ELECTRIC power distribution systems constitute the

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1 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 20, NO 1, FEBRUARY A Reliability-Centered Asset Maintenance Method for Assessing the Impact of Maintenance in Power Distribution Systems Lina Bertling, Member, IEEE, Ron Allan, Fellow, IEEE, and Roland Eriksson, Senior Member, IEEE Abstract This paper proposes a method for comparing the effect of different maintenance strategies on system reliability and cost This method relates reliability theory with the experience gained from statistics and practical knowledge of component failures and maintenance measures The approach has been applied to rural and urban distribution systems In particular, a functional relationship between failure rate and maintenance measures has been developed for a cable component The results show the value of using a systematic quantitative approach for investigating the effect of different maintenance strategies Index Terms Asset management, electric power distribution system, maintenance strategy, reliability evaluation, reliability-centered asset maintenance (RCAM) I INTRODUCTION ELECTRIC power distribution systems constitute the greatest risk to the interruption of power supply [1] [3] Traditionally, however, distribution systems have received less attention than generation and transmission, evidenced by the difference in the number of publications [4] However, focus is moving toward distribution as the business focus changes from consumers to customers Deregulation of the power system market has led to a shift from technical to economic driving factors The utilities that own and operate the power distribution systems now face various market requirements On the one hand, customers are paying for a service (delivered energy) and the authorities are imposing regulation, supervision, and compensation depending on the degree to which contractual and other obligations are fulfilled, see for example Norway [5], Sweden [6], and the UK [7] On the other hand, utilities must ensure that their expenditure is cost-effective This means that electricity utilities must satisfy quantitative reliability requirements while at the same time minimizing their costs One predominant expense for a utility is the cost of maintaining system assets, for example through adopting preventive measures, collectively called preventive maintenance (PM) PM Manuscript received June 29, 2004 This work was supported by the Competence Center in Electric Power Engineering at the Royal University of Technology (KTH) Paper no TPWRS L Bertling and R Eriksson are with the Electrical Engineering Department, Royal Institute Technology (KTH), Stockholm, Sweden ( linabertling@etskthse; rolanderiksson@etskthse) R Allan is with the Electrical Engineering Department, Manchester Centre for Electrical Energy, University of Manchester Institute of Science and Technology (UMIST), Manchester, UK ( RonAllan@ieeorguk) Digital Object Identifier /TPWRS measures can impact on reliability by either improving the condition, or prolonging the lifetime of an asset Reliability overall can be improved by lowering either the frequency or the duration of interruptions PM activities could impact on the frequency by preventing the actual cause of the failure Consequently, PM is cost-effective when the reliability benefit outweighs the cost of implementing the PM measure There is, therefore, a need for utilities to incorporate systematic methods which relate maintenance of system assets to the improvement in system reliability This is part of the wider concept of asset management Asset management involves making decisions to allow the network business to maximize long term profits, while delivering high service levels to the customers with acceptable and manageable risks Reliability evaluation and maintenance planning techniques have separately been well developed, for example [1] [4], [8], [9], with reliability assessment starting in the 1930s [10] However, few techniques relate system reliability to component maintenance Furthermore, the available techniques are not generally put into practice The reason for this, according with the authors, is the lack of suitable input data and a reluctance to use theoretical tools to address the practical problem of maintenance planning One method for relating reliability to PM is known as reliability-centered maintenance (RCM) RCM is a qualitative systematic approach to organizing maintenance [11] [13] It originated in the civil aircraft industry in the 1960s with the introduction of the Boeing 747 series, and the need to lower PM costs in attaining a certain level of reliability The results were successful and the methodology was developed further In 1975, the US Department of Commerce defined the concept RCM and declared that it should be used in all major military systems [11] In the 1980s, the Electric Power Research Institute (EPRI) introduced RCM into the nuclear power industry Today RCM is used or being considered by an increasing number of electrical utilities [14], [15] The main feature of RCM is its focus on preserving system function where critical components for system reliability are prioritized for PM measures However, the method is generally not capable of showing the benefits of maintenance for system reliability and costs This paper proposes a reliability-centered asset maintenance (RCAM) method, which provides a quantitative relationship between PM of assets and the total maintenance cost [2] The method is developed from RCM principles attempting to relate more closely the impact of maintenance to the cost and reliability of the system The method has been developed from /$ IEEE

2 76 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 20, NO 1, FEBRUARY 2005 comprehensive application studies for real power distribution systems Application studies have been made on two different distribution systems in Sweden: a rural system of overhead power lines in southern Sweden, and an urban underground cable system in central Stockholm, the Birka System Both studies used data for the systems in question, and were done in close co-operation with the operating utilities (Sydkraft AB and Fortum Distribution AB (former Birka Nät AB), respectively) More details are provided for the Birka System in Section IV II RCAM METHOD A Reliability Evaluation This paper addresses the effects of failure events in electric power distribution systems These events occur randomly and therefore models based on probability theory have been used A computer code RADPOW (reliability assessment of electrical distribution systems), based on the analytical approach, has been developed within the Competence Centre of Electrical Engineering at KTH [2] A network modeling technique and the minimal cut set (load-point-driven) approach [1] is used to deduce the failure modes RADPOW evaluates the load point indices, and the overall system indices The load point indices are: expected failure rate, annual outage time (unavailability) (U) [h/yr], average outage duration (r) [h/int], and average energy not supplied (E) [kwh/yr] The system indices are: SAIFI [int/yr,customer], SAIDI [h/yr,customer], CAIDI [h/int], and AENS [kwh/yr,customer] As a first step in the method, the critical components for the system reliability are identified from a sensitivity analysis These components are further studied, focusing on the impact of maintenance measures The relationship between reliability and maintenance has been established by relating the effect of PM to the causes of failures for the component being assessed Two different approaches have been used The first approach assumes a constant reduction ratio between failure rates and the effect of PM, whereas the second approach assumes this ratio to be dependent on time In the first case, depends only on the effect of PM (Approach I) In the second case, is also time-dependent (Approach II), and the failure rate reduction is a consequence of the PM actions considered for the specific component that is studied Formulating the failure rate model for Approach II is a complicated task This has presently been done for one component type, underground cables, which was shown to be critical for the reliability of one of the systems used in these studies The details of the underlying theory are too extensive to be developed in this paper, so only the overall principles, results and applications are included The main stages of the RCAM approach are as follows Stage 1 System reliability analysis: defines the system and evaluates critical components affecting system reliability Stage 2 Component reliability modeling: analyzes the components in detail and, with the support of appropriate input data, defines the quantitative relationship between reliability and PM measures Stage 3 System reliability and cost/benefit analysis: puts the results of Stage 2 into a system perspective, and evaluates the effect of component maintenance on system reliability and the impact on cost of different PM strategies These three stages emphasize a central feature of the method: that the analysis moves from the system level to the component level and back to the system level B Economic Evaluation The economic evaluation brings the RCAM analysis to its final step: to relate the benefits in costs due to the impact of maintenance on reliability The motivation for any PM strategy is that the cost of applying the PM measure should be less than taking no action at all If little or no PM is done, then more system failures are likely to occur resulting in more repair actions being required, ie, in more corrective maintenance (CM) actions Therefore, the important issue is to compare the costs associated with different maintenance methods, including both PM and CM with the objective of minimizing the total cost of maintenance There are several costs that can be related to the effect of system failures Two direct utility costs are: 1) cost of failure (CM), eg, repair costs and losses in revenue due to nondelivered energy and 2) cost of the PM actions, eg, planned maintenance or replacement of a component in advance of failure However, the cost of failure also depends on the customer cost [16] A supply interruption affects the customer, who will suffer supply unavailability and may suffer direct costs and/or be compensated via a penalty payment Consequently, the proposed cost analysis considers: the cost of failure ; the cost of preventive maintenance ; the cost of interruption The optimal maintenance method and PM strategy is the solution that minimizes the sum of these three costs However, in some cases it may not be necessary to include, for example for a simple or first-order comparison of strategies The economic evaluations have been made using fundamental techniques The costs are evaluated on an annual basis with an assumed increase due to inflation Furthermore, the investments in PM measures are spread over the remaining time of the assessment period Finally, the present worth value of the total annualized costs is evaluated The present worth value of one outlay to be paid after years with the discount rate, is gained by multiplying by the present worth value factor III STEPS IN THE RCAM METHOD Fig 1 illustrates the logic for the RCAM method This figure includes the different stages and steps in the method, and the systematic process for analyzing the system components and their causes of failures The resulting method has been implemented in MATLAB where output from RADPOW is used as input [2]

3 BERTLING et al: RCAM METHOD FOR ASSESSING THE IMPACT OF MAINTENANCE IN POWER DISTRIBUTION SYSTEMS 77 2) Identify critical voltage levels and components for the system reliability based on results from reliability analysis Theapproachforthesensitivityanalysisisasfollows:categorize components according to their type, vary their input failure rates for one type at a time, and evaluate the resulting indices for the system and different load points Perform this analysis for different voltage levels and load points The results provide a prioritized list of components for PM measures Stage 2 Component reliability analysis 3) Identify failure causes by failure modes analysis for each component identified as critical and affected by PM Identify causes of failures from an understanding of: component functions, failure modes and failure events Determine the percentage each cause contributes to the total number of failures from interruption data and expertise Identify experience data for interruptions due to these causes of failures Identify possible effect of alternative PM methods 4) Define a failure rate model For components model the failure rate function as follows: Simply assume that the failure rate equals the average failure interruption,, from reliability input data (from Step 1) Assume that the component failure rate function can be obtained as a sum of contributions from the different causes of failures of type Deduce a model for the failure rate as a function of time, using experience data from Step 2 for the failure rate modeling, as follows: (1) (2) Fig 1 Logic for the RCAM method (the steps that feature the asterisk (*) use RADPOW for reliability analysis) The ten steps needed to perform the RCAM approach, as identified in Fig 1, are presented in more detail in this section Stage 1 System reliability analysis 1) Define reliability model and required input data Define input data including: network data, component reliability data and customer data, and a reliability model 5) Model effect of PM methods on reliability for each failure cause Assume that the PM method, preventing failure cause is applied to component number For each PM method define a failure rate model as follows: Assume that the effect of applying PM is a reduction of the actual failure cause with % reduction, where and, is the percentage contribution to the total failures of that failure cause, and given from Step 3 Assume that the failure rate for the analyzed component is reduced by the same percentage The resulting failure rate function can be evaluated from (3)

4 78 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 20, NO 1, FEBRUARY 2005 Deduce a model for functional relationship between reliability and PM activities as a function of time This model requires more knowledge about the component behavior and the effect of applying PM with method and the impact on specific failure causes The resulting failure rate function can be evaluated from 6) Deduce different plans for applying PM, and evaluate the resulting effect on the component failure rate Note that for Approach II this requires the effect of applying PM at different times on the resulting failure rate functions to be evaluated Stage 3 System reliability and cost/benefit analysis 7) Define and implement different strategies for PM A PM strategy,, for the system is defined by: applied PM methods denoted by: ; proportion of the component type that are affected by each PM method denoted by, and also for Approach II, and within the period ; number of times PM is applied ; at what times PM is applied 8) Estimate the resulting composite failure rate This step implies developing the failure rate model for the component applied with PM strategy The resulting failure rate function provides the input data for component type to the system reliability model Define which failure causes are affected by each PM method in the strategy Let denote the affected causes, and denote the nonaffected causes The resulting failure rate function captures the average composite failure rate characteristic for the component It is made up of several parts, depending on the PM strategy Define the extent of the effect for each failure cause, affected by PM method, that is Evaluate the resulting composite failure rate for component type, which is given as follows: (4) where we also have (6 ), which is shown at the bottom of the page, define the resulting failure rate function 9) Compare system reliability when applying different maintenance methods and PM strategies Perform system reliability analysis with result from Step 8 as input data for included components The output is the system and load-point reliability indices that show the different effects of the PM strategy (S) on the system Compare the impact of PM strategy on system and load-point reliability indices For Approach II, an alternative is to compare the average load-point indices during the period, evaluated as follows: and similarly for each load point,, in the system model Analyze the effect of using different PM strategies on system reliability 10) Identify cost effective PM strategy Evaluate cost functions in [cost/yr], based on those that were introduced in Section II: the cost of failure ; the cost of preventive maintenance ; the cost of interruption with and without PM respectively as follows: where is the cost of failure for component [cost/int] (6) (7) (8) (5) where is the inflation rate (9) (6 )

5 BERTLING et al: RCAM METHOD FOR ASSESSING THE IMPACT OF MAINTENANCE IN POWER DISTRIBUTION SYSTEMS 79 c) Approach I: (10) where is the cost of applying PM method for component [cost/measure] d) Approach II: is shown in (11), at the bottom of the page, where the cost of applying PM, at each PM occasion, is equally spread over the remaining time period (12) where is the customer interruption cost in [cost/kwh] Fig 2 Identifying critical components for the Birka system with cases (1) base case, (2) bus bars, (3) breakers, (4) cables, and (5) transformers (Step 2) Evaluate the total annualized costs in [cost/yr]: Evaluate present values in [cost]: The same value as given by (14) (13) (14) (15) (16) The cost-effective solution is the maintenance strategy that provides the lowest total cost when comparing the total costs for PM with different sets of, and with no PM, that is CM IV RESULTS FROM APPLICATION STUDIES This section provides selected results from application studies of the Birka system including failure rate modeling for the underground cables and with the effect of PM on one failure cause (water-treeing) For each of the results presented in figures the corresponding step in the RCAM method is noted Stage 1 System reliability analysis for the Birka system The disturbance data for the Stockholm city power system (from 220-, 110-, 33-, to 11-kV level) and the period was surveyed [17] The statistics showed that the 11-kV voltage level contributed most to the number of failures and customers affected A system was selected to investigate this voltage level in more detail This system includes the 220/110-kV Bredäng station and 33/11-kV Liljeholmen station, which are connected to each other via two parallel 110-kV cables From the Liljeholmen station (LH11) there are 32 outgoing 11-kV feeders that supply the southern part of central Stockholm and customers In the model, customers are represented as one average 11-kV load point The following component types were included: bus bars, breakers, underground cables, and transformers Furthermore, these were categorized into the different voltage levels between kv The reliability of the Birka system was analyzed using input reliability data from experience and statistics and RADPOW [18] Fig 2 shows results from Step 2 in the RCAM method defining the critical components For each case, a specific component failure rate is assumed to be zero, and the resulting effect on the load point indices is evaluated Case 1 refers to the base case with no PM The most significant reduction occurs in Case (11)

6 80 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 20, NO 1, FEBRUARY 2005 Fig 3 Process to relate underlying failure cause to reliability (Step 3 5) 4, when cables are considered 100% reliable This shows that these have the greatest impact on the failure rate and the unavailability for the average 11-kV customer The significant rise in average outage time is because the repair time for the dominant population of cables, that is 11 kv, is much lower than the repair times for the other components Therefore, the average restoration time increases when the number of short interruptions is reduced The conclusion is that the 11-kV cables are critical components for this system Stage 2 Component reliability modeling A comprehensive failure modes analysis was made (Step 3) using 18 years of data and 58 interruptions that were caused by the 11 kv underground cables The underlying causes of failures for each of these interruptions were investigated The class of material or method made the most significant contribution with 59% of the total failures, including the underlying failure causes of material faults 1) Approach I: The information from the failure modes analysis provides input data for the failure rate modeling (Step 4) 2) Approach II: Data from the statistics (Step 3) were complemented with practical experience From discussions with maintenance personnel a list of underlying causes of cable faults was defined One of these causes was water treeing This is a tree-like phenomenon that involves water penetration through the insulation, occurring primarily in the early produced (mid-1970s) XLPE insulation cables Data related to this failure were collected and selected These include disturbance statistics [19], measurements and modeling of the cable condition [20], and PM of cables [21] One effective method for preventing failures of water-treed cables is the rehabilitation method [21], [22] This involves injecting a silicon-based liquid between the individual wires of the conductor, which stops the growth of the current water trees The water trees, on the other hand, impact on the breakdown strength of the cable, which can be measured with diagnostic methods Based on the experience data and the logic shown in Fig 3, a failure rate model (Step 4) and a functional relationship between the failure rate and the effect of PM measures (Step 5) were defined [2] Three different maintenance activities were considered for these studies: no PM activities, PM by the rehabilitation method and PM by replacing cables systematically before they failed (the replacement method) with notations: org, si, and rp, respectively Fig 4 shows the final result for modeling the failure rate, assuming one PM action on each cable The initial value for the cable failure rate is relatively small but not zero, as the figure indicates The failure rate characteristic with no PM is the resulting approximation of a function obtained from experience data [2] The data is assessed from a complete population of cables over a 13-year aging period It was assumed that the failure rate, after this time and due to this specific failure cause, is constant Furthermore, it was assumed that replacement is made Fig 4 Resulting failure rate model for a water-treed cable affected by PM measures after 11 years (Steps 4 5, Approach II) with a cable having the same characteristics as the current cable had when new These assumptions were motivated by two aspects: that the water trees grow to a maximum length (that of the insulation thickness) and that this provides a worst-case scenario when showing the benefit of PM However, it should be noted that for these XLPE insulated cables, a new cable would not have the same characteristics due to changes in the manufacturing techniques Nevertheless, a changed characteristic can be included quite readily In practice, PM procedures are likely to be performed several times during the lifetime of a particular component, in which case the characteristic shown in Fig 4 would have a series of decrements similar to that shown The number of occasions and their timing should depend on the cost of performing the PM actions and the cost-benefit of doing so The RCAM approach described in this paper allows this to be assessed objectively The resulting cable failure rate model was used for the Birka system The characteristics of the XLPE cables in this system are consequently assumed to follow those of the XLPE cables with insulation degradation due to water treeing (It should be stressed that this assumption enabled complete demonstration of the RCAM method, rather than providing a true picture of the cables in the Birka system) To obtain the composite failure rate for the cable, it was assumed that the total failure causes were due to water trees and other causes The resulting input data for the component then consisted of the developed failure rate model for failures due to water trees, and the average failure rate for the 11-kV cable in the Birka system due to other causes (Step 6) Stage 3 System Reliability and Cost/Benefit Analysis: 1) Approach I: Results from the survey of statistics provided input data for modeling the relationship between PM and reliability using Approach I Sensitivity studies were made to see the effect at the system level if each of these causes of failures were decreased individually or in combination The different cases are as follows: 1) base case; 2) %;

7 BERTLING et al: RCAM METHOD FOR ASSESSING THE IMPACT OF MAINTENANCE IN POWER DISTRIBUTION SYSTEMS 81 Fig 5 Effect on system reliability for different maintenance strategies using Approach I for the Birka system (Step 9) Fig 6 Impact of maintenance methods and PM strategies on cost of failure for the Birka system (Step 10, Approach II) TABLE I RELIABILITY RESULTS APPLYING DIFFERENT MAINTENANCE METHODS 3) %; 4) %; 5) total of %; 6) total for % The difference in percentages between cases 5 and 6 (25%) relates to those causes that were reported as included in material and method, but with no further detailed level of classification Fig 5 shows the benefit of these different cases on the system indices It has been assumed for each case that the causes of failures can be eliminated by the PM activities Thus the corresponding failures would be eliminated and the reliability indices influenced The results show that PM measures to reduce individual causes of failures for a critical component in the system can significantly improve the system reliability The cases represent different maintenance strategies for the RCAM method with Approach I (Step 7) 2) Approach II: A system analysis is performed for the Birka system including two strategies for applying the PM with either rehabilitation or replacement Both of these involve PM applied on three occasions (years ), and with the following proportions of cables subject to PM per occasion: 10% for and 30% for (Step 7) The results from the system reliability analysis, as shown in Table I (Step 9), show consistently that the best reliability is achieved with PM by replacement and with as much as possible of the component replaced, that is Fig 7 Impact of different maintenance methods on the total annual costs of applying a PM strategy for the Birka system Results are shown for the case with the interest rate d =2% (Step 10, Approach II) Fig 6 shows one result from the economic evaluation according to the RCAM method Input data for the economic assessment was provided by the utility, and from the Swedish customer interruption costs included in [23] It is seen that the cost of failures is decreased for the Birka system, when the 11-kV cables are affected by PM measures Furthermore, it is seen that the most significant decrease in cost of failures is achieved with the replacement method The final step in the RCAM analysis is to evaluate the present worth values of the annualised total costs of maintenance Fig 7 presents annual costs for the different maintenance methods using PM strategy S1 It can be seen directly from the annual costs that PM is a dominating cost Furthermore, it is clearly more cost-effective to rehabilitate the cable than to replace it, since the greater benefit in reliability by the replacement method is offset by the higher investment cost Consequently, the cost-effective solution is not to carry out PM in this case, but if PM is carried out, rehabilitation is better than replacement

8 82 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 20, NO 1, FEBRUARY 2005 This is, however, a constructed example considering only one type of component and does not provide the complete result for the Birka system It is also important to note that cables compared with other components in a power system involve extremely high PM costs with relatively few possible PM actions It is, however, of significant importance for efficient maintenance planning to evaluate the relative values of implementing different maintenance strategies, as shown in this application example V CONCLUSION A RCAM has been presented which includes establishing a quantitative relationship between system reliability and maintenance effort Results from application studies show how the RCAM method can be used to compare different maintenance methods and PM strategies based on the total cost of maintenance, which includes the impact of the PM measure on the system reliability Furthermore, the application study shows that the RCAM method can be performed and supported by real input data Relating maintenance effort and reliability improvement is, however, a complex problem, and substantial input data is required to support the method, which may need significant updates of relevant data bases ACKNOWLEDGMENT The authors express their gratitude to those people who made the application studies possible, particularly to staff at Fortum Distribution and Fortum Service involved in the Birka system case study A special thanks to Dr J Endrenyi for contributions during final discussions The financial support from the Competence Center in Electric Power Engineering at KTH is gratefully acknowledged, as well as the input from the associated reference group REFERENCES [1] R Billinton and R N Allan, Reliability Evaluation of Power Systems, 2nd ed New York: Plenum, 1996 [2] L Bertling, Reliability centred maintenance for electric power distribution systems, PhD dissertation, Dept Elect Power Engineering, KTH, Stockholm, Sweden, 2002 [3] R E Brown, Electric Power Distribution Reliability New York: Marcel Dekker, 2002 [4] R Billinton, M Fotuhi-Firuzabad, and L Bertling, Bibliography on the application of probability methods in power system reliability evaluation , IEEE Trans Power Syst, vol 16, no 4, pp , Nov 2001 [5] G H Kjølle, A T Holen, K Samdal, and G Solum, Adequate interruption cost assessment in a quality based regulation regime, in Proc IEEE PowerTech 01, vol 3, Porto, Portugal, Sep 2001 [6] Swedish National Energy Administration, Nätnyttomodellen, Distribution System Utility Effectivity Model, News Report, in Swedish [7] OFGEM, Report on Services for Electricity Customers, Office of Gas and Electricity Markets, published annually [8] C Singh, M Schwan, and W H Wellssow, Reliability in liberalized electric power markets From analysis to risk management, in Proc 14th PSCC, Sevilla, Spain, Jun 2002 [9] J Endrenyi et al, The present status of maintenance strategies and the impact of maintenance on reliability, IEEE Trans Power Syst, vol 16, no 4, pp , Nov 2001 [10] R Billinton, Bibliography on the application of probability methods in power system reliability evaluation, IEEE Trans Power App Syst, vol PAS-91, Mar/Apr 1972 [11] F S Nowlan and H F Heap, Reliability-Centered Maintenance Springfield, VA: National Technical Information Service, US Dept of Commerce, 1978 [12] A M Smith, Reliability-Centered Maintenance New York: McGraw- Hill, 1993 [13] J Moubray, Reliability-centred Maintenance Oxford, UK: Butterworth-Heinemann, 1995 [14] A B Swedenergy, RCM for electrical distribution systems A simplified decision model for maintenance planning part I, in RCM För Elnät Enförenklad beslutsmetod för underhållsplanering Del 1 Användningsområden och arbetssätt, 2001 [15] International Council on Large Electric Systems, Life Management of Circuit-Breakers, Cigré Working Group 1308, Paris, France, Report 165, 2000 [16] K K Kariuki and R N Allan, Application of customer outage costs in system planning, design and operation, Proc Inst Elect Eng Generation, Transmission, and Distribution, vol 143, no 2, Mar 1996 [17] L Bertling, R Eriksson, R N Allan, L Å Gustafsson, and M Åhlén, Survey of causes of failures based on statistics and practice for improvements of preventive maintenance plans, in Proc 14th PSCC, Sevilla, Spain, Jun 2002 [18] L Bertling, R Eriksson, and R N Allan, Relation between preventive maintenance and reliability for a cost- effective distribution systems, in Proc IEEE PowerTech 01, vol 4, Sep 2001 [19] A B Swedenergy, The Lifetime and Usefulness of XLPE Cables, 1990 PEX-kablar livslängd och användbarhet, Swedish [20] P Werelius, P Thärning, R Eriksson, B Holmgren, and U Gäfvert, Dielectric spectroscopy for diagnostics of water tree deteriorated XLPE cables, IEEE Trans Dielect Elect Insulation, vol 8, no 1, Feb 2001 [21] H Faremo, Report: Rehabilitation of XLPE Cables with long Watertrees, SINTEF, Trondheim, Norway, 1997 Energiforsyningens Forskningsinstitutt (EFI), EFI TR A 4512, Norwegian [22] J Pilling and G Bertini, Incorporating cablecure injection into a cost-effective reliability program, IEEE Ind Appl Mag,, vol 3333, no , Sep/Oct 2000 [23] Methods to Consider Customer Interruption Costs in Power System Analysis Paris, France: Cigré Task Force , 2001 Lina Bertling (S 98-M 02) received the PhD degree in electric power systems in 2002 from the Department of Electrical Engineering and the MSc degree in systems engineering in 1997, both at the Royal Institute of Technology (KTH), Stockholm, Sweden She is currently a visiting postdoctoral student at the University of Toronto, Toronto, ON, Canada, associated with Kinectrics Inc She is also engaged at KTH as Research Associate and Project Leader of the research program on asset management in power systems Her research interests are in reliability evaluation of power systems and development of methods for maintenance optimization Ron Allan (F 88) is an Emiritus Professor of Electrical Energy Systems at the University of Manchester Institite of Science and Technology, Manchester, UK He was previously a Visiting Professor at the Royal Institute of Technology (KTH), Stockholm, Sweden (during the time these studies were done) His research interests include power system reliability and customer outage costs, on which he has published numerous papers and books Roland Eriksson (SM 89) received the MSc and PhD degrees in electrical engineering from the Royal Institute of Technology (KTH), Stockholm,Sweden, in 1969 and 1975, respectively Since 1988, he has been a Professor in the Department of Electrical Engineering, KTH His research interests include condition-based maintenance and electrical insulation diagnostics