IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 4, JULY 2014 1839

Overhead Line Preventive MaintenanceStrategy Based on Condition Monitoringand System Reliability Assessment

Dabo Zhang, Wenyuan Li, Fellow, IEEE, and Xiaofu Xiong

Abstract—This paper presents a maintenance strategy for over-head lines based on condition monitoring and system reliability as-sessment. A quantitative relationship between the condition mon-itoring data and failure rate of overhead lines is established. Thesystem reliability is evaluated usingmonitoring data; the reliabilitybenefit due to the maintenance of each line is quantified; and themaintenance ranking order is created using the benefit/cost ratioindex. The proposed strategy can provide the largest reliabilityimprovement at the lowest maintenance cost and within budget.Case studies were performed on a 182-bus, 5474-MW transmissionsystem located in southwest China.

Index Terms—Condition-based maintenance, overhead lines, re-liability assessment, reliability-centered maintenance.

I. INTRODUCTION

S MART Grid is the next generation of electric powersystems. One of the key technologies in smart grid is

to achieve advanced asset management (AAM) [1], whichcan enhance asset utilization efficiency and improve systemreliability. Electrical equipment maintenance is one of the mostimportant tasks in asset management. Scheduling of main-tenance will greatly affect reliability and economy of powersystem.With the development of computers, sensors and other ad-

vanced monitoring technologies, conditions of overhead linescan be consistently monitored and condition-based maintenance(CBM) can be implemented [2]. An overhead line includestower foundations, tower structures, conductors, insulators, fit-tings, grounding devices, auxiliary facilities and surroundings.The condition of each item will impact the overall condition

Manuscript received July 19, 2013; revised November 16, 2013 andDecember 14, 2013; accepted December 15, 2013. Date of publicationJanuary 09, 2014; date of current version June 16, 2014. This workwas supported in part by the Scientific Research Foundation of the StateKey Laboratory of Power Transmission Equipment & System Securityand New Technology in China (Project No. 2007DA10512710201,No.2007DA10512709202). Paper no. TPWRS-00941-2013.D. Zhang and X. Xiong are with the State Key Laboratory of Power Trans-

mission Equipment & System Security and New Technology, Chongqing Uni-versity, Chongqing, China (e-mail: zhangdb2004@163.com; cquxxf@vip.sina.com).W. Li is with the State Key Laboratory of Power Transmission Equipment

& System Security and New Technology, Chongqing University, Chongqing,China; with BC Hydro, Vancouver, Canada; and with Simon Fraser University,Vancouver, Canada (e-mail: wenyuan.li@ieee.org).Digital Object Identifier 10.1109/TPWRS.2013.2295379

of an overhead line and therefore the conditions of all criticalitems should be monitored. The monitoring technologies forall the items of overhead lines have been developed and suc-cessfully applied. Many items (such as insulators, conductorsand fittings) can be continuously monitored online [3]–[9].Although the conditions of foundations, tower structures, aux-iliary facilities and surroundings still basically rely on human’sinspections, various hand-carrying instruments can be utilizedduring inspection tours [2]. Actually, aerial inspections havebeen applied widely in utilities. The monitoring and inspectionfor individual items of overhead line can provide the usefulinformation on the condition of different parts of overhead line.However, it is difficult to make a decision based on separateinformation of individual items. The following two challengingproblems must be resolved:• The outcome of each monitoring item reflects only a localcondition for that item and an overall index is needed torepresent the overall condition of whole overhead line.

• The monitoring data are not directly related to the failurerate or failure probability of overhead line and cannot beused to quantitatively assess the impact of overhead linecondition on a possible failure of overhead line and systemunreliability caused by line failure.

Health indices have been widely used at present. For ex-ample, [10] presents a health index based on monitoring data.Such a health index provides useful information for the healthstatus of individual equipment. However, this index cannot beutilized for assessment of system reliability and the impact ofthe maintenance on to system reliability cannot be quantified.Reliability-centered maintenance (RCM) for overhead lines hasbeen proposed for years [11], [12]. The application of RCM tooverhead line maintenance has been investigated by some utili-ties [13], [14]. The traditional RCM essentially only focuses onindividual lines but does not quantitatively analyze the systemreliability. One of the principles in reliability economics is: theimportance of a component in a system relatively less dependson itself but mainly on the impact on system reliability due to itsfailure [15]. Different locations, voltage levels and topologicalconnections of overhead lines in a power system have differentimpacts on system reliability. In developing amaintenance plan,not only a line’s own condition but also the impact of line failureon system reliability should be considered. A line that has thelargest impact on system reliability should be placed in the firstpriority for maintenance. A line whose maintenance can makea greater contribution to improve system reliability should be

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1840 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 4, JULY 2014

ranked for earlier maintenance, whereas the maintenance of aline whose failure has a relatively small effect on system re-liability may be delayed if there is a restriction of workforceor maintenance budget. To implement the principle above, aranking list of overhead lines should be produced on the basisof quantified reliability evaluation and cost efficiency analysis.A maintenance strategy based on both condition monitoring

and reliability assessment is proposed in this paper. The maincontributions include:• A method of estimating line failure rates using conditionmonitoring data. The failure rate calculated can be used toassess the system reliability and the reliability change dueto a variation of overhead line condition can be quantified.

• An importance ranking method based on the impact ofoverhead line on system reliability and its maintenancecost.

• A maintenance strategy considering the reliability andmaintenance cost on the basis of importance ranking. Thisstrategy provides the largest reliability improvement at thelowest cost and within budget.

This paper focuses on the maintenance in a short term (oneyear basis) since the monitoring information represents the cur-rent status of lines. The rest of the paper is organized as fol-lows. The model of estimating the failure rate of overhead linesusing monitoring data and the method of assessing the expectedenergy not supplied (EENS) index for system reliability arepresented in Section II. The maintenance importance rankingmethod and the corresponding maintenance strategy are pro-posed in Section III. A case study based on an actual transmis-sion system of an electric power company is given in Section IV,followed by conclusions in Section V.

II. RELIABILITY ASSESSMENT USING OVERHEADLINE CONDITION MONITORING DATA

A. Basic Procedure

Usually, an average failure rate based on historical statisticdata is used in reliability assessment [15]. In fact, the averagefailure rate model cannot reflect the variation of failure ratecaused by the varying overhead line condition which changesover time. The condition monitoring can provide sufficient in-formation of overhead line condition that represents its actualstatus in real life.The proposed reliability assessment method includes the fol-

lowing steps:1) The conditions of critical items such as tower foundations,tower structures, conductors, insulators, fittings, groundingdevices, auxiliary facilities and surroundings are moni-tored or inspected and the condition data are collected.

2) The condition measurements for each item under moni-toring have different units and value ranges because thesemeasurements are different quantities in nature (electrical,mechanical, structural, and spatial measurements). In orderto obtain a single condition index for a whole overhead linefrom the condition monitoring measurements of differentitems, it is necessary to normalize the original monitoringdata.

TABLE IOVERHEAD LINE MONITORING ITEMS AT A UTILITY COMPANY

3) The normalized monitoring data for different items areconverted into an overall condition index for the wholeoverhead line. This is called condition evaluation.

4) A failure rate model that represents the relationship be-tween the failure rate and overall condition index is builtusing historical monitoring data and historical failure ratedata.

5) The failure rate corresponding to the monitoring data of thecurrent overhead line condition is calculated.

6) The system reliability indices for the current overhead linecondition and after-maintenance are evaluated using a re-liability assessment model.

The quantified reliability indices together with maintenancecosts are used to calculate the ranking index for maintenanceand provide criteria for establishing a maintenance strategy.

B. Evaluating the Overall Condition Index of Overhead Line

The conditions of different portions of an overhead line canbe monitored or inspected. The main monitoring items at autility company are listed in Table I. Different items shouldhave different monitoring or inspection strategies accordingto their characteristics. A weighting factor between 10 and 1is assigned to each item by the utility in terms of its relativeimportance on overall reliability condition of overhead line.The weight 10 means the most important and 1 means theleast important. Note that the weightings represent the relativeimportance among different types of items.To relate monitoring data to the failure rate of an overhead

line, it is necessary to combine monitoring measurements forall the critical items into an overall condition index for wholeoverhead line. As seen in Table I, each item listed can be di-vided into sub-items. Each item or sub-item needs a specificmonitoring or inspection approach or technology. The moni-toring measurements from different items have different unitsand orders of magnitude. In order to combine the monitoringmeasurements in different dimensions into an overall conditionindex, it is necessary to normalize the monitoring measurementsand convert them into a form without unit between 0 and 1, with0 representing the most preferable condition and 1 representing

ZHANG et al.: OVERHEAD LINE PREVENTIVE MAINTENANCE STRATEGY 1841

the worst condition. There are the two following cases for allcritical items of an overhead line:• When a measurement reaches its lower limit , thecorresponding item is in the best condition. The normal in-terval of the measurement is and the item wouldbe abnormal only when is larger than . It can be ob-served from the items listed in Table I that most of thembelong to this case, in which the monitoring measurementcan be normalized by

(1)

• When a measurement reaches its upper limit , thecorresponding item is in the best condition. The normal in-terval of the measurement is and the item wouldbe abnormal only when it is smaller than . For example,the safety distance of clearance belongs to this case, inwhich the monitoring measurement can be normalizedby

(2)

In (1) and (2), is the normalized monitoring measurementand is the original monitoring measurement. Note that a mea-surement in case 1 only has the lower limit , whereas ameasurement in case 2 only has the upper limit . The nor-malized monitoring measurement , either from (1) or from (2),has a value between 0 and 1 regardless of units and magnitudesin original measurement z. A value closer to 0 represents a bettercondition and a value toward 1 represents a worse condition.Let denote the boundary value of a normalized measure-

ment at which an item would exceed its normal range. It can beverified that obtained from (1) is equal to from (2):

(3)

Equation (3) indicates that any measurement for differentmonitoring items reaches the same constant when it arrivesat the boundary beyond which the item exceeds its normal rangeregardless of a lower or upper boundary. It can be seen that theproposed normalization method has three features: the normal-ized monitoring measurement has no unit, ranges between 0 and1, and has the unique boundary value to distinguish betweennormal and abnormal conditions for all monitored items.The normalized measurements do not have units and are lo-

cated between 0 and 1, and therefore the arithmetic operationscan be directly conducted to obtain an overall condition indexfor a whole line. As mentioned earlier, each monitoring item isassigned a weight according to its importance on overall re-liability condition of overhead line, as shown in Table I. Theseweights are determined based on inputs from overhead line de-signers, field service personnel and system operation experi-ence, and are generally utility specific. The overall conditionindex of overhead line is defined by

(4)

where denotes the overall condition index of overhead line;is the normalized value for the th monitoring measurement ;and is the number of monitoring items. Apparently, is alsolocated between 0 and 1. A relatively large value beyond theregular range corresponds to a relatively large line failure rate.

C. Failure Rate Model Based on the Overall Condition Index

Average failure rates are used in the traditional reliability as-sessment methods. Asmentioned earlier, the average failure rateof an overhead line cannot reflect the variation of reliabilitycaused by changes in the condition of overhead line. In fact, thefailure rate of overhead lines varies with its condition. Exten-sive investigations show that an approximate exponential rela-tionship exits between the failure rate of overhead lines and itscondition measurement [16]–[18]. Therefore, the relationshipbetween the failure rate of overhead lines and the overallcondition index can be represented by

(5)

where A, B, and C are the three constants to be determined.The three constants A, B, and C can be estimated using histor-

ical statistics of failure rate and monitoring data that have beenrecorded in a same time period such as 10 years or even longer.Assume that a line with length has failures in a year andits condition index in the same year is . If the line conditionhas a relatively large variation in a year, the time window can bereduced to half a year or even a month depending on data avail-able. The and for all lines in the whole period are collected.All the pairs of and corresponding are arranged in an as-cending order of . The data are divided into multiple groupsin each of which the values are so close that they can be rep-resented by an average with a very small standard deviation. Agroup can contain only one or more values depending on theircloseness. An overall condition index and a failure rate foreach group are calculated as follows:

(6)

(7)

where is the number of pairs of and in the th datagroup. is expressed in failures/year/km.Each pair of failure rate and condition index corresponds to

the same line condition. Assume that there are pairs of failurerate and condition index. These pairs of data are applied to(5) using a nonlinear least squares method to estimate the threeconstant A, B, and C. In the estimation, transmission lines are di-vided into groups in each of which lines have the same voltagelevel and are located in the same region. Each group of lineshas its common A, B, and C. The unit of failure rate in (5) isfailures/year/km. Once the three constants are estimated, for anoverall condition index that represents the current or any spe-cific condition of overhead line, the corresponding failure rate

in one year can be estimated using (5). The failure rateof each line (in failures/year) is the estimated failure rate infailures/year/km times its length (km).

1842 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 4, JULY 2014

D. Reliability Assessment Model

The method presented in the above subsections can be usedto calculate the failure rates corresponding to the monitoringdata of overhead lines. The EENS index that represents thesystem reliability can be evaluated with the failure rates as inputdata. The system reliability assessment method includes the fol-lowing steps [15]:1) Create a load model. It will be seen in Section III that theEENS is used only for a relative comparison in the main-tenance strategy. The system reliability after performingmaintenance must satisfy the requirement at the peak loadlevel. Therefore, an annualized EENS index based on thepeak load level is good for the purpose. If necessary, how-ever, a multiple-level load representation can be used.

2) Select a system state using the following enumeration tech-nique. A system state refers to a state of the system havingcomponents in which components are in the failure

state and the remaining components are in thenormal state. The probability of system state is calculatedby [15]

(8)

where is the number of components unavailable in thesystem state is the total number of components inthe system; and is the unavailability of component ,which is equal to the product of failure rate and averagerepair time . For overhead lines

(9)

Conceptually, (8) is valid under the condition of indepen-dence. However, the multiple components associated witha dependent failure event can be viewed as a whole groupthat could be modeled as a “virtual component” with asingle failure probability since these components fail to-gether with this probability. Therefore, (8) can be readilyextended to model both independent and dependent com-ponents.

3) Perform a failure analysis for a system state with oneor more components in outage, and if there is a systemproblem, conduct an optimal power flow (OPF) analysisto evaluate the load curtailment required in the selectedstate. If the load curtailment is not zero, the selected stateis a failed one. The probabilities and load curtailments ofonly failure system states are recorded. The OPF model isas follows [15]:

(10)

subject to

(11)

(12)

(13)

(14)

(15)

where is the real power flow vector in the outagestate; is the relation matrix between real power flowsand power injections in the outage state ; PG and PD arethe generation output and load power vectors, respectively;CD is the load curtailment vector; , , , and

are the elements of PG, PD, CD and , respec-tively; , , and are the limits ofand , respectively; NG, ND, and are the sets ofgeneration buses, load buses, and circuits in the system.The objective of the model is to minimize the total loadcurtailment while satisfying the DC power flow relation-ships (11), power balance (12), and limits on generationoutputs (13) and line flows (15). Equation (14) representsthe restriction of maximum load curtailment. If the resultfrom this model requires no load curtailmentafter rescheduling of generations, then selected system

state makes no contribution to the EENS reliability index.If the result from the OPF model requires a non-zero loadcurtailment , the load curtailment makes con-tribution to the EENS reliability index [19].

4) Calculate the reliability indices EENS (MWh/year) usingthe following equations [15]:

(16)

where is the total load curtailment (MW) in state ,is the probability of state , is the set of all failure

system states at the th load level in the multiple-level loadmodel, is the time length (in hours) of the th load level;and NL is the number of load levels in the multiple-levelload model. If the annualized EENS only for the peak loadlevel is calculated, and becomes the total timelength considered for line maintenance, which is usuallyone year.

III. LINE MAINTENANCE STRATEGY BASED ON MONITORINGDATA AND RELIABILITY ASSESSMENT

Many factors need to be considered in amaintenance strategy,including the condition of overhead line, impact of line failureon system reliability and maintenance cost. A maintenance planincludes two tasks. First, it is necessary to rank overhead lines interms of benefit/cost ratios. Secondly, it is essential to determinewhich line should be maintained right away and which line canbe considered for a delayed maintenance according to the im-provement in system reliability due to its maintenance.

A. Overhead Line Ranking for Maintenance

The improvement in system reliability due to maintenance ofeach line is different because the condition of each overhead lineand its importance to network are both varied. The reduction inthe system reliability index after the maintenance of line isdefined as the maintenance reliability benefit index (MRBI) ofline as follows:

(17)

ZHANG et al.: OVERHEAD LINE PREVENTIVE MAINTENANCE STRATEGY 1843

where is the overall condition index of line beforemaintenance; is the overall condition index of line aftermaintenance; is the EENS indexwhen the overall condition indices of line 1 to are , ,, , , respectively; and is

the EENS index when the overall condition indices of line 1to are , respectively. The MRBI of lineis the difference between the EENS before the maintenanceof line and the EENS after the maintenance of line , fromwhich the overall condition index of line is decreased(improved) from to and the conditions of all other linesare assumed to be unchanged. can be chosen as the averageoverall condition index after the maintenance of line in thepast, which can be calculated from recent historical records.Due to the difference in length, voltage, type, terrain and ex-

posed environment, the maintenance cost of each overhead linediffers greatly. The improvement in the reliability index due tounit maintenance cost is defined as the maintenance cost utilityindex (MCUI) as follows:

(18)

where is the maintenance cost of line .The procedure to rank overhead lines for maintenance with

the MCUI is as follows:Step 1) Choose the lines whose normalized condition index

for any monitoring item is larger than as main-tenance candidates and assess the overall conditionindex of each candidate line.

Step 2) Calculate the and of each mainte-nance candidate line.

Step 3) Rank the overhead lines according to the .The lines with larger values are put at pri-ority for maintenance.

B. Maintenance Strategy for Overhead Line

Based on the ranking list, impacts on system reliability, andthe budget for overhead line maintenance, a strategy can beworked out, including which line should be maintained as soonas possible, which line’s maintenance can be postponed andwhich line does not need a preventive maintenance. Accordingto the relationship between historical records of maintenanceactivities and the reliability level that the system can bear, thefour reliability levels of normality, attention, abnormality andseverity can be defined using the EENS index. Let , , anddenote the boundary EENS values between normality and at-

tention, between attention and abnormality, and between abnor-mality and severity, respectively. When the EENS is lower than, the system reliability is in normal state. When the EENS

falls between and , the system reliability is in an atten-tion state, in which the system reliability is still acceptable butattention should be paid. When the EENS falls between and, the system reliability is in an abnormal state and one or

more lines should be considered for maintenance at a propertime. When the EENS is higher than , the system reliabilityis in a severe state and one or more lines must be maintained as

Fig. 1. Maintenance strategy of overhead lines based on monitoring data andsystem reliability.

Fig. 2. Relation between condition index and failure rate of overhead lines.

soon as possible. The flow chart for the maintenance strategy isshown in Fig. 1.The maintenance strategy includes the following main steps:1) Calculate the failure rates of overhead lines using moni-toring data and assess the reliability index EENS; set theinitial maintenance ranking indicator .

2) Judge the initial EENS to see if a maintenance activity isneeded or not.

3) Calculate the and for each candidateoverhead line and rank the lines using the .

4) Calculate the EENS index by considering the maintenanceof lines in the ranking order in Step 3. According to thevalue of the EENS ( , ,

or ), it can be judgedif the current line in consideration should be maintained assoon as possible, or should be arranged for maintenance ata proper time, or does not need to schedule a maintenance

1844 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 4, JULY 2014

Fig. 3. Maintenance reliability benefit index in the two cases.

Fig. 4. Maintenance cost utility index in the two cases.

so far, or needs no maintenance at all. If the system relia-bility is in the attention or normal state, then the procedureends; otherwise, go to the next step.

5) Determine whether the total maintenance cost exceeds thebudget. If the total cost is lower than the budget, considerthe next line on the ranking list; otherwise, consider thepossibility of increasing the budget. If the budget cannotbe increased to bring the system from the attention state tothe normal state, the system reliability should be closelymonitored.

6) Consider the overhead lines on the ranking list one by one.After the maintenance of the th line is considered, thesystem reliability index is reduced to

; then go back to step 4.It can be seen that the proposed maintenance strategy can de-

termine which line should be maintained as soon as possible,which line should be maintained at a proper time and whichline does not need a preventive maintenance due to its so smallimpact on the system reliability. The strategy uses the condi-tion monitoring data of individual lines as the original inputsand considers both impacts of line maintenance on system reli-ability and maintenance costs to build the criterion. The main-tenance plan obtained from the proposed strategy leads to thelargest benefit in system reliability improvement at the lowestcost within the financial budget.

IV. CASE STUDY

The maintenance of overhead lines in a transmission systemlocated in the southwest of China is used as an example. The

system supplies a total load of 5473 MW and has 182 busesand 240 branches. Seven lines were identified as abnormal ac-cording to normalized condition indices which were calculatedfrom condition monitoring data, and thus selected to be the can-didates for maintenance. These lines are denoted byin the paper. The data pairs of condition index and failure ratewere obtained using historical records and (6) and (7). Thesedata points are plotted in Fig. 2. The three constants A, B, andC in (5) were estimated using these data pairs and the methodgiven in Section II-C as follows:

; ; ;The solid curve in Fig. 2 was obtained using (5) with the

estimated constants A, B, and C, from which it can be seen thatit closely matches to the original data and shows an exponentialincreasing trend of failure rate with the condition index after apoint close to the limit for the normal condition [see(3)]. Before the point, the failure rate almost remains unchangedas the line condition is within the normal range.Two maintenance cases, in which the 7 lines have different

monitoring conditions, are compared. The overall conditionindices of the 7 lines and corresponding maintenance costsare shown in Table II. The failure rate of each line was cal-culated using the method given in Section II-C. The resultingfailure rates corresponding to the condition in each case arealso listed in Table II. The EENS reliability indices werecalculated using the method in Section II-D. The MRBI andMCUI indices for the 7 lines in the two cases were calculatedusing (17) and (18) in Section III and are shown in Figs. 3and 4, respectively. Note that the 7 lines have been arranged

ZHANG et al.: OVERHEAD LINE PREVENTIVE MAINTENANCE STRATEGY 1845

TABLE IICONDITION INDICES, FAILURE RATES AND MAINTENANCE COSTS IN CASES 1 AND 2

TABLE IIIRESULTS IN CASE 1

TABLE IVRESULTS IN CASE 2

in the ranking order using the MCUI in each figure. Theranking order, MRBI, MCUI and EENS reliability indicesbefore and after maintenance, and cumulated maintenancecosts for the 7 lines in case 1 are given in Table III and thosein case 2 in Table IV. The cumulated cost in each columnrefers to the sum of maintenance costs of the line in thiscolumn and all other previous lines in the ranking order.For example, in Table III, the cumulated cost in the columnnumbered by 3 is the sum of maintenance costs of L3, L6,and L1. It can be seen that the ranking orders of lines in thetwo cases are different due to the difference in the monitoredconditions of the lines, whereas the ranking orders of lines interms of the MRBI and MCUI indices are the same in eachcase.The system reliability EENS boundary values between

normal and attention states, between attention and abnormalstates, and between abnormal and severe states are desig-nated as , , and

, respectively. For case 1, the MCUI(15.292MWh/yr/k$) of L3 is the largest and thus L3 shouldbe maintained first. The system reliability index before L3’smaintenance is , which falls in therange for the severe state. Therefore, L3 should be maintainedas soon as possible. After the maintenance of L3, the systemreliability index is reduced to ,

which is still in the range for the severe state. The second line L6in the ranking order therefore should be maintained as soon aspossible too. After the maintenance of L6, the system reliabilityindex is reduced to , which fallsin the range for the abnormal state. The third line L1 should bemaintained at a proper time. After the maintenance of L1, thesystem reliability is reduced to ,which is still higher than . After L5 is scheduled to maintainat a proper time, the system reliability is controlled withinthe range for the normal state. The maintenance plan obtainedusing the proposed strategy for case 1 is: L3 and L6 should bemaintained as soon as possible, and L1 and L5 should be main-tained at a proper time, whereas L2, L4, and L7 do not need apreventive maintenance in terms of their current statuses. Thetotal maintenance cost for case 1 is $100 500.For case 2, the MCUI (15.254MWh/yr/k$) of L6 is the largest

and thus L6 should be maintained first. The maintenance planfor case 2 is: L6 should be maintained as soon as possible, andL1 and L3 should be maintained at a proper time, whereas L2,L4, L5, and L7 do not need a preventive maintenance in terms oftheir current statuses. After the maintenances, the system relia-bility index is reduced to 2852.807MWh/yr which is just below, requiring a close attention to the system reliability level. The

total maintenance cost for case 2 is $75 000. Note that it is nec-essary to check the maintenance cost against the budget in the

1846 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 4, JULY 2014

decision process for each line. When the budget does not allowmore maintenance activities, the proposed method can providethe information about the potential improvement in the systemreliability due to each maintenance activity for decision-makersto consider.

V. CONCLUSIONS

Traditional condition-based maintenance methods only con-sider the monitored condition of individual equipment. Onemajor disadvantage is the monitoring data is not converted tothe failure rate of equipment and thus it is impossible to quan-tify the impacts of equipment failures on system reliability. Bycombining the condition monitoring techniques for overheadlines with the idea of reliability centered maintenance, a newmethod is presented to model the quantitative relationshipbetween monitoring data of overhead lines and failure ratesas well as system reliability. A maintenance strategy that isbased on the monitoring data and impacts of line maintenanceon system reliability is proposed. In the proposed strategy,the lines that do not have sufficient impacts on the systemreliability may not need a preventive maintenance, whereasthe lines that have relatively large impacts are considered forpreventive maintenances at different levels according to theimpact degrees, costs and the budget constraint.The overhead lines in a transmission system in the southwest

of China have been used to demonstrate the procedure and ef-fectiveness of the proposed method. The results indicate that inorder to meet the required system reliability level, the mainte-nance cost using the proposed method is about two-thirds of thatusing the traditional method in case 1 and half in case 2. Col-lection of online monitoring data is a challenge in applying theproposed method and more work is needed in this regard in thefuture.

REFERENCES

[1] NETL. The Modern Grid Strategy: A vision for the smart grid. Pitts-burgh, PA, USA, U.S. Department of Energy, Jun. 2009 [Online].Available: http://www.netl.doe.gov/smartgrid/refshelf.html#WhitePapers

[2] N. Kilroe, “Line inspections: Eyes and ears of RCM,” in Proc. IEEE10th Int. Conf. ESMC, 2003, pp. 18–24.

[3] H. H. Kordkheili, H. Abravesh, M. Tabasi, M. Dakhem, and M. M.Abravesh, “Determining the probability of flashover occurrence incomposite insulators by using leakage current harmonic components,”IEEE Trans. Dielectr. Electr. Insul., vol. 17, no. 2, pp. 502–512, 2010.

[4] C. Andrea, S. Chandrasekar, M. G. Carlo, and P. Francesco, “Infer-ring ceramic insulator pollution by an innovative approach resorting toPD detection,” IEEE Trans. Dielectr. Electr. Insul., vol. 14, no. 1, pp.23–29, 2007.

[5] S. Venkataraman, R. S. Gorur, and A. P. Mishra, “Impact of weatheringon flashover performance of nonceramic insulators,” IEEE Trans. Di-electr. Electr. Insul., vol. 15, no. 4, pp. 1073–1080, 2008.

[6] I. Ramirez-Vazquez, R. Hernandez-Corona, and G. Montoya-Tena,“Diagnostics for nonceramic insulators in harsh environments,” IEEEElectr. Insul. Mag., vol. 25, no. 6, pp. 28–33, 2009.

[7] V. S. Murthy, K. Tarakanath, D. K. Mohanta, and S. Gupta, “Insu-lator condition analysis for overhead distribution lines using combinedwavelet support vector machine (SVM),” IEEE Trans. Dielectr. Electr.Insul., vol. 17, no. 1, pp. 89–99, 2010.

[8] M. Komoda, T. Kawashima, M. Minemura, A. Mineyama, M. Aihara,Y. Ebinuma, T. Kanno, and M. Kiuchi, “Electromagnetic inductionmethod for detecting and locating flaws on overhead transmissionlines,” IEEE Trans. Power Del., vol. 5, no. 3, pp. 1484–490, 1990.

[9] H. Zangl, T. Bretterklieber, and G. Brasseur, “A feasibility study on au-tonomous online condition monitoring of high-voltage overhead powerlines,” IEEE Trans. Instrum. Meas., vol. 58, no. 5, pp. 1789–1796,2009.

[10] T. Hjartarson, B. Jesus, D. T. Hughes, and R. M. Godfrey, “Develop-ment of health indices for asset condition assessment,” in Proc. IEEETransmission and Distribution Conf. Expo., 2003, vol. 2, pp. 541–44.

[11] J. Moubray, Reliability-Centered Maintenance. New York, NY,USA: Industrial Press, 1997.

[12] M. E. Beehler, “Reliability centered maintenance for transmission sys-tems,” IEEE Trans. Power Del., vol. 12, no. 2, pp. 1023–1028, 1997.

[13] A. Ozdemir and E. D. Kuldasli, “RCM application for turkish nationalpower transmission system,” in Proc. IEEE 11th Int. Conf. Proba-bilistic Methods Appl. to Power Syst. (PMAPS), 2010, pp. 143–147.

[14] J. W. Goodfellow, “Applying reliability centered maintenance (RCM)to overhead electric utility distribution systems,” in Proc. IEEE PESSummer Meeting, 2000, pp. 566–569.

[15] W. Li, Risk Assessment of Power Systems: Models, Methods, and Ap-plications. New York, NY, USA: IEEE Press/Wiley, 2005.

[16] R. E. Brown, G. Frimpong, and H. L. Willis, “Failure rate modelingusing equipment inspection data,” IEEE Trans. Power Syst., vol. 19,no. 2, pp. 782–787, 2004.

[17] R. E. Brown, Electric Power Distribution Reliability. NewYork, NY,USA: Marcel Dekker, 2002.

[18] D. Hughes, “Condition based risk management (CBRM)—enablingasset condition information to be central to corporate decisionmaking,” in Proc. 18th Int. Conf. Exhib. Electricity Distribution,CIRED 2005, Technical Reports—Session 1: Network Components,Turin, Italy, 2005, pp. 39–43.

[19] W. Li, Probabilistic Transmission System Planning. Hoboken, NJ,Wiley: , 2011.

Dabo Zhang received the M.S. degree in electrical engineering from SouthwestJiaoTong University, Sichuan, China, in 2007 and the Ph.D. degree in the Col-lege of Electrical Engineering, Chongqing University in China.He is currently a faculty member in He Fei University of Technology in

China. His research interests include maintenance strategy of power equipment,asset management and reliability evaluation of power systems.

Wenyuan Li (F’02) is a Professor at Chongqing University of China, a PrincipalEngineer at BC Hydro, Vancouver, BC, Canada, and an Adjunct Professor atSimon Fraser University of Canada. He has published five books and over 170papers in power system planning, probabilistic applications and reliability.Dr. Li is an editor of IEEE TRANSACTIONS ON POWER SYSTEMS and IEEE

POWER ENGINEERING LETTERS. He is a Fellow of the Canadian Academy ofEngineering and Engineering Institute of Canada. He has received several IEEEawards including the IEEE PES Roy Billinton Power System Reliability Awardin 2011 and the International PMAPS Merit Award in 2012.

Xiaofu Xiong is a full Professor at the College of Electrical Engineering ofChongqing University in China. His interest includes power system protection,automation and reliability evaluation.