Reliability Centered Asset Mgmet for Power System

IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 20, NO. 1, FEBRUARY 2005 75

A Reliability-Centered Asset Maintenance Methodfor Assessing the Impact of Maintenance in

Power Distribution SystemsLina Bertling, Member, IEEE, Ron Allan, Fellow, IEEE, and Roland Eriksson, Senior Member, IEEE

Abstract—This paper proposes a method for comparing theeffect of different maintenance strategies on system reliability andcost. This method relates reliability theory with the experiencegained from statistics and practical knowledge of component fail-ures and maintenance measures. The approach has been appliedto rural and urban distribution systems. In particular, a functionalrelationship between failure rate and maintenance measures hasbeen developed for a cable component. The results show the valueof using a systematic quantitative approach for investigating theeffect of different maintenance strategies.

Index Terms—Asset management, electric power distributionsystem, maintenance strategy, reliability evaluation, relia-bility-centered asset maintenance (RCAM).

I. INTRODUCTION

E LECTRIC power distribution systems constitute thegreatest risk to the interruption of power supply [1]–[3].

Traditionally, however, distribution systems have received lessattention than generation and transmission, evidenced by thedifference in the number of publications [4]. However, focus ismoving toward distribution as the business focus changes fromconsumers to customers.

Deregulation of the power system market has led to a shiftfrom technical to economic driving factors. The utilities thatown and operate the power distribution systems now facevarious market requirements. On the one hand, customers arepaying for a service (delivered energy) and the authorities areimposing regulation, supervision, and compensation dependingon the degree to which contractual and other obligations arefulfilled, see for example Norway [5], Sweden [6], and the U.K.[7]. On the other hand, utilities must ensure that their expendi-ture is cost-effective. This means that electricity utilities mustsatisfy quantitative reliability requirements while at the sametime minimizing their costs.

One predominant expense for a utility is the cost of main-taining system assets, for example through adopting preventivemeasures, collectively called preventive maintenance (PM). PM

Manuscript received June 29, 2004. This work was supported by the Com-petence Center in Electric Power Engineering at the Royal University of Tech-nology (KTH). Paper no. TPWRS-00271-2003.

L. Bertling and R. Eriksson are with the Electrical Engineering Department,Royal Institute Technology (KTH), 100 44 Stockholm, Sweden (e-mail:[email protected]; [email protected]).

R. Allan is with the Electrical Engineering Department, Manchester Centrefor Electrical Energy, University of Manchester Institute of Science and Tech-nology (UMIST), Manchester, U.K. (e-mail: [email protected]).

Digital Object Identifier 10.1109/TPWRS.2004.840433

measures can impact on reliability by either improving the con-dition, or prolonging the lifetime of an asset. Reliability overallcan be improved by lowering either the frequency or the du-ration of interruptions. PM activities could impact on the fre-quency by preventing the actual cause of the failure. Conse-quently, PM is cost-effective when the reliability benefit out-weighs the cost of implementing the PM measure. There is,therefore, a need for utilities to incorporate systematic methodswhich relate maintenance of system assets to the improvementin system reliability. This is part of the wider concept of assetmanagement. Asset management involves making decisions toallow the network business to maximize long term profits, whiledelivering high service levels to the customers with acceptableand manageable risks.

Reliability evaluation and maintenance planning techniqueshave 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 componentmaintenance. Furthermore, the available techniques are notgenerally put into practice. The reason for this, according withthe authors, is the lack of suitable input data and a reluctanceto use theoretical tools to address the practical problem ofmaintenance planning.

One method for relating reliability to PM is known as relia-bility-centered maintenance (RCM). RCM is a qualitative sys-tematic approach to organizing maintenance [11]–[13]. It origi-nated in the civil aircraft industry in the 1960s with the introduc-tion of the Boeing 747 series, and the need to lower PM costsin attaining a certain level of reliability. The results were suc-cessful and the methodology was developed further. In 1975,the U.S. Department of Commerce defined the concept RCMand declared that it should be used in all major military sys-tems [11]. In the 1980s, the Electric Power Research Institute(EPRI) introduced RCM into the nuclear power industry. TodayRCM is used or being considered by an increasing number ofelectrical utilities [14], [15]. The main feature of RCM is itsfocus on preserving system function where critical componentsfor system reliability are prioritized for PM measures. However,the method is generally not capable of showing the benefits ofmaintenance for system reliability and costs.

This paper proposes a reliability-centered asset maintenance(RCAM) method, which provides a quantitative relationshipbetween PM of assets and the total maintenance cost [2].The method is developed from RCM principles attempting torelate more closely the impact of maintenance to the cost andreliability of the system. The method has been developed from

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76 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 20, NO. 1, FEBRUARY 2005

comprehensive application studies for real power distributionsystems. Application studies have been made on two differentdistribution systems in Sweden: a rural system of overheadpower lines in southern Sweden, and an urban undergroundcable system in central Stockholm, the Birka System. Bothstudies used data for the systems in question, and were done inclose co-operation with the operating utilities (Sydkraft AB andFortum 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 electricpower distribution systems. These events occur randomly andtherefore models based on probability theory have been used.

A computer code RADPOW (reliability assessment of elec-trical distribution systems), based on the analytical approach,has been developed within the Competence Centre of ElectricalEngineering at KTH [2]. A network modeling technique and theminimal cut set (load-point-driven) approach [1] is used to de-duce the failure modes. RADPOW evaluates the load point in-dices, 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 forthe system reliability are identified from a sensitivity analysis.These components are further studied, focusing on the impactof maintenance measures. The relationship between reliabilityand maintenance has been established by relating the effect ofPM to the causes of failures for the component being assessed.Two different approaches have been used. The first approachassumes a constant reduction ratio between failure rates and theeffect of PM, whereas the second approach assumes this ratio tobe dependent on time. In the first case, depends only onthe effect of PM (Approach I). In the second case, isalso time-dependent (Approach II), and the failure rate reductionis a consequence of the PM actions considered for the specificcomponent that is studied.

Formulating the failure rate model for Approach II is a com-plicated task. This has presently been done for one componenttype, underground cables, which was shown to be critical for thereliability of one of the systems used in these studies. The de-tails of the underlying theory are too extensive to be developedin this paper, so only the overall principles, results and applica-tions are included.

The main stages of the RCAM approach are as follows.

Stage 1 System reliability analysis: defines the system andevaluates critical components affecting system reli-ability.

Stage 2 Component reliability modeling: analyzes the com-ponents in detail and, with the support of appropriate

input data, defines the quantitative relationship be-tween reliability and PM measures.

Stage 3 System reliability and cost/benefit analysis: puts theresults of Stage 2 into a system perspective, andevaluates the effect of component maintenance onsystem reliability and the impact on cost of differentPM strategies.

These three stages emphasize a central feature of the method:that the analysis moves from the system level to the componentlevel and back to the system level.

B. Economic Evaluation

The economic evaluation brings the RCAM analysis to itsfinal step: to relate the benefits in costs due to the impact ofmaintenance on reliability. The motivation for any PM strategyis that the cost of applying the PM measure should be less thantaking no action at all. If little or no PM is done, then moresystem failures are likely to occur resulting in more repair ac-tions being required, i.e., in more corrective maintenance (CM)actions. Therefore, the important issue is to compare the costsassociated with different maintenance methods, including bothPM and CM with the objective of minimizing the total cost ofmaintenance.

There are several costs that can be related to the effect ofsystem failures. Two direct utility costs are: 1) cost of failure(CM), e.g., repair costs and losses in revenue due to nondeliv-ered energy and 2) cost of the PM actions, e.g., planned main-tenance 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 suffersupply unavailability and may suffer direct costs and/or be com-pensated via a penalty payment. Consequently, the proposedcost analysis considers:

• the cost of failure ;• the cost of preventive maintenance ;• the cost of interruption .

The optimal maintenance method and PM strategy is the solu-tion that minimizes the sum of these three costs. However, insome cases it may not be necessary to include , for examplefor a simple or first-order comparison of strategies.

The economic evaluations have been made using fundamentaltechniques. The costs are evaluated on an annual basis with anassumed increase due to inflation . Furthermore, the invest-ments in PM measures are spread over the remaining time ofthe assessment period . Finally, the present worth value of thetotal annualized costs is evaluated. The present worth value ofone 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 figureincludes the different stages and steps in the method, and thesystematic process for analyzing the system components andtheir causes of failures. The resulting method has been imple-mented in MATLAB where output from RADPOW is used asinput [2].

BERTLING et al.: RCAM METHOD FOR ASSESSING THE IMPACT OF MAINTENANCE IN POWER DISTRIBUTION SYSTEMS 77

Fig. 1. Logic for the RCAM method (the steps that feature the asterisk (*) useRADPOW for reliability analysis).

The ten steps needed to perform the RCAM approach, asidentified 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 re-

liability data and customer data, and a reliability model.

2) Identify critical voltage levels and components for thesystem reliability based on results from reliability analysis.

Theapproachforthesensitivityanalysisisasfollows:catego-rizecomponentsaccording to their type,vary their input failurerates for one type at a time, and evaluate the resulting indicesfor the system and different load points. Perform this analysisfor different voltage levels and load points. The results providea prioritized list of components for PM measures.

Stage 2—Component reliability analysis.3) Identify failure causes by failure modes analysis for eachcomponent 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 tothe total number of failures from interruption data andexpertise.

• Identify experience data for interruptions due to thesecauses of failures.

• Identify possible effect of alternative PM methods.4) Define a failure rate model.

For components model the failure rate func-tion as follows:

a) Approach I:Simply assume that the failure rate equals the av-

erage failure interruption, , from reliability inputdata (from Step 1)

(1)

b) Approach II:Assume that the component failure rate function can

be obtained as a sum of contributions from the differentcauses of failures of type . Deduce amodel for the failure rate as a function of time, usingexperience data from Step 2 for the failure rate mod-eling, as follows:

(2)

5) Model effect of PM methods on reliability for each failurecause.

Assume that the PM method , preventingfailure cause is applied to component number . For eachPM method define a failure rate model as follows:

a) Approach I:• Assume that the effect of applying PM is a re-

duction of the actual failure cause with %reduction, where and , is the per-centage contribution to the total failures of thatfailure cause, and given from Step 3.

• Assume that the failure rate for the analyzedcomponent is reduced by the same percentage.The resulting failure rate function can be evalu-ated from

(3)


b) Approach II:• Deduce a model for functional relationship be-

tween reliability and PM activities as a functionof time. This model requires more knowledgeabout the component behavior and the effect ofapplying PM with method and the impact onspecific failure causes.

• The resulting failure rate function can be evalu-ated from

(4)

6) Deduce different plans for applying PM, and evaluate theresulting effect on the component failure rate.

Note that for Approach II this requires the effect of ap-plying PM at different times on the resulting failure rate func-tions 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 ApproachII, 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 resultingfailure rate function provides the input data for componenttype to the system reliability model.

• Define which failure causes are affected by each PMmethod in the strategy. Let denote the affectedcauses, and denote the nonaffected causes.

• The resulting failure rate function captures the averagecomposite failure rate characteristic for the component. It is made up of several parts, depending on the PM

strategy.

a) Approach I:• 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:

(5)

b) Approach II:

......

(6)

where we also have (6 ), which is shown at the bottomof the page, define the resulting failure rate function.

9) Compare system reliability when applying different main-tenance methods and PM strategies.

• Perform system reliability analysis with result fromStep 8 as input data for included components. Theoutput is the system and load-point reliability indicesthat show the different effects of the PM strategy (S)on the system.

• Compare the impact of PM strategy on system andload-point reliability indices.

• For Approach II, an alternative is to compare the av-erage load-point indices during the period, evaluated asfollows:

(7)

and similarly for each loadpoint, , in the system model.

• Analyze the effect of using different PM strategies onsystem 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:a) Approach I:

(8)

where is the cost of failure for component[cost/int].

b) Approach II:

(9)

where is the inflation rate.

...

(6 )


c) Approach I:

(10)

where is the cost of applying PM method forcomponent [cost/measure].

d) Approach II: is shown in (11), at thebottom of the page, where the cost of applying PM, ateach PM occasion, is equally spread over the remainingtime period.

a) Approach I:

(12)

where is the customer interruption cost in[cost/kWh].

b) Approach II:

(13)

• Evaluate the total annualized costs in [cost/yr]:a) Approach I:

(14)

b) Approach II:

(15)

• Evaluate present values in [cost]:a) Approach I:

The same value as given by (14).b) Approach II:

(16)

The cost-effective solution is the maintenancestrategy that provides the lowest total cost whencomparing the total costs for PM with differentsets of , and with no PM, that is CM.

Fig. 2. Identifying critical components for the Birka system with cases (1) basecase, (2) bus bars, (3) breakers, (4) cables, and (5) transformers (Step 2).

IV. RESULTS FROM APPLICATION STUDIES

This section provides selected results from applicationstudies of the Birka system including failure rate modelingfor the underground cables and with the effect of PM on onefailure cause (water-treeing). For each of the results presentedin figures the corresponding step in the RCAM method is noted.

Stage 1—System reliability analysis for the Birka systemThe disturbance data for the Stockholm city power system

(from 220-, 110-, 33-, to 11-kV level) and the period 1982–1999was surveyed [17]. The statistics showed that the 11-kV voltagelevel contributed most to the number of failures and customersaffected. A system was selected to investigate this voltage levelin more detail. This system includes the 220/110-kV Bredängstation and 33/11-kV Liljeholmen station, which are connectedto each other via two parallel 110-kV cables. From the Lil-jeholmen station (LH11) there are 32 outgoing 11-kV feedersthat supply the southern part of central Stockholm and 14 300customers. In the model, customers are represented as one av-erage 11-kV load point. The following component types wereincluded: bus bars, breakers, underground cables, and trans-formers. Furthermore, these were categorized into the differentvoltage levels between 220–11 kV.

The reliability of the Birka system was analyzed using inputreliability data from experience and statistics and RADPOW[18]. Fig. 2 shows results from Step 2 in the RCAM methoddefining the critical components. For each case, a specific com-ponent failure rate is assumed to be zero, and the resulting effecton the load point indices is evaluated. Case 1 refers to the basecase with no PM. The most significant reduction occurs in Case

......

(11)


Fig. 3. Process to relate underlying failure cause to reliability. (Step 3–5).

4, when cables are considered 100% reliable. This shows thatthese have the greatest impact on the failure rate and the un-availability for the average 11-kV customer. The significant risein average outage time is because the repair time for the domi-nant population of cables, that is 11 kV, is much lower than therepair times for the other components. Therefore, the averagerestoration time increases when the number of short interrup-tions is reduced. The conclusion is that the 11-kV cables arecritical 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 bythe 11–kV underground cables. The underlying causes of fail-ures for each of these interruptions were investigated. The classof material or method made the most significant contributionwith 59% of the total failures, including the underlying failurecauses 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 withpractical experience. From discussions with maintenance per-sonnel a list of underlying causes of cable faults was defined.One of these causes was water treeing. This is a tree-like phe-nomenon that involves water penetration through the insula-tion, occurring primarily in the early produced (mid-1970s)XLPE insulation cables. Data related to this failure were col-lected and selected. These include disturbance statistics [19],measurements and modeling of the cable condition [20], andPM of cables [21]. One effective method for preventing fail-ures of water-treed cables is the rehabilitation method [21],[22]. This involves injecting a silicon-based liquid betweenthe individual wires of the conductor, which stops the growthof the current water trees. The water trees, on the other hand,impact on the breakdown strength of the cable, which can bemeasured with diagnostic methods. Based on the experiencedata and the logic shown in Fig. 3, a failure rate model (Step4) and a functional relationship between the failure rate andthe effect of PM measures (Step 5) were defined [2].Three different maintenance activities were considered

for these studies: no PM activities, PM by the rehabilitationmethod and PM by replacing cables systematically before theyfailed (the replacement method) with notations: org, si, and rp,respectively.

Fig. 4 shows the final result for modeling the failure rate, as-suming one PM action on each cable. The initial value for thecable failure rate is relatively small but not zero, as the figureindicates. The failure rate characteristic with no PM is the re-sulting approximation of a function obtained from experiencedata [2]. The data is assessed from a complete population of ca-bles over a 13-year aging period. It was assumed that the failurerate, after this time and due to this specific failure cause, is con-stant. Furthermore, it was assumed that replacement is made

Fig. 4. Resulting failure rate model for a water-treed cable affected by PMmeasures after 11 years (Steps 4–5, Approach II).

with a cable having the same characteristics as the current cablehad when new. These assumptions were motivated by two as-pects: that the water trees grow to a maximum length (that ofthe insulation thickness) and that this provides a worst-case sce-nario when showing the benefit of PM. However, it should benoted that for these XLPE insulated cables, a new cable wouldnot have the same characteristics due to changes in the manu-facturing techniques. Nevertheless, a changed characteristic canbe included quite readily.

In practice, PM procedures are likely to be performed severaltimes during the lifetime of a particular component, in whichcase the characteristic shown in Fig. 4 would have a series ofdecrements similar to that shown. The number of occasions andtheir timing should depend on the cost of performing the PMactions and the cost-benefit of doing so. The RCAM approachdescribed in this paper allows this to be assessed objectively.

The resulting cable failure rate model was used for the Birkasystem. The characteristics of the XLPE cables in this systemare consequently assumed to follow those of the XLPE cableswith insulation degradation due to water treeing. (It should bestressed that this assumption enabled complete demonstrationof the RCAM method, rather than providing a true picture ofthe cables in the Birka system.) To obtain the composite failurerate for the cable, it was assumed that the total failure causeswere due to water trees and other causes. The resulting inputdata for the component then consisted of the developed failurerate model for failures due to water trees, and the average failurerate 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 formodeling the relationship between PM and reliability usingApproach I. Sensitivity studies were made to see the effectat the system level if each of these causes of failures weredecreased individually or in combination. The different casesare as follows:

1) base case;2) %;


Fig. 5. Effect on system reliability for different maintenance strategies usingApproach I for the Birka system (Step 9).

TABLE IRELIABILITY 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 ma-terial and method, but with no further detailed level of clas-sification. Fig. 5 shows the benefit of these different cases onthe system indices. It has been assumed for each case that thecauses of failures can be eliminated by the PM activities. Thusthe corresponding failures would be eliminated and the relia-bility indices influenced. The results show that PM measuresto reduce individual causes of failures for a critical compo-nent in the system can significantly improve the system reli-ability. The cases represent different maintenance strategiesfor the RCAM method with Approach I (Step 7).2) Approach II:

A system analysis is performed for the Birka system in-cluding two strategies for applying the PM with either reha-bilitation or replacement . Both of these in-volve PM applied on three occasions (years ),and with the following proportions of cables subject to PMper occasion: 10% for and 30% for (Step 7). The re-sults from the system reliability analysis, as shown in Table I(Step 9), show consistently that the best reliability is achievedwith PM by replacement and with as much as possible of thecomponent replaced, that is .

Fig. 6. Impact of maintenance methods and PM strategies on cost of failurefor the Birka system (Step 10, Approach II).

Fig. 7. Impact of different maintenance methods on the total annual costs ofapplying a PM strategy for the Birka system. Results are shown for the case withthe interest rate d = 2% (Step 10, Approach II).

Fig. 6 shows one result from the economic evaluation ac-cording to the RCAM method. Input data for the economic as-sessment was provided by the utility, and from the Swedish cus-tomer interruption costs included in [23]. It is seen that the costof failures is decreased for the Birka system, when the 11-kVcables are affected by PM measures. Furthermore, it is seen thatthe most significant decrease in cost of failures is achieved withthe replacement method.

The final step in the RCAM analysis is to evaluate the presentworth values of the annualised total costs of maintenance. Fig. 7presents annual costs for the different maintenance methodsusing PM strategy S1. It can be seen directly from the annualcosts that PM is a dominating cost. Furthermore, it is clearlymore cost-effective to rehabilitate the cable than to replaceit, since the greater benefit in reliability by the replacementmethod is offset by the higher investment cost. Consequently,the cost-effective solution is not to carry out PM in this case, butif PM is carried out, rehabilitation is better than replacement.


This is, however, a constructed example considering only onetype of component and does not provide the complete resultfor the Birka system. It is also important to note that cablescompared with other components in a power system involveextremely high PM costs with relatively few possible PMactions. It is, however, of significant importance for efficientmaintenance planning to evaluate the relative values of im-plementing different maintenance strategies, as shown in thisapplication example.

V. CONCLUSION

A RCAM has been presented which includes establishing aquantitative relationship between system reliability and main-tenance effort. Results from application studies show how theRCAM method can be used to compare different maintenancemethods and PM strategies based on the total cost of mainte-nance, which includes the impact of the PM measure on thesystem reliability. Furthermore, the application study shows thatthe RCAM method can be performed and supported by realinput data. Relating maintenance effort and reliability improve-ment is, however, a complex problem, and substantial input datais required to support the method, which may need significantupdates of relevant data bases.

ACKNOWLEDGMENT

The authors express their gratitude to those people who madethe application studies possible, particularly to staff at FortumDistribution and Fortum Service involved in the Birka systemcase study. A special thanks to Dr. J. Endrenyi for contributionsduring final discussions. The financial support from the Compe-tence Center in Electric Power Engineering at KTH is gratefullyacknowledged, as well as the input from the associated referencegroup.

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[9] J. Endrenyi et al., “The present status of maintenance strategies and theimpact of maintenance on reliability,” IEEE Trans. Power Syst., vol. 16,no. 4, pp. 638–646, Nov. 2001.

[10] R. Billinton, “Bibliography on the application of probability methods inpower system reliability evaluation,” IEEE Trans. Power App. Syst., vol.PAS-91, Mar./Apr. 1972.

[11] F. S. Nowlan and H. F. Heap, Reliability-Centered Mainte-nance. Springfield, VA: National Technical Information Service,U.S. Dept. of Commerce, 1978.

[12] A. M. Smith, Reliability-Centered Maintenance. New York: McGraw-Hill, 1993.

[13] J. Moubray, Reliability-centred Maintenance. Oxford, U.K.: Butter-worth-Heinemann, 1995.

[14] A. B. Swedenergy, RCM for electrical distribution systems—A sim-plified decision model for maintenance planning part I, in RCM FörElnät En förenklad beslutsmetod för underhållsplanering—Del 1 An-vändningsområden och arbetssätt, 2001.

[15] International Council on Large Electric Systems, “Life Management ofCircuit-Breakers,” Cigré Working Group 13.08, Paris, France, Report165, 2000.

[16] K. K. Kariuki and R. N. Allan, “Application of customer outage costs insystem planning, design and operation,” Proc. Inst. Elect. Eng. Genera-tion, 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 improve-ments of preventive maintenance plans,” in Proc. 14th PSCC, Sevilla,Spain, Jun. 2002.

[18] L. Bertling, R. Eriksson, and R. N. Allan, “Relation between preventivemaintenance and reliability for a cost- effective distribution systems,” inProc. 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 XLPEcables,” IEEE Trans. Dielect. Elect. Insulation, vol. 8, no. 1, Feb. 2001.

[21] H. Faremo, “Report: Rehabilitation of XLPE Cables with long Water-trees,” SINTEF, Trondheim, Norway, 1997. Energiforsyningens Forskn-ingsinstitutt (EFI), EFI TR A 4512, Norwegian.

[22] J. Pilling and G. Bertini, “Incorporating cablecure injection into acost-effective reliability program,” IEEE Ind. Appl. Mag,, vol. 3333,no. 208 333, Sep./Oct. 2000.

[23] Methods to Consider Customer Interruption Costs in Power SystemAnalysis. Paris, France: Cigré Task Force 38-06-01, 2001.

Lina Bertling (S’98-M’02) received the Ph.D. degree in electric power systemsin 2002 from the Department of Electrical Engineering and the M.Sc. degree insystems 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 atKTH as Research Associate and Project Leader of the research program on assetmanagement in power systems. Her research interests are in reliability evalua-tion of power systems and development of methods for maintenance optimiza-tion.

Ron Allan (F’88) is an Emiritus Professor of Electrical Energy Systems at theUniversity of Manchester Institite of Science and Technology, Manchester, U.K.He was previously a Visiting Professor at the Royal Institute of Technology(KTH), Stockholm, Sweden (during the time these studies were done). His re-search interests include power system reliability and customer outage costs, onwhich he has published numerous papers and books.

Roland Eriksson (SM’89) received the M.Sc. and Ph.D. degrees in electricalengineering 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 Engi-neering, KTH. His research interests include condition-based maintenance andelectrical insulation diagnostics.

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Reliability Centered Asset Mgmet for Power System