etep1764

Risk-based maintenance for asset management of powertransformer: practical experience in Thailand

Thanapong Suwanasri1,3, Rattanakorn Phadungthin1,3 and Cattareeya Suwanasri2,3*,†

1The Sirindhorn International Thai-German Graduate School of Engineering, Bangkok, Thailand2Department of Electrical and Computer Engineering, Faculty of Engineering, Bangkok, Thailand

3King Mongkut’s University of Technology North Bangkok, Bangsue, Bangkok 10800, Bangkok, Thailand

SUMMARY

Nowadays, the requirement on diminishing operating and maintenance costs is highly concerned for utilitiesin competitive electricity market. Power transformer is first focused due to its acquisition costs and failureconsequences (FCs). Traditional preventive maintenance for power transformer is generally applied. However,it is very costly and does not take into account actual transformer condition. Hence, risk-based maintenance isintroduced in this paper to facilitate the maintenance works of power transformers. The conditions of powertransformers are evaluated from electrical test, insulating oil test and visual inspection by using the rankingand weighting techniques. In addition, the Analytic Hierarchy Process (AHP) is proposed to determine the im-portant weighting factor of each transformer component. The three main criteria of the AHP are maintenancedifficulty, FC and failure history. Moreover, the Weibull distribution techniques are applied to analyze failurecauses. The computerized web-application program is developed and implemented for practical use in a utilityin Thailand. The power transformers installed in 115 kV and 230 kV transmission systems in Thailand areassessed due to availability and quality of data. From statistical analysis of failure records, the conditions ofpower transformer can be divided into three different zones as risk, moderate and healthy zone. The defectivecomponents in risk zone with higher proportion of failure should be carefully focused. The components andoverall condition of the transformers are perceived from the analysis of test results. Subsequently, therisk-based maintenance is properly planned according to the actual condition. Therefore, maintenance of powertransformer fleet can be effectively managed resulting in higher availability and reliability, lower risk of failureand lower cost of maintenance. Copyright © 2013 John Wiley & Sons, Ltd.

key words: Analytic Hierarchy Process; pairwise comparisons; power transformer; condition assessment;failure analysis; condition-based maintenance

1. INTRODUCTION

Power transformer is one of the most important high voltage equipment in power system. Its functionis to transform the voltage into an appropriate level for electricity utilization. The transformer mainte-nance is needed to maintain system’s stability and reliability. Normally, the preventive maintenance isapplied because it uses knowledge method with a simple and routine approach. However, it is verycostly and does not take into account the actual transformer condition. Thus, the condition-based main-tenance is presently introduced to achieve the cost-effective maintenance tasks based on the actualevaluated condition.Different techniques are applied in transformer condition evaluation, e.g. fuzzy evaluation, fuzzy

neural network, fuzzy model multi-layers classification, rough set theory, D–S evidential reasoning,artificial neural networks and evidential reasoning [1–3]. These techniques can determine ambiguousand uncertain characteristic numbers; however, they are all qualitative assessment to transformer con-dition. Therefore, one excellent technique so called the Analytic Hierarchy Process (AHP) is proposed

*Correspondence to: Cattareeya Suwanasri, King Mongkut’s University of Technology North Bangkok, Bangsue,Bangkok 10800, Thailand.†E-mail: [email protected]

Copyright © 2013 John Wiley & Sons, Ltd.

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMSInt. Trans. Electr. Energ. Syst. (2013)Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/etep.1764

to deal with the interdependent criteria involving both quantitative and qualitative issues [4–8]. TheAHP has various abilities to structure a complex of problems such as multi person, multi attributeand multi period [9]. In addition, it has a unique technique to quantify judgmental consistency.This paper aims to determine the critical components of power transformers and their failure caused

by using the failure statistics, to estimate expected lifetime of the critical components by using theWeibull distribution and to evaluate the component as well as overall conditions of power transformersby using the historical test results for setting up the condition-based maintenance strategy. The scoringand weighting technique is applied to evaluate the condition of the transformer components whereasthe AHP technique is applied to obtain important weighting factor of the transformer components.The historical test results for the condition evaluation consist of electrical test, insulating oil and visualinspection. The computerized web-application program is developed for the proposed condition-basedmaintenance strategy so that power transformer fleet can be effectively managed.

2. POWER TRANSFORMER ASSET MANAGEMENT

The asset management process of power transformer is shown in Figure 1. Historical test records areused to assess condition of power transformer, while network data is used to assess importance of thetransformer. The transformer risk is obtained from combining the condition and the importance. In ad-dition, economic aspect regarding financial information should be taken into account. Subsequently,the decision with the most cost-effective task can be made, and the asset management strategies arefinally planned appropriately.In this paper, the power transformer asset management includes failure analysis and condition-based

maintenance. The software program is also developed and implemented for the analysis. The compo-nents of power transformer are classified into seven categories, e.g. active part, insulating oil, bushing,arrester, on load tap changer, tank and protective devices.

2.1. Failure statistics

Failure events of power transformers should be systematically recorded and analyzed in order to deter-mine critical components and failure causes. For accurate failure analysis and forecasting as well asaging and reliability assessment, the well-known Weibull distribution method is applied [10,11].The three main Weibull parameters are Beta, Eta and Gamma. Beta parameter (b) indicates type of fail-ure modes as burn-in, random or wear-out period. Eta parameter (Z) is Weibull characteristic life as ameasure of the scale or spread in data distribution. Gamma parameter (g) is the location parameter in-dicating the time shift from the origin of the distribution. According to these parameters, Weibull sta-tistical properties in terms of probability distribution (PDF), cumulative distribution functions (CDF),reliability, failure rate, as well as Mean Time between Failure (MTBF) or expected life time can becalculated as follows.

Figure 1. Power transformer asset management.

T. SUWANASRI ET AL.

Copyright © 2013 John Wiley & Sons, Ltd. Int. Trans. Electr. Energ. Syst. (2013)DOI: 10.1002/etep

A. PDF

f tð Þ ¼ b�

� �t � g�

� �b�1

e�t�g�ð Þb ; t≥g (1)

B. CDF

F tð Þ ¼ 1� e�t�g�ð Þb ; t≥g (2)

C. Reliability

R tð Þ ¼ 1� F tð Þ ¼ e�t�g�ð Þb ; t≥g (3)

D. Failure rate

l tð Þ ¼ f tð ÞR tð Þ ¼

b�

� �t � g�

� �b�1

(4)

E. MTBF

MTBF ¼ gþ �:Γ 1þ 1b

� �(5)

where Γ 1þ 1b

h iis the gamma function evaluated at the value of 1þ 1

b

h i[12].

2.2. Condition-based maintenance

Presently, a condition-based maintenance [13] is introduced to maintain the asset as power transformerbecause it takes into account the actual transformer condition resulting in the reduction of operatingand maintenance costs. In this paper, power transformer components are classified into seven catego-ries, e.g. active part, insulating oil, bushing, arrester, on load tap changer, tank and protective devicesas given in Figure 2.

2.2.1. Scoring and weighting technique for component condition evaluation. Diagnostic technique forcomponent condition evaluation consists of three categories as electrical tests, insulating oil tests andvisual inspection. The electrical tests and the insulating oil tests [14–18] from online and offline tests

Main tank

Figure 2. Power transformer components.

RISK-BASED MAINTENANCE, POWER TRANSFORMER, CONDITION ASSESSMENT


are usually evaluated for transformer component condition, whereas the visual inspection is regularlyinvestigated on daily and weekly basis. The multi-criterion analysis form [19] is applied in the trans-former component condition evaluation. The scoring technique is used for classifying the conditioninto several levels such as good, suspect and poor. The weighting technique is used for ranking the pre-cision and importance of each diagnosis.In this paper, the diagnostic techniques for active part are the measurements of exciting current, core

insulation resistance, winding insulation resistance, 1f winding leakage impedance, 3f windingequivalent leakage impedance, winding ratio and DC winding resistance obtained from [20], as shownin Table I. The diagnostic techniques, which have the better ability to access the actual condition, willbe assigned with the higher weighting number for the active part (i.e. the weighting numbers for thewinding exciting current are assigned to be “3”, and if the percent deviation of the exciting currentcompared to the commissioning value lies between “0 and 2”, the condition is good; as well as thatfor the magnetic core insulation and winding insulation resistance is assigned to be “5”). Diagnosistechniques of other components are summarized in Table II.Thereafter, the condition of each component is evaluated in the percentage of component condition

index (CI) (%index) as shown in Equation [6].

%index ¼

Xni¼1

Si �Wið ÞXni¼1

Smax;i �Wi

� �� 100 (6)

where:Si = Scoring numberSmax,i =Maximum scoreWi =Weighting numbern =Number of diagnostic tests

Table I. Diagnostic technique for active part.

Diagnosis

Condition

WeightGood Suspect Poor

Core insulation resistance >100 10–100 0–10 5Exciting current 0–2 2–10 >10 31f impedance 0–0.5 0.5–3 >3 43f impedance 0–0.5 0.5–3 >3 4Ratio 0–0.1 0.1–0.5 >0.5 4DC resistance 0–1 1–5 >5 3Winding insulation (%PF) 0-0.5 0.5–2 >2 5

Table II. Diagnostic technique for other components.

Components Diagnosis Components Diagnosis

Insulating oil • Dielectric Breakdown OLTC • Transition Resistance• Moisture Content • Contact Wear• Power Factor • Dissolved Gas Analysis• Color • Color• Acidity • Water Content• Interfacial Tension • Dielectric Breakdown

• Power Factor• Leakage Current

Bushing • Power Factor Arrester • Watt Loss• Capacitance • Insulation Resistance• Visual Inspection • Visual Inspection

Tank • Visual Inspection Protection • Visual Inspection

T. SUWANASRI ET AL.


The percentage of component CI (%index) is further ranked within the intervals for indicating the CIof each component; therein, color indicators as green, yellow and red stand for good, suspect and poorconditions, respectively.

2.2.2. AHP for component important weighting. AHP is a flexible and powerful technique to determinethe important weighting factor (Wj) [21–26]. It is also a classical technique for Multi-Criteria DecisionAnalysis or Multi-Criteria Decision Making. The AHP is suitable for complex decisions concerning thecomparison, which is difficult to quantify [27]. As the AHP is based on pairwise comparisons of decisioncriteria, all individual criteria must be first paired against the others when the qualitative criteria are iden-tified and organized in a hierarchical structure. Finally, the goal of the structure as important weightingfactors will be determined. The AHP methodology is summarized step by step in [28–31].To implement the important weighting factor of each component of power transformer using the

AHP technique, a hierarchy decision model is developed as presented in Figure 3. The model is sep-arated into five main levels including goal, criteria, two sub-criteria and alternative levels. The goal iscomponent ranking using three criteria as maintenance difficulty (MD), failure consequence (FC) andfailure history (FH). In the first sub-criteria, MD consists of detectability, resource and effort. FC iscomposed of operability and environment. The second sub-criteria showing different important criteria(e.g. D1-D3, R1-R3, etc.) are used to estimate the important weighting factors of the power trans-former components. The AHP evaluation is judged by four different groups of experts who are special-ized in power transformer research, high voltage testing laboratory, maintenance and repair shop, aswell as power transformer and transmission system management. The complete detail of the AHP isshown in Appendix.

2.2.3. Overall condition evaluation of power transformer. To evaluate overall condition of powertransformer, the CI (CIj) and the important weighting factor (Wj) estimated from AHP technique oftransformer components (e.g. active part, insulating oil, bushing, arrester, on load tap changer, tankand protective devices) are combined together in order to obtain the overall condition (%CI) as shownin Equation [7].

%CI ¼Xmj¼1

Wj � CIj� �� 100 (7)

GOALImportance Weighting Factor

Maintenance Difficulty Failure Consequence Failure History

Detectability Resource Effort

R2R1 R3 E2E1

Operability Environment

O2O1 O3 V2V1

Power Transformer ComponentsActive Part, Insulating Oil, Bushing, Arrester, OLTC, Tank, Protective Device

D2D1 D3

Note: D1 = shutdown, D2 = normal monitoring, D3 = special testing,R1 = repairability, R2 = spare availability, R3 = knowledge, E1 = cost,

E2 = time, O1 = transformer operation, O2 = other component,O3 = other equipment, V1 = social, V2 = people

Figure 3. AHP structure model of transformer component.



where:Wj = important weighting factorCIj = condition index of transformer componentsm = number of transformer components, in this paper “m” is 7.

Similarly, the overall condition of power transformer will be indicated as green, yellow and red thatstand for good, suspect and poor conditions, respectively.

3. SOFTWARE DEVELOPMENT

It is necessary to develop a decision support tool for condition-based maintenance of power trans-former. The tool consists of database management system for a convenient data record, analytical pro-cess and user interface module via web application as shown in Figure 4. The database module isdeveloped to work with web application program in order to retrieve the information from databasefor analyzing and back-recording the data. The Spring MVC framework with JAVA language applica-tion has been developed for web application.

4. ANALYSIS AND RESULTS

4.1. Failure analysis and statistical results

Power transformers in Thailand are mainly used in transmission system as tie-transformer in 500 kVand 230 kV levels; whereas, loading transformers are used in 115 kV. Failures and their causes of eachmain component are calculated and shown as a percentage for tie transformer rated 230/115 kV,200 MVA and loading transformer rated 115/22 kV, 50 MVA.The number of the tie transformer population is 117 units with 30 failure records, whereas a loading

transformer population is 186 units with 59 failure records. Thus, the total 89 failure events of 303power transformers are systematically recorded and analyzed for the critical component and failurecauses. The largest proportion of the 200 MVA tie transformer as shown in Figure 5 is tank with

Figure 4. Database management program using spring MVC framework.

Figure 5. Defective components of tie-transformer 230/115 kV, 200 MVA.

T. SUWANASRI ET AL.


40%, while the smallest proportion is self-protective devices or protection with 3.3%. However, activepart and arrester have not failed during the last 10 years. For the 50 MVA loading transformers asshown in Figure 6, the largest proportion is OLTC with 25.4%, while the smallest proportion is insu-lating oil with 1.7%.Failure causes are classified into several types, i.e. flashover, leakage, winding short circuit and so

on as shown in Figure 7 and Figure 8. For example, the highest proportion of failures occurred inthe 200 MVA tie transformer is tank, which is mostly resulted from oil leakage. Similarly, for the50 MVA loading transformer, the highest proportion of failure occurs with OLTC by oil leakage.

Figure 6. Defective components of loading transformer 115/22 kV, 50 MVA.

3.3%

13.3%

16.7%

40.0%

13.3%13.3%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Insulating Oil Tank Protection OLTC Bushing Others

Test not passed

Dirty

Loose

Broken

Others

Wrong operation

Leakage

Figure 7. Failure statistics for tie-transformer 230/115 kV, 200 MVA.

3.4%

10.2%

15.3%

20.3%

3.4%

20.3%

1.7%

25.4%

30.0% Flashover

Test not passed

Dirty

Loose

Unsmooth

Broken

Not work

Damage

Short circuit

Others

Wrong operation

Leakage

Hotspot

3.4%

10.2%

15.3%

20.3%

3.4%

20.3%

1.7%

25.4%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

ActivePart

InsulatingOil

Tank Protection OLTC Bushing Arrester Others

Figure 8. Failure statistics for loading transformer 115/22 kV, 50 MVA.



4.2. Weibull distribution analysis

For the transformers rating 230/115 kV 200 MVA and 115/22 kV 50 MVA, the failure components,causes and calculated values of Weibull parameters are summarized in Table III.The tank leakage of the 200 MVA transformers and the OLTC leakage of the 50 MVA transformers

are considered for the highest percentage of failure. For the tank leakage, the b of the 200 MVA trans-formers is 2.25, leading to an increasing failure rate with increasing time, called wear-out period. TheZ and the MTBF are 15.04 and 13.32 years, respectively. The average lifetime of the tank is approx-imately 13 years longer than that of the 50 MVA, approximately 7 years because the tank design of thelarge transformers could be thicker than the smaller units. In case of OLTC, the b of OLTC leakage is4.65, which is also in wear-out period. The Z and the MTBF are 17.82 and 16.30 years, respectively.This implies that the average lifetime of the OLTC is approximately 16 years when the leakage prob-lem is concerned. Unfortunately, the number of OLTC leakage of the 200 MVA transformers is only 1record so that its average lifetime used to compare with the 50 MVA cannot be possibly estimated byWeibull. However, OLTC of small transformers seems to fail more frequently than larger units due totheir load variation.

4.3. AHP analysis

As the tie transformers rating 230/115 kV, 200 MVA and loading transformer rating 115/22 kV, 50MVA are selected in the analysis, component important weightings of these two groups are assessedseparately by using AHP technique.

4.3.1. AHP results for 230/115 kV 200 MVA tie transformers. Eigenvectors and the relative weights ofthe pairwise comparison matrices are judged. Weights or priorities of criteria, sub-criteria and alterna-tives are then determined and aggregated. Finally, the percentage of important weight factors of 230/115 kV, 200 MVA power transformer components is accomplished reasonably as illustrated inTable IV.The result shows that the highest percentage of the importance weighting is bushing with 28.97%,

while the lowest is protective devices with 4.27%. This results from the fact that the decision makersjudge the bushing as the most critical component due to its experienced FC.

4.3.2. AHP results for 115/22 kV, 50 MVA loading transformers. Most eigenvectors and relativeweights of AHP pairwise comparison matrices for 50 MVA transformers are assumed equal to that

Table III. Weibull parameters for life time estimation.

Component Failure Rating b � MTBF

Tank Leakage 230/115 kV 200 MVA 2.25 15.04 13.32Tank Leakage 115/22 kV 50 MVA 3.94 7.35 6.66Bushing Leakage 230 kV System 2.26 17.47 15.48Bushing Leakage 115 kV System 1.39 12.29 11.22OLTC Leakage 115/22 kV 50 MVA 4.65 17.82 16.30

Table IV. Important weighting for 230/115 kV, 200 MVA transformer components.

Transformer Components Important Weighting (%)

Active Part 13.48Insulating Oil 11.00Bushing 28.97Arrester 9.75OLTC 19.48Tank 13.05Protective Devices 4.27

T. SUWANASRI ET AL.


of the 200 MVA transformers except the ones with respect to quantitative aspect, e.g. acquisition cost,repairing time and FH. After the numbers in these matrices are normalized, the important weightingfactor in form of percentage for the 50 MVA transformer components can be concluded in Table V.It has no doubt that bushing is the highest weighted component with 30.24% because the decision

makers judged that it is the most critical component due to its experienced FC, while the lowest pro-portion is tank with 5.53%. Furthermore, the number of bushing failure record is in the second highestproportion whereas the OLTC is the highest one.

4.4. Condition evaluation

The condition evaluations of 18 transformers as rating of 230/115 kV, 200 MVA tie transformers and115/22 kV, 50 MVA loading transformers are presented. However, for describing the method, analysisand result, only one transformer (T1) as the worst condition transformer is described as an example.

4.4.1. Condition of transformer (T1). The transformer T1 was installed in 1976 as rating of 115/22 kV,50 MVA. Some test results are shown in Tables VI–VIII. The condition of transformer T1 component isassessed and shown by color indicators in Table IX.

• Insulating oil of the OLTC is contaminated due to exceeding water content value. The dielectricproperty of the oil deceases due to below limit value of dielectric breakdown and over limit ofpower factor. In addition, the rate of C2H4 gas produced is higher compared to C2H2 gasproduced in the oil. By visual inspection, bottom color of silica gel of the OLTC conservator is

Table V. Overall priorities for 115/22 kV, 50 MVA transformer components.

Transformer Components Important Weighting (%)

Active Part 14.51Insulating Oil 8.03Bushing 30.24Arrester 10.70OLTC 23.23Tank 5.53Protective Devices 7.76

Table VI. Diagnostic test results of HV winding.

Exciting Current Leakage Impedance Ratio DC Winding Winding Insulation

[%Error] [%Error] [%Error] [%Error] [%PF]8.47 1.85 (1f), 1.73 (3f) 0.38 11.17 0.25

Table VII. Diagnostic test results of on load tap changer.

Contact Contamination Dielectric Property

Transition Resistance Contact Wear Color Moisture Dielectric BD Power Factor[%Error] [mm/100k times] - [ppm] [kV] [%]7.80 0.50 0.5 24 40.06 0.4

Table VIII. Diagnostic test results of DGA of on load tap changer insulating oil.

CH4 C2H6 C2H4 C2H2 H2

[ppm] [ppm] [ppm] [ppm] [ppm]5023 5633 22 881 13 771 11 535



changed more than a quarter, and there are dried oil stains because oil leakage in the conservatorand OLTC compartment. This can be verified that the condition of the OLTC is poor and indi-cated by red color.

• For active part, the values of exciting current, leakage impedance, ratio and DC winding resis-tance tests are above the maximum limit. After the analysis, the condition of windings is suspectand indicated by yellow color. The windings might have a problem of short-turn and insulationdeterioration. This result can be confirmed by the higher CO2 gas concentration from dissolvedgas analysis test. In case of bushing, the exceeding power factor values and dark color of oildetected by visual inspection provide suspect condition denoted by yellow color.

• Arrester is also in suspect condition owing to below limit of insulation resistance.• For insulating oil, the value of interfacial intension is below the limit, while the values of acidity andpower factor are above. Thismeans that the oil condition is suspect, represented by a yellow indicator.

• Tank and accessories are in suspect condition as well, resulting from tear gasket of transformercontrol cabinet, animal net found in radiator, dirty condition and support insulation as well asloosing terminal connecter of neutral ground reactor, as observed by visual inspection.

The critical component required to be maintained first is OLTC, while the other parts are focusednext. Therefore, the actual condition of the components is used to calculate the overall condition, ap-proximately 64%, which is “suspect” and needs careful intension.

Table IX. Component condition of transformer T1.

Component Subcomponent Result Condition

Active Part Core Yellow SuspectHV Winding Yellow SuspectLV Winding Yellow SuspectTV Winding Yellow Suspect

Bushing HV, LV, TV Yellow SuspectArrester HV, LV, TV Yellow SuspectOLTC - Red PoorInsulating Oil - Yellow SuspectTank - Yellow Suspect

0

20

40

60

80

100

T14 T15 T16 T17 T18

Active part

Bushing

Arrester

OLTC

Insulating oil

Tank

Protection

Overallcondition

Transformer condition

70%

35%

Transformer

Figure 9. Component and overall conditions of 200 MVA power transformers.

T. SUWANASRI ET AL.


4.4.2. Conditions of a total 18 power transformers. The overall condition and the component condi-tion of 18 sample transformers are evaluated and plotted in Figure 9 and Figure 10 for the 200 MVAand the 50 MVA transformers, respectively. The higher percentage of the condition means “poor” con-dition, which lies between 71 and 100%. The “suspect” condition is between 36% and 70%, while the“good” condition is between 0% and 35%.In case of 200 MVA transformers as presented in Figure 9, the active part is the critical components

with “poor” condition of transformers T14 and T18. Thus, it should be paid attention. However, T15,T16 and T18 are the worst transformers of this group with 60% of overall condition. For the 50 MVAtransformers as presented in Figure 10, the active part is critical component with “poor” condition ofT4, T7, T9 and T10 while OLTC is that of T1 and T4. Furthermore, the worst transformer is T1 with64% of overall condition. In practice, the evaluated transformer condition is verified by untanking sometransformers at the repair shop in the utility for internal investigation, especially windings. This provides aresponse to an adjustment of scoring and weighting parameters in the evaluation to achieve the actual con-dition. Finally, the developed power transformer condition-based maintenance program is implementedas a decision support tool in a utility via web application in order to effectively plan the maintenance tasks.

5. CONCLUSIONS

The failure statistics of power transformers are analyzed to identify the percentage of failure and itscauses of each component. The Weibull distribution is used to determine the average lifetime of failedcomponents. The conditions of transformer components are evaluated by visual inspection, electricaland insulating oil tests. The scoring and weighting technique is applied to assess the component con-ditions whereas the important weighting factor of the components is determined based on AHP tech-nique. Combining scoring, weighting and the AHP technique, the overall condition of the transformeris assessed. The results show that by using the historical records with Weibull distribution techniques,from a total of 303 transformers, the critical component for transformers rated 115/22 kV, 50 MVA isOLTC with leakage failure and 16-year expected lifetime whereas the critical component for trans-formers rated 230/115 kV, 200 MVA is tank with leakage failure and 13-year expected lifetime.The developed method has been applied to 18 transformers, and it is found that the critical componentsare active part and OLTC. The transformer “T1” rated 115/22 kV, 50 MVA is the worst transformer

0

20

40

60

80

100

T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13

Active part

Bushing

Arrester

OLTC

Insulating oil

Tank

Protection

Overallcondition

70%

35%

Transformer condition

Transformer

Figure 10. Component and overall conditions of 50 MVA power transformers.



with suspect overall condition. With the proposed condition-based maintenance strategy, powertransformers can be effectively maintained according to its actual condition. Finally, high avail-ability and reliability, low risk of failure, lifetime extension and cost reduction for power trans-former can be achieved. The proposed method will be further applied to other high voltageequipment in Thailand.

ACKNOWLEDGEMENT

The authors gratefully acknowledge the Transmission System Maintenance Division at Electricity GeneratingAuthority of Thailand (EGAT) for data support of this work.

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25.Ahmad N, Laplante PA. Software projects management tools: making a practical decision using AHP. Proceedings ofthe 30th Annual IEEE/NASA Software Engineering Workshop SEW-30, 2006, Columbia, Apr.2006, pp.76–84.

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27.Goh HH, Kok BC. Application of analytic hierarchy process (AHP) in load shedding scheme for electrical power sys-tem. Proceeding of 9th International Conference on Environment and Electrical Engineering (EEEIC), 2010, Prague,Czech Republic, 16–19 May 2010, pp.365–368.

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APPENDIX

PAIRWISE COMPARISON BY FOUR DECISION MAKERS

As the AHP evaluation is judged by the four different groups of experts in the utility, the aggregationprocess is clarified with an example of the main criteria in the analysis. Pairwise comparison matricesfilled by the four groups (A, B, C, D) with the three criteria, i.e. maintenance difficulty (MD), failureconsequence (FC) and failure history (FH) are shown in Table A.I to Table A.IV, while Table A.Vshows the aggregated evaluation.

According to the evaluation of group “A”, the geometric mean in the first row is calculated as follows.ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1ð Þ � 1=7ð Þ � 2ð Þ3

p¼ 0:6586

The mean values in the second and the third rows as well as in other tables are calculated corre-spondingly. Subsequently, the mean values of each table are summed up as illustrated in the Σ row.The normalized weight can be determined by dividing each mean value by the sum. Note that theweight values of each table must be totally equal to 1. For the aggregation evaluation, each elementof the matrix is calculated similarly by the geometric mean technique. For instance, the aggregatedvalue of criteria “MD” against “FC” is determined from the following equation.ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

7ð Þ � 5ð Þ � 9ð Þ � 1=5ð Þ4p

¼ 2:8173

The inverse value is then determined as follows.ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1=7ð Þ � 1=5ð Þ � 1=9ð Þ � 5ð Þ4

p¼ 1=2:8173

The rest values in this aggregated matrix as well as the mean and the normalized weight aredetermined in the similar calculation as above. The normalized weights of the sub-criteria includingshutdown (D1), normal monitoring (D2), special testing (D3), repair ability (R1), spare availability(R2), knowledge (R3), acquisition cost (E1), repairing time (E2), transformer operation (O1), othercomponent (O2), other equipment (O3), social (V1) and people (V2) are calculated and summarizedin Table A.VI.In the alternative level or the power transformer components in this case, pairwise comparisons of

one component against the others with respect to each sub-criterion are performed and aggregated;for example, the comparison for transformer operation (O1) of the failure consequence is shown inTable A.VII, while normalized weights of the transformer components with respect to the failure his-tory are shown in Table A.VIII for 50 MVA transformers.The normalized weights of the transformer components by several sub-criteria for the 200 MVA and

50 MVA transformers are summarized in Table A.IX and Table A.X, respectively. Consequently, thevalues of each row are summed to achieve the important weighting of each power transformer compo-nent, whereas C1, C2, C3, C4, C5, C6, C7 and W represent active part, insulating oil, bushing, arrester,on load tap changer, tank, protective and weight, respectively.



Table AI. AHP evaluation by group A.

MD FC FH Mean Weight

MD 1 1/7 2 0.6586 0.1312FC 7 1 9 3.9791 0.7928FH 1/2 1/9 1 0.3816 0.0760Σ - - - 5.0193 1

Table AII. AHP evaluation by group B.


MD 1 1/5 1/9 0.2811 0.0629FC 5 1 1/3 1.1856 0.2654FH 9 3 1 3.0000 0.6716Σ - - - 4.4668 1

Table AIII. AHP evaluation by group C.


MD 1 1/9 1/3 0.3333 0.0704FC 9 1 5 3.5569 0.7514FH 3 1/5 1 0.8434 0.1782Σ - - - 4.7337 1

Table AIV. AHP evaluation by group D.


MD 1 5 9 3.5569 0.7514FC 1/5 1 3 0.8434 0.1782FH 1/9 1/3 1 0.3333 0.0704Σ - - - 4.7337 1

Table AV. AHP aggregated evaluation.


MD 1 1/2.8173 1/1.1067 0.6845 0.2027FC 2.8173 1 2.5900 1.9396 0.5743FH 1.1067 1/2.5900 1 0.7532 0.2230Σ - - - 3.3773 1

T. SUWANASRI ET AL.


Table AVI. Normalized weights of sub-criteria.

Main criteria Sub-criteria Sub-criteria Weight

Maintenance Difficulty, 0.2027 Detectability D1 0.0487D2 0.0190D3 0.0148

Resource R1 0.0205R2 0.0265R3 0.0164

Effort E1 0.0511E2 0.0057

Failure Consequence, 0.5743 Operability O1 0.3884O2 0.0921O3 0.0364

Environment V1 0.0431V2 0.0144

Failure History, 0.2230 - - 0.2230

Table AVII. AHP aggregated evaluation with respect to transformer operation.

Activepart Oil Bushing Arrester OLTC Tank Protective Mean Weight

Active part 1 2.63 1/2.21 1.26 1/1.57 6.90 4.86 1.64 0.1694Oil 1/2.63 1 1/4.40 1/1.73 1/3.36 4.23 2.21 0.75 0.0778Bushing 2.21 4.40 1 3.46 2 8.45 6.44 3.23 0.3333Arrester 1/1.26 1.73 1/3.46 1 1/2.45 6.19 4.16 1.23 0.1266OLTC 1.57 3.36 1/2 2.45 1 7.44 5.42 2.21 0.2284Tank 1/6.90 1/4.23 1/8.45 1/6.19 1/7.44 1 1/2 0.24 0.0246Protective 1/4.86 1/2.21 1/6.44 1/4.16 1/5.42 2 1 0.39 0.0398Σ - - - - - - - 9.69 1

Table AVIII. Normalization of weight with respect to failure history.

Number of failures WeightActive part 2 0.0426Oil 1 0.0213Bushing 12 0.2553Arrester 2 0.0426OLTC 15 0.3191Tank 6 0.1277Protective 9 0.1915Σ 47 1



Table

AIX

.Im

portantweightin

gof

transformer

componentsfor200MVA

transformers.

D1

D2

D3

R1

R2

R3

E1

E2

O1

O2

O3

V1

V2

FH

W

C1

0.011

0.005

0.001

0.002

0.003

0.002

0.027

0.002

0.066

0.008

0.003

0.004

0.001

0.000

0.135

C2

0.005

0.007

0.003

0.001

0.002

0.003

0.003

0.000

0.030

0.010

0.004

0.005

0.002

0.034

0.110

C3

0.017

0.002

0.005

0.007

0.009

0.004

0.004

0.000

0.129

0.033

0.014

0.016

0.006

0.043

0.290

C4

0.005

0.002

0.002

0.005

0.006

0.002

0.000

0.000

0.049

0.013

0.005

0.006

0.002

0.000

0.098

C5

0.006

0.003

0.003

0.002

0.002

0.005

0.010

0.001

0.089

0.023

0.007

0.009

0.003

0.034

0.195

C6

0.002

0.000

0.000

0.001

0.001

0.001

0.006

0.002

0.010

0.002

0.001

0.001

0.000

0.103

0.130

C7

0.002

0.001

0.000

0.003

0.004

0.000

0.001

0.000

0.015

0.003

0.002

0.002

0.001

0.009

0.043

Σ-

--

--

--

--

--

--

-1

T. SUWANASRI ET AL.


Table

AX.Im

portantweightin

gof

transformer

componentsfor50

MVA

transformers.

D1

D2

D3

R1

R2

R3

E1

E2

O1

O2

O3

V1

V2

FH

W

C1

0.011

0.005

0.001

0.002

0.003

0.002

0.028

0.002

0.066

0.008

0.003

0.004

0.001

0.009

0.145

C2

0.005

0.007

0.003

0.001

0.002

0.003

0.003

0.000

0.030

0.010

0.004

0.005

0.002

0.005

0.080

C3

0.017

0.002

0.005

0.007

0.009

0.004

0.003

0.000

0.129

0.033

0.014

0.016

0.006

0.057

0.302

C4

0.005

0.002

0.002

0.005

0.006

0.002

0.000

0.000

0.049

0.013

0.005

0.006

0.002

0.009

0.107

C5

0.006

0.003

0.003

0.002

0.002

0.005

0.009

0.002

0.089

0.023

0.007

0.009

0.003

0.071

0.232

C6

0.002

0.000

0.000

0.001

0.001

0.001

0.006

0.002

0.010

0.002

0.001

0.001

0.000

0.028

0.055

C7

0.002

0.001

0.000

0.003

0.004

0.000

0.001

0.000

0.015

0.003

0.002

0.002

0.001

0.043

0.078

Σ-

--

--

--

--

--

--

-1



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