence in making public statements, safe in the knowledge that these will be more accurate than before. This in turn will improve customer relations at times when the company s performance is under particular scrutiny. This new method will estimate the time to restore the customer supplies affected by all the known faults and will indicate how this would vary with different levels of resourcing.it estimates the likely number of faults to be expected by the end of the current storm and provides an estimate of the time to restore customer supplies for the faults that are not yet reported. This will ensure that customers are given more realistic restoration times, reduce their frustration, and help them make their own plans to deal with the event. In addition the company will benefit from less effort on interpretation of high volumes of incident information, freeing resources at a time when they are most stretched. Within the model forecasting restoration is based upon information on historic fault performance, current network faults, current weather conditions and available resources. Network operations staff are able to adjust resource allocation, modelling the effect on fault performance. This provides guidance on when additional resources need to be called out to achieve performance or released to minimise costs. Types of weather related incidents indicate the amount of nested faults that will have to be dealt with. These hidden faults will protract the time to restore supplies and an early prediction of such damage will further enhance the information available from the company. The decision support tool eastorm will assist companies in achieving regulatory goals. The tool allows fault managers to deliver enhanced quality information and assist in the effective and efficient management of the overall fault restoration purpose. This in turn gives customers the opportunity to plan their own strategies based upon a short or long period of expected restoration.

2 Optimising Customer Information and the Fault Management Process D C Newis L Roberts John Barnett EA Technology (UK) EA Technology (UK) NEDL (UK) INTRODUCTION The potential for improved customer information was first identified following discussions between EA Technology and NEDL. These discussions addressed the strategic issues of fault management, linking the costs of network investments and fault response to quantified improvements in the regulatory indicators, such as customer minutes lost. Whilst mainly strategic, the original discussions also introduced the concept of a tactical model that could be used every day to improve the estimates of when all current faults would be restored. Subsequent discussions with NEDL focused the need on these tactical issues. In particular it is required to address the management of information and resources under emergency conditions, with the aim of making significant improvements in the quality of information supplied to the customers and to the media. It is well known that in emergencies it is extremely difficult for the incident managers to make accurate forecasts of when the customer supplies will be restored. However, following recent storms in the UK the regulator concluded that there is a need to enhance forecasts and there is a recognised need for improvements in this area. The new eastorm system outlined in this paper has been designed to meet this requirement. It provides a systematic way of estimating the time to restore customer supplies with significant improvements in accuracy. ISSUES There are two main issues that have to be addressed. Firstly the need to better inform customers, the media and other interested parties about the progress of fault restoration. Secondly the requirement to optimise the cost of dealing with faults and hence more efficiently use the available resources. The UK regulator has reinforced the above and stated that customers need to be better informed about the duration that they can expect a loss of supply to occur. Recommendations as such were made in the Offer (Ofgem) May 1999 report Supply Interruptions following the Boxing Day storms These recommendations have been followed up in the July 2000 Information and Incentives Project Review of PES Measurement Systems. The recent distribution price reviews have also continued to put pressure on distribution companies to reduce their operational costs. It is the responsibility of distribution companies and in particular those managers involved in restoration policy to resolve these and many other issues to achieve customer satisfaction and regulatory performance in the field of faults. OBJECTIVE Customers are becoming ever more demanding of all their utility suppliers. The increasing use of electronic equipment is making people more and more expectant of a reliable, high quality electricity supply. A loss of supply has many potential implications for customers ranging from the impact on local business services to special needs customers reliant on electrical medical used within the home. It is therefore vital that distribution companies are able to communicate with their customers in order that customers themselves can appropriately plan & implement their contingency action. This requires telephony/computer systems to deal with the avalanche of calls that can occur during fault or multiple fault situations However, these systems need to have information input to them based upon data from the field. The UK regulator has identified three requirements

3 of customers when they call to notify distribution companies about faults. Firstly they want the company to know that they are off supply. Secondly the customer wants or even needs to know how long they can expect to be without supply. Thirdly, due to human nature, people like to know what caused the loss of supply. This data will need to be analysed so that useful information can be created. This information will then be used to make the necessary decisions to restore supplies in an optimum manner. The decisions made and the appropriate information then have to be communicated to all interested parties both internal and external to the company. It is the second of these three issues that eastorm addresses. It takes currently available data and models how a company can expect to perform as the fault situation progresses. This enables incident managers to enhance the accuracy of their forecast restoration times and more efficiently use the available resources. THE FAULT MANAGEMENT PROCESS The management of faults varies from company to company. The most appropriate policy will depend on many different criteria from the type of network, the environment of operation to the commercial considerations and judgement of individual companies. Faults under storm conditions may be managed differently to faults that occur on normal days. The decision support tool eastorm is designed to assist the fault management process under both storm and normal fault days. Fig 1 below shows a possible fault management flow diagram. INFORMATION PUBLICITY RESOURCES Fault calls, SCADA, PODS, etc. Weather forecast Other networks Company procedures Network investment Database of past faults Public statements Incident Managers Customer information 12 a Fig 1 Regulatory performance Faults waiting Faults in progress Faults restored Resources in use Normal teams Standby teams Emergency teams Equipment and spares Portable generators The generic process of storm management will begin by monitoring the advance of bad weather. This will alert the company to the possibility of resources being required and raise the company to a predetermined level of readiness. As a storm progresses to and across a distribution company new data will continuously be available. As has already been indicated, the data available about fault conditions is changing constantly. The process of data collection, analysis, decision making and communication requires constant feedback and updating. DATA There is a multi criteria nature to the data available for fault conditions. All of this data is of value and should be analysed. This will enhance not only the forecast restoration times for customers but also the decisions of how to optimise the use of the available resources. The prevailing weather conditions have a direct influence on the nature of faults and therefore the time to restoration. The predicted weather conditions will influence not only the nature of the faults but also the number of future faults that can be expected. The weather will also influence the number of unseen or nested faults. It is important in forecasting the time to restoration that the likelihood of these faults is taken into account. Faults waiting for repair, currently being repaired or already restored will provide valuable information on current performance. In addition the historic data of how the company has performed provides valuable experience that can be used in the forecasting of supply restoration. Knowledge of the resources available to the company is vital in being able to forecast restoration performance. Along with company policy, this data is used to ensure that the forecast information given to customers is as accurate as possible. There may also be company specific data available. This data will vary dependant on the various policies and procedures adopted by distribution companies. With the amount and constantly changing nature

4 of the data available, it is becoming ever more complex to analyse and make the optimum decisions necessary. The decision support tool eastorm has been designed to provide the necessary fault data collection and analysis. The results are presented to give incident managers and customer service staff the information they require at times of stress for the distribution companies customers, network and staff. THE LOGISTICS MODEL eastorm Decision support is provided by eastorm by collecting relevant fault data, analysing the data and presenting information to the user. Fig 2 below shows the typical data flows to and information from eastorm. Trouble Call X Weather data Staff schedules Active faults data Debug and log files Data flows eastorm Fig 2 Recent fault history eastorm predictions and estimations Average fault data from historical fault records Average storm data from historical fault records user interface output file 14 a Once a potential fault situation is identified via weather forecasts etc then eastorm can be initiated to forecast the likely fault damage that may occur. The relevant weather forecast can be entered into the model along with the available resources from a predetermined database. Analysis and can then be performed. The current situation and historic fault data, of faults under similar weather conditions, are simulated using all the available data to forecast the response of the company to the incident. This can be used to move the company to the required level of alertness before and incident. During the incident as new data is received on the forecast weather, number of known faults etc. the simulations provide up to date information that can be given to customers or other interested parties. The simulations also provide incident managers with the ability to model differing levels of resources to deal with the incident. This will assist in the necessary decisions that need to be made to ensure that costs are optimised, regulatory performance is achieved and to highlight when to call additional emergency resources that may be external to the company. HISTORIC FAULT DURATIONS The historic fault data based upon the UK Nafirs (National Fault & Interruption Reporting Scheme) database was used for eastorm. The company specific fault data for the last few years is required, going back far enough to include several major incidents of each type:- snow, ice, wind, lightning, flood, etc. The aim of the analysis is to estimate the duration of faults of different types. The fault records are therefore divided into different categories such as:- Voltage (LV, HV, EHV) Network type (Underground, Overhead) Weather type (Wind, Ice, Snow, Lightning, Rain, Normal) Damaged equipment (Line, Transformer, Pole, etc.) Sub-region (e.g. North, South) Time of incident (Day, Night, Weekday, Weekend, Bank Holiday) The properties of the faults that can be compared against these categories are as follows:- The total fault duration from site arrival to final restoration. The travel time from deployment to site arrival. The number of customers affected by each type of fault. For this purpose the actual length of the interruption seen by the customer is not needed. The travel time will vary with the number of teams on duty. The more teams are on duty, the more likely it is that there will be one nearby who can travel quickly to the site. This pre analysis is performed in order to give eastorm the necessary database to base its forecasts on. The pre analysis should be performed at least once a year to ensure that duration tables are as up to date as possible and reflect most recent practice & performance.

5 IDENTIFYING THE TYPES OF FAULT The fault data is already separated by voltage level into three separate files. However there are several different voltages that are classed as HV (e.g. 6.6, 11, 20, 33, and 66 kv). It may be necessary to check these separately. There may already be a field that distinguishes OH from UG faults, but if not the separation can be done by filtering on the Main Equipment fields MEI1, MEI2, etc. and Component Involved. A set of 8 or 10 of these covers all the overhead equipment that can be either selected for keeping or for deleting. The weather type is not recorded in the data, only the causes of the fault in the fields: Direct Cause and Contributory Cause. These are often given as wind, snow, ice, lightning, etc. but may also be given as tree damage, ageing or corrosion, which may have been influenced by wind or snow. In addition it may be helpful also to work with the types of day rather than the types of fault. To do this the days can be classed as normal, snowy, windy, icy, etc. and then all the faults on each day are treated as being of the same day type. Where the weather was both snow and wind it may be necessary to define an extra category of wind & snow. The equipment involved is given in the fields MEI1 to MEI6 and the damaged components are given by DAM1 to DAM3. These may not be known at the start of the fault, but if the team reports back the information it could then be made use of. For example, if the team reports 10 broken poles the incident manager s estimate of completion will use that information, and this will be passed into eastorm and used by it. The region is indicated by the field: District, which correspond to the sub-regions data, e.g.south and North. The incident date and time is recorded and so the faults can be easily separated into different date and time categories. IDENTIFYING THE PROPERTIES OF THE FAULTS The total fault duration is the time that the team actually took to restore all the affected customers including completion of repairs where necessary. It is measured from the first site arrival time to the final restoration. All stages after the customers involved are reduced to zero correspond to periods of waiting for non-urgent repairs. As these would not be done during storms they can be ignored in this analysis. The total fault duration is therefore defined as the time from the first interruption to the last restoration with customers affected. Any stage with zero customers is ignored. The simulation can later be extended to include the stages of non-urgent repairs that have been left for attention after the storm is over. The above discussion assumes that the site arrival time is known, which may be true for recent data, based on the Company Trouble Management System, but it is not true for the historic Nafirs records. The site arrival time can therefore estimated for the Nafirs data by working back from the first restoration time. Consider the duration of each stage. For stage 1 this is the time from site arrival to the first stage restoration. For subsequent stages it is the time from the previous restoration to the current restoration, i.e. the duration of stage 2 is the time from restoration 1 to restoration 2. It is assumed that where there are many stages then the duration of stage 1 is the same as the duration of stage 2. The average duration was therefore found for the stage 2 of all faults with at least 3 stages which had non-zero customers for stage 2. It was found however that this must be subdivided into those with and without repairs and those with manual restoration. It was assumed that the duration of stage 1 for faults of the same type was the same as the duration of stage 2. The site arrival time was thereby estimated, and hence the fault duration could be calculated. The travel time is measured from the team deployment time to the site arrival time. This cannot be found from Nafirs, but may be available on a company specific basis. The number of customers is the number of customers affected by each type of fault, and can be found from the field: Total Consumers Involved in the Nafirs REPORTS file.

6 Faults are usually restored in several stages and so the average number of stages per fault can be calculated as well as the percentage of those off at each stage who are restored at the next stage. This will allow the simulation to restore the customers gradually over the period of the fault duration. OUTLINE SYSTEM REQUIREMENT The system provides real time information to enable users to inform customers and the media about the time it will take to restore supplies during emergency conditions. This depends on the number of staff available and so it will include a simulation of the fault restoration process to show the effect of varying resources and indicate when more or less staff are needed to hit the performance targets. The system is intended to operate all the time and so will be effective during normal conditions as well as emergencies. eastorm system is heavily dependent on the availability of data describing the fault restoration performance. Historic data is needed to provide estimates of the typical past behaviour, and the on-line data is needed for an understanding of what is happening at the time. In the course of the data analysis it was noted that some of the data relating to the queuing of faults for the repair teams needs to be estimated. It is likely therefore that the accuracy of the system would be improved if two extra items of data were recorded in trouble management systems. These are the time at which a repair team is requested (as opposed to the initial rapid response team) and the time at which it becomes available and is allocated to a particular fault. Refinements such as this are dependent on the operating regime within the Company. OUTLINE SYSTEM ENVIRONMENT It is envisaged that eastorm will be installed in Network Operations Centres and will have a link to existing or proposed IT systems, in particular Trouble Management Systems. eastorm is designed to be installed on a single PC and its outputs will be displayed on a single monitor. In addition, however, the outputs can be written to a networked text file, from where they can be read by a text editor from any PC on a network. BUSINESS CASE CONCLUSION Customers are ever more demanding of all their utility suppliers. The requirements of the regulator put pressures on distribution to improve the quality of service and at the same time reduce costs. Shareholders expect to see a return on their investment. There are therefore multiple conflicting objectives that need to be resolved to ensure that the optimum package is delivered to all the stakeholders of the business. The management of faults is one extremely visible demonstration of how a company is meeting some of its stakeholder expectations. It is therefore crucial that distribution companies are seen to be embracing new technology to assist in meeting the demands that are placed upon them. The decision support tool eastorm will assist in the achievement of enhanced customer information and fault management by:- Giving incident managers a greater time to deal with overall restoration management Significantly reducing the need for manual data collection & analysis within very limited time windows Improved information and decision support from the multiple data sources Simulation of the cost and performance effects of resource balancing Enhanced accuracy of public information hence Fewer customer complaints Information to assist the optimising of cost and performance response REFERENCES (1) OFFER Supply Interruptions following the Boxing Day storms 1998, May (2) OFGEM Information and Incentives Project Review of PES Measurement Systems, July (3) OFGEM Information and Incentives project Output measures and monitoring delivery between reviews Final Proposals, September 2000.