[2011] a Bayesian Network Approach to Fault Diagnosis and Prognosis in Power Transmission Systems

8/13/2019 [2011] a Bayesian Network Approach to Fault Diagnosis and Prognosis in Power Transmission Systems

1/6

A Bayesian Nework Approach to Fault Diagnosisand Prognosis in Power Transmission Systems

R C G Teive Mebe EEE, J Ce C C B C P C Ce T Lne nd L Ciin J

bact - This paper proposes an intelligent system forsolving the fault diagnosis problem in electrical powertransmission system, involving substation equipments andtransmission lines. The proposed methodology is based onBayesian Networks and this paper is focusing only on the ilusrelated to tnsmission lines. The fault diagnosis in powertransmission systems is a complex problem and its solutionusually needs a great deal of expertise and experience rlated toequipments, transmission systems and failures modes. Thedeveloped system allows not only the denition of the mostprobable cause of a detected failure, but also the prognosis ofpossible failures, when some evidences of equipment status orcauses are known. Validation tests were performed with thiscomputational model, considering realistic data from a Braziliantransmission utility. The tests have demonstrated theeectiveness of this approach conrming it as a promisingcomputational tool to the maintenance engineers.

Ind Ters Power Transmission Systems, Fault Diagnosis,Maintenance Systems, Bayesian Networks, Belief CausalNetworks.

I. INTRODUCTION

IN a deregulated environmt, electrical utilities e undconstt pressure for reducing operating costs, enhcinge equipmts availability and improving gy supply

reliability and quality for customers. Electrical gysmission ilities, picully in Brazil, have to maintainhi asst availability not only due to deregulation processes,but also to the hi demd groth, economic pressures, d

prot consaints.Wi e electric sector deregulation, electric energy is

eated not only as product but also as service. This sector issubjected to private investmts d to mket rules.Moreover, e regulatory agencies have establishedmeasrements stdds to control e product d e svicequality oered by the utilities. us, e utilities mustcontinuously improve their eciency. These new paadisd consaints have been leading to st chges in operationd system expsion sategies.

Wi regds to prot consaints, e Brazili ElecicityRegulatory Agency AEEL set forth e NormativeResolution 270/07, which dened economic penalties for lackof availability of trission nction (TF) assts.

As a result of the implemtation of this NormativeResolution, smission utilities e currently raerintested in improving maintce policies as well as

developing new techniques to estimate equipment conditis,considering mainly those having TF in e smissionsystem.

In relation to equipmt at pforms TF, smissilines play a importt role in e smission systems, sinceits magemt has gained remkable recognition, involving

technical d economic aspects. Its reliability andperformce e still of great impce to ese utilities.

In such a contet it is necessary to develop methodologiesto diaose possible causes of predened iles in eission system. On e oer hd, e computationalmodels must be exible enough to allow the proosis ofilres, since some evidences of equipmts status or causese knon.

Wi regds to e diaostic reasoning e informatireected om the equipmt is ra ncertain due to thecomplexity of the relationships existing between e viablesinvolved in is problem.

e development of computation tools to allow iluresdiaosis, aid e maintce engineers to mage e

ission system, seching for e maximization of assetsavailability, resulting in capital investment minimization.

In is sense, is study proposes an intelligt system basedon Bayesian Ntworks, for simulating e pformce ofission equipmts (in is case aission lines),taking into account e most iles modes involved,penalties for unavailability, evidences on causes or ilresd all e cause-eect relationips related to e ilre

modes of the issi system.

II. ELECTRICAL POWER TRSMISSION SYSTEMS

Fault Diagnosis Problem

e problem of ult in equipments of power

ission system has accompied ese systems sinceeir rst implemtations up to now.

Originally, the most diculty is in e clearcomprehsion of the processes that create damages, which be initiated by deterioration processes, contination or,simply, due to external causes, exposing e dielecric toconditis not compatible with its supportability, consideringor not e detioration.

Expiced maintce engineers of Power TrsmissionSystems have a lot of knowledge ad experise about ultdiaosis. is reasoning process this owledge has to be


2/6

combined with technical information. However, in esecases, most of this information is imprecise, involvinguncerainties mainly related to:

data smission err or data loss; operation conditions of the smission network; conditions at e ult time; Bad operation or non-operation of circuit

breakers d relays; Human iles caused by stress or ill-ained

operators, for instce.In is case, e alysis of ulty elements or even causes

of some outage by the engines, involves reasoning nderuncerainty. us, y technique chosen to solve this problemmust be able to model computationally these certainties.

In e literatre, e problem of ult diaosis in powersmission systems can be divided into four major topics:

Fault detection. is case the focus is on theidentication of ults in e system;

Fault classicati. e aim is to dene whichkind of ult has happen in e system;

Fault location. e interest is to detmine eulty secor in e trission network or even

e ulty equipmt in a lge blackout ea; d Fault cause diaosis, which is e focus of this

work.In spite of being as import as e oer ult diaosis

problems, e cause of some outage or ilre in equipmtsof the power trsmission systems is still less explored in elitatre.

e rst paps which adressed the ult diaosisproblem in power system [04], [06], [11], [12] e [13] werebased on the exper system technique. e problem addressedin ese works

In relation to e application of reasoning nder ncertaintymeod to e ult diaosis, e rst approach ws e use ofcertainty ctor [13]. Aerwards, zzy rules were combinedwith expert system to improve e ult alysis [02].

A zzy expert system approach was also proposed to thisproblem [08]. is case, lse operati or nonoperations ofprotective devices are analyzed, including multiple ults,togeth wi e zzy inference. e aim is also e ultseion estimation.

e ult location problem is solved in [10], by usingsupport vector machines d radial basis ncti of aArticial Neal Network. e focus of this pap is the

identication of the faulty line d nding e ult distceom e substation.In [09], e aors propose an approach based on wavelt

sform d neural network to deal wi ult diaosis insmission lines, involving detecion, classication ad ultlocation.

Wi regds to the application of Bayesi Networks toult diaosis in power system, the rst approach was

proposed in [01]. is case a pre Bayesian network wasproposed. Aerwards, a special Bayesian Network wasproposed in [05], whe e ppose is to estimate e ulty

seion of a power trsmission system, involving ultyequipmts of a blackout area, such as: line, sform orbusbar.

e most applications in e litatre e related to eult location problem, d e ulty setion estimation in e

power trsmission system. genal, e cause of ult isnot adressed in e literatre, as it was presented in issection.

Since e diaostic process is inductive reasoningprocess in natre, it is desirable to apply an inexact reasoning,as it is pointed out in [08]. ct, e diaosis process worksom some evidences (particul observations or states)

towards e cause of the problem, which is a galization.hen a probabilistic reasoning is necessary in e

diaosis process, a new kind of reasoning is involved, calledabductive. Actually, this is e main reason y deductive,like a pre expert systems, or nmerical algimicapproaches e hd to be applied to is subjec. us, it isproposed in is pap a Bayesian Ntwork approach to solvee ult diaosis in power smission system.

B. e Diagnosis Problem

Model-based diaosis conces for modeling of thestrure d behavior of a system or device in order toestablish why the system or device is malnctiing.Traditionally, little atttion has been given to the problem ofdealing wi certainty in model-based diaosis [17].

Diaosis of a malnctioning physical system is e taskof identiing ose component ps whose iles are

responsible for discrepcies between observed and correctsystem behavior. e tpical reason for performing diaosisis to repair e system by replacing failed ps wi workingones so that e system can r to its normal operation.

[18].

Human diaosticis e very good at utilizingexperitial owledge to map smptoms direly to ilures.Smptom-based diaostic systems to achieve e secompetence level as hum diaosticis by using esediret associatis. [18]

e diaosis system has to meet certain performcecritia, of which e most impt e [19]:

Accuracy a diaosis is accurate if e set of suspectedults contains e ue ult;

Resolution e ability of the diostic system to

provide e minimal nmber of ults cdidates (ideallyone) on e basis of available informati om esystem;

Sensitivity the ability to detect small ult which causesonly slight sial chges;

Diaostic stability a stable diaosis results in a stableset of ult cdidates in spite of chging systemconditis;

Reliability - the diaostic system should provideaccurate diaosis for all ults, including ticipatedults for which e is no past expice.


3/6

In e elecical smission systems, consideringuncertainties related to data d equipments, e patt ofdiaostic reasoning involves a probabilistic process. In thiscase, abductive reasoning is necessy to model this problem,

where, e soluti process starts om some evidencestowards e probable cause.

Abduction is dened as e process of generating aplausible explation f a given set of observations or s,

d in e context of probabilistic reasoning, abductiveinference corresponds to nding e maximm a posteriori(MAP) probability state of the system's variables, given someevidence (observed variables). [20].

In abductive diaosis, tpically a causal model ofabnormal behavior is used to explain observed abnormalndings in terms of a given causal eory. [17].

Normative Resolution /

e Brazili Electricity Regulatory Agency AEELset for e Normative Resolution 270/07 in 2007, whichdened economic penalties for lack of availability of

smission nction (TF) assets. e main aim of thisresolution is to stimulate e quality of energy trsmissionservice. is mechism is based on e reduction of the

unavailability of TF assets, including ission lines,sforms d voltage conol equipmts.

Basically, this resolution seeks to motivate thesmission utilities to improve the magemt of theirelectrical system plning processes, as well as, e operationd maintce procedures. e main tget is e sech for

e mimization of FT availability and, at e se, ereduction of ults at produce problems with e operationof the elerical system ad e service quality.

In according with is Resolution, e is a performceindex, called PVI, which regulates e smission svice inBrazil. e PVI denes e economical value of penalties dueto unavailability of trission assets (trsmissionnctions). is case, e considered e following

smission nctions: Trsmission lines (TL) Trsformation ction (TR) Reacive Conol (RC).

In Figes 2 d 3, it is possible to see the relationipsbetween the variables TL, TR, RC ad PVI in e developedcausal nwork.

III. BAYESIAN NETWOS

Bayesian Ntworks (BNs) oer a natal amework fordealing wi ctainty in abductive reasoning problems[07]. e reason for is is easy to undstd: as BN can begiven a causal intpretati, ere exists obvious

relatiship between abducive reasoning in qualitative causalmodels d diaostic reasoning in BN. [17].

BN are equtly used as the kel of Probabilistic ExptSystems, because they provide an ecient representation of

e joint probability distributi, d allow us to calculateprobabilities by means of local computation, i.e., only relevant

information is consided wh a probability has to becalculated. [20]

e basis of BNs is formed by probability theory and apheory. Random variables can be represented as nodes in adirected acyclic graph (DAG), whereas conditionaldependencies e represented as aph edges. A BN, whosegraph suctre oen reects a domain's causal sucte, is acompac representation of a joint probability table if its graph

is relatively spse. [14]BNs represent multiviate probability distributions d eused for reasoning d leing nd ncertainty [07]. Theye powerl tools in solving problems at encapsulate

uncrtainties d able to me eective inteation of multiknowledge of the expts domain. Moreover, BNsdemonsate e probability cause-eect relations between

rdom viables as well as e extt to which ey areaected. [15].

A BN consist basically of [03]:

A set of variables and probabilistic relationipsbetween em represented by directed edges Each viable has a nite number of mutuallyexclusive states e variables d e direced edges form a directedacyclic graph To each variable A with pts B" B, B, a

potential table P(AB" B, B) is attached, whichmes, given events B" B, B e probability ofe event A is x.

By analzing e diaosis problem, it is possible to inferat e expice d knowledge of experts domain c beused to design e strctre d e initial values of therelatiip coecients between y two causal nodes of thecausal ntwork [21], d e nal appropriate values c be

revised rou ling algims.Abdutive inference in BNs, also knon as the mostprobable explation problem (E), correspond to ndinge Maximum a Posteriori (M) probability state of thenetwork, given e observed variables (e evidence). amore formal way: if X is the set of observed variables dX is the set of unobserved variables, we aim to obtain econgurati Xu of X such at: [20]

Xu = rgmx Pxlxo'u

Where X = Xo is e observed evidence. Usually, Xu isknon as e E. It is well knon at e E be

found using probabilities propagation meods, but replacingsummation by maxim in e mginalization operator (due

to the disibutive property of maximm with respect tomultiplication).

e main reason for using a probabilistic reasoning in isproblem is at usually experts ink in probabilities in ecausal directi (prognosis problem), which mesP(system=ilcomponent=bad) d not in e diaosticdiretion, which mes P(compont=badstem=il). [16].However, in is work, both approaches (diaosis d

proosis reasonings) e emulated into a BN.


4/6

IV. PROPOSED THODOLOGY

e problem of fault cause diaosis in elecicalsmission systems is tackled in is work, by using a BN.Furermore, e developed BN allows the reasoning in bothdirections (diaostic ad proostic reasoning), dependingwheth e evidences (observed variables) e on the problemor on e conditions of the smission system.

e causal network, which is basis of the developed BN, isdepicted in Figre 2 (APPENDIX). Since this paper is due toa project that is still nder development, only the failemodes related to trsmission lines were mapped d emodeled in the BN.

In relation to Trsmission Lines, e following failemodes were mapped (viables in een in Fige 2):

Windstorm Forest re Vegetation Lighting dischges Problems with isolators

Switching overvoltage Problems with bays of trsmission (switching off

modules)

e bays of transmission lines considered in is work erelated to the following equipments: circuit-breakers, crtsforms d capacitive voltage sformers, sgeresters d shnt reactors.

e meodology proposed for solving both diaosis dproosis problem in electrical smission systems isshowed in the owch of Fige 1.

TopologicaInformation

of the inteestedSubestaton

AutomaticConstucton ofthe Bayesan

NetwokYes

Inut of FautyEqupment

Yes

The evdence sset n theBayesanNetwwork

No

Evdences onfalue modes

The most obablecause s suplie

by the system

The most obablePrognoss of the

system is suplie

The mpact on VIIs cacuate

Fige 1 Flowcht of the oposed Method

It is import to observe in Figre, e BN is consuctedautomatically by the system, considering e tpe ofsubstation involved (topology, number of bays andequipmts per bay). en, the BN is able to ow whichequipmts c to switch off a trsmission line, when thisequipment has a teinal faile, chactizing a switchingoff smission module (bay).

V. CASE STDY

In relation to the failre modes of trsmission lines, ept associated wi the switching off bays is still nderdevelopment. All e occurrences involving the equipments

related to e switching o bays ae gaered into the variableProblems with Bays e performce index of the system(PVI) is not considered in e BN of Figure 3.

e failre modes of trsmission lines in Figre 3, wererepresented in een into the BN. e prior probabilities dposterior probabilities (conditional probability), presented inFige 3, were calculated om data obtained om e

maintence data base of the electrical energy trsmission

utility.e BN shon in Figre 3 is not complete. is necessary

to elicit e probabilities related to missing viables in Fige3. this case, the data acquisition is going to be executedwith e compys experts, including the probability ofvariable TL.

Since e BN is not complete, it is not possible to obtaincoherent results om simulations, involving evidence

propagation throu the developed BN; or even the deitionof failre cause diaosis. this sense, Fige 3 is presented

just for giving a exaple of the probabilities in the BN.e number of trsmission lines existing in e

maintence data base, used in is work, is presented in

Table 1.

Table I Nmber of Trsmission Lines

Voltage Level (kV)

230

345

440

Number of TrsmissionLines

51

43

58

Analyzing e BN of Figure 3, especially in relation to the

probabilities, it is possible to observe that e viableLightning discharges is e failre mode with e highestimpact on e variable (Transmission Line) mes atabout 74% of the knon failres, which switch a smissionline o, are related to liing dischges, in is case.

Wi regds to the viable Problem with Insulator, it ispossible to observe in Fige 3, e variable vandalism is theunique cause of failure of insulator. O e other hd, bosalt air d pollution do not have inuence on e problems

with insulators of power ission line towers, consideringe data of maintence data base.


5/6

VI. FIN ES

In power smissi systems, considering nctaintiesrelated to data d equipmts for instce, e patt ofdiaostic reasoning involves a probabilistic process. In iscase, abductive reasoning is necessay to model this problem,

where, e soluti process starts om some evidencestowards e probable cause.

Since Bayesian Networks oer a natral amework fordealing wi nctainty in abdutive reasoning problems, it is

proposed in is pap approach based on BayesiNtwork for solving e ult diaosis problem in powersmission systems.

is paper focuses on the modeling of the BayesianNetwork, seching for the ilre modes, which haveinuence on e smission lines operation. e mostprobabilities has calculated om e data base, however,it is still necessay to obtain some conditional probabilitiesom compys experts.

e advtage of this approach is Bayesian Network allowsboth kind of reasoning: diaostic d proostic one,depending whe e evidences e set on ilre modes oron e smission line vaiable.

Despite this computational system is still nddevelopment, is proposal seems to be very promising todiaose possible causes of predened iles in esmission system. O e oth hd, is approach isexible enough to allow the prognosis of ilures, since someevidences of equipments status or causes e knon.

EFEENCES

[01] CHEN, C. 1. and TLUKDAR, S. N. "Causal Nets for Diagnosis.

In Proc. 4th Symposium Expert Systems Application to PowerSystems. Melboue, jun. 1993.

[02] CHO, H. J. and PRK, J. R. " An Expert System for Fault SectionDiagnosis of Power System Using Fuzy Relations. IEEE Trans.On Power Systems. Vol. 12. No I. Feb. 1997.

[03] JENSEN, S. V. Bayesian Networks and Decision Graphs. Springer.2001.

[04] YONGLI, Z. et ali. " An Expert System for Power System FaultAnalysis. IEEE Trans. On Power System. Vol. 9. No I. Feb. 1994.

[05] YONGLIM, Z. ; LIN, H. JINLING, 1. " BayesianNetworks Based Approach for Power Systems Fault Diagnosis.IEEE Trans. On Power Delivery. Vol. 21. No 2. April. 2006.

[06] PRK, Y. M.; KIM, G. W. and SOH, J. M. " A Logic Based

Expert System (LEBS) for Fault Diagnosis of Power System. IEEETrans. On Power Systems. Vol. 12. No I. Feb. 1997.

[07] PERL, J. Probabilistic Reasoning in Intelligent Systems: Networksof Plausible Inference. San Mateo, CA: Morgan Kauann. 19.

[0] LEE, H.; PK, D.; , B.; PARK, Y.; PRK, J.; VENTAKA,S. S. A Fuzy Expert System for the Integrated Fault Diagnosis.IEEE Trans. On Power Delivery. Vol. 15. No 2. April. 2000.

[09] SIL VA, K. M.; SOUZA, B. A; BRITO, N. S. D. Fault Detectionand Classication in Transmission Lines Based on WaveletTransform and N. IEEE Trans. On Power Delivery. Vol. 21. No4. October. 2006.

[10]

[II]

[12]

[l3]

[14]

[15]

[16]

[17]

[1]

[19]

RVIKUMAR, B.; T, D.; KHINCHA, H. P.Application of Support Vector Machines for Fault Diagnosis inPower Transmission System. ET Gener. Transm. Distrib. 200.

FUKUI, C. ; KAWAKMI, J. Na Expert System for Fault SectionEstimation Using Information om Protective Relays and CircuitBreakers. IEEE Trans. On Power Delivery. Vol. PWRD-1. No 4.October. 1996.

TOMSOVIC, K.; LIU, C. C.; ACKERMN, P.; POPE, S. AnExpert System as a Dispatchers' Aid for the Isolation of Line

Section Faults. IEEE Trans. On Power Delivery. Vol. 2. No 3. July.197.

PK, Y . M.; LEE, H. J. An Expert System for the Fault Diagnosisin Power Systems. In Proc. Of IFAC on Power System and PowerPlant Control. Korea. August. 199.

NGSHOEL, O. J.; CHAVIRA, M.; CASCIO, K.; POLL, S.;DRWICHE, A; UCKU, S. Probabilistic Model-based Diagnosis:An Electrical Power System Case Study. IEEE trans. On Systems,Man and Cybernetics. Part A: systems and humans. Vol. 40. No 5.September. 2010.

TIN, Z. G.; NG, X Y.; ZHNG, H. F. Aplication ofBayesian Theory in Fault Diagnosis of Turbo-generators. IEEETransmission and Distribution Conference. Asia and Pacic. China.2005.

ROLIM, J. G.; MAIOLA, P. C.; BAGGENSTOSS, H. R; PAULO,A R. G. S. Bayesian Networks Application to Power TransformerDiagnosis. Powertech . Lausanne. 2007.

LUCAS, P. J.. F. Bayesian model-based diagnosis. InternationalJoual of Aproximate Reasoning. Elsevier.No 27. 2001.

CHEN, J. A probabilistic theory of model-based diagnosis.International Journal Human-Computer studies. 1994.

CHNTLER, M. J.; WYATT, T. K.; LDEA, A; MCGUNIGLE,G. A taxonomy of user requirements for model-based diagnosticsystems. In: Proc. 14th IFAC World Congress. Beijing. 1999.

[20] DE CAOS, 1. M.; GS, J. A; MORAL, S. Partial abductiveinference in Bayesian belief networks by simulated anneling.International Journal of Approximate Reasoning. Elsevier.No 27.2001.

[21] TEVE, R. C. G.; COELHO, J.; LNGE, T.; LCIO, J. C. M.;FENNDES, R. C.; BACHMN, J.; VARELA, C. DistributionMaintenance Management by Using Bayesian Network. IEEETransmission and Distribution Conference. Latin America. SoPaulo. 2010 (in portuguese).


6/6

o fest fireYes

P lm wh 8y

Swch ig vrvo lagYeso

o t in stomfuetRa

APPENDIX

f requentRae

g m_ /hJs u

P Yeso

Vdl

L igtning icag o

Prb lm wh 8ays

Figre 2- Causal Network Trsmission Lines

Figre 3 Causal Network Exple of Probabilities

[2011] a Bayesian Network Approach to Fault Diagnosis and Prognosis in Power Transmission Systems

Documents

Transcript of [2011] a Bayesian Network Approach to Fault Diagnosis and Prognosis in Power Transmission Systems