The Condition Based Maintenance Evaluation Model on Onpost Vacuum Circuit Breaker

Transcription

1 Avalable onlne at Systems Engneerng Proceda 4 (202) The 2 nd Internatonal Conference on Complexty Scence & Informaton Engneerng The Condton Based Mantenance Evaluaton Model on Onpost Vacuum Crcut Breaker Cu Yanbn a, Cu Bo b,a a Department of Mechancal Engneerng North Chna Electrc Power Unversty, Baodng, 07003, Chna b Department of Electrcal Engneerng North Chna Electrc Power Unversty, Baodng, 07003, Chna Abstract The safe operaton of power supply equpments s closely related to the securty of electrc network. The planned mantenance of exstng power equpments o cannot meet the needs of development of power system. To solve the problems n mantenance for vacuum crcut breaker, ths paper buld the equpment condton and rsk assessment ndex system and brng out the outdoor on-post vacuum crcut breaker condton based mantenance evaluaton model whch based on Rough Set and Support Vector Machne accordng to the real condton. To prove the hgh accuracy of ths method, a research whch about the data of 00 Box-type substaton n the dstrbutng network of one power supply company s conducted n ths paper Publshed Publshed by by Elsever Elsever Ltd. Ltd. Selecton Selecton and and peer-revew peer-revew under under responsblty responsblty of Desheng of Desheng Dash Dash Wu Wu. Open access under CC BY-NC-ND lcense. Key words::condton Based Mantenance;On-post Vacuum crcut Breaker;Evaluaton model;rough sets; Support Vector Machne;. Introducton In recent years, on-post crcut breaker n the electrc power equpments, as dstrbuted protectve equpment, has been wdely appled n the transformaton of dstrbuton systems. Our out-door on-post crcut breakers manly rely on vacuum crcut breaker whch has ruled the on-post swtch market. Functons of power users to the vacuum crcut breaker manly embodes n overcurrent or short crcut protecton. Therefore, when power users operate the vacuum crcut breaker, they should not only safeguard the operaton of equpments but also concern other factors affectng equpment condton and rsks. Ths paper constructs the on-post crcut breaker condton and rsk assessment ndex system and brngs out the equpment condton and rsk assessment model based on Rough Set Attrbutes Reducton and Support Vector Machne Classfcaton. In ths model, the Rough Set s used to fll up a defcency of Support Vector Machne n cuttng down the redundant nformaton, whle the Support Vector Machne * Correspondng author. Tel.: ; fax: E-mal address: cuybsw@26.com Publshed by Elsever Ltd. Selecton and peer-revew under responsblty of Desheng Dash Wu. Open access under CC BY-NC-ND lcense. do:0.06/j.sepro

2 Cu Yanbn and Cu Bo / Systems Engneerng Proceda 4 (202) can fll up a defcency of the Rough Set n generalzaton ablty. To prove the effectveness of the method, an emprcal research based on real data s adopted, ndcatng hgh classfcaton accurate rate. 2. Equpment condton and rsk evaluaton ndex system Constructng a set of scentfc and mproved evaluaton ndex system s a sgnfcant premse of evaluatng equpment condton and rsk evaluaton and the base of makng comprehensve evaluaton. On the characterstcs of the on-post vacuum crcut breaker, ths paper bulds an ndex system (Table.) consstng techncal parameter, operaton securty, protectve functon and relablty through analyzng varous affectng factors of the equpment condton and rsk. Table Equpment condton and rsk evaluaton ndex system Frst-grade ndexes Techncal parameter ndexes Operaton securty ndexes Protectve functon ndexes Relablty ndexes Second-grade ndexes Swtch n/on poston ndcaton P Temperature of gude pole juncton P2 Temperature of scaffold s auxlary equpments P3 energy storage poston ndcator p4 Seal desgn dfferenccesp5 groundng resstance P6 Swtch tself corroson P7 defecton of Pole s number plate P8 slope of Scaffold P9 Crack of pole P0 Accumulated short open crcut tmes P Unsmooth stagnaton of sprng mechnsmsp2 Groundng connectons P3 Out-taken solaton toolp4 nrush current defense functonp5 Voltage transformer nstallaton methodp6 Surge arrester nstallatonp7 length of Operaton P8 Famly defect P9 Hstorcal defect P20 3. Attrbute reducton based on the rough set Not all of the ndexes selected are very mportant, n whch some attrbutes are redundant. In condton of keepng attrbute condtons unchangng, attrbute reducton s removng the ones not related or not mportant. If posc( D j) pos { } ( D j) ( pos c a c( D j) ) ' redundant, c c { a} = ( a c ) s c Postve feld of a and a s = wll be a reducton of c.

3 84 Cu Yanbn and Cu Bo / Systems Engneerng Proceda 4 (202) The condton attrbutes are nterconnected n a decson system. Reducton can be consdered as dependency and assocaton of concluson attrbutes to condton attrbutes set n a smple way wthout losng any nformaton. The more sgnfcant the attrbutes are, the greater the nfluences of attrbutes are on the dvson of polcy[][2][3]. 4. Prncple and algorthm of Support vector machne Support Vector Machne, based on forward network structure and settng up a hyperplane as decson camber, manly make pros and cons more nclne to the lateral edge of nterval lne. Error rate of test data on machne learnng makes the sum of tranng error rate and Vapnk Chervonenks dmenson bound. In dvson mode, Support Vector Machne s zero for the prevous value, and mnmal for the second. It possesses excellent generalzaton ablty on pattern classfcaton. The basc thoughts can be shown by 2D case of fgure. In fgure, the two knds of sample square, expressed by trangle and square, can be dvded by hyper planes H. The dstance, between H and H2, s called classfcaton nterval. The optmzng classfcaton hyperplanes request both correct separaton and bggest classfcaton nterval.[4]-[8] H H H 2 SV nterv fg : the optmal classfcaton face for lnear Separablty For the Non-lnear classfcaton, the optmal classfcaton hyperplanes can be ganed n transformaton space through nvertng nonlnear transformaton nto a hgh lnear dmensonal. The nner product of kernel functon K( x, x ), whch meet the Mercer`s condtons, realzes lnear classfcaton of nonlnear j transformaton. Then the problem s lookng for maxmzng objectve functon n n n Q( α) = α αα yyk( x, x) j j j = 2 = j= () n { } α = s the Lagrange coeffcent. Classfcaton functon can be ganed by solvng constrants n f( x) = sgn α yk ( x, xj) + b = (2)

4 Cu Yanbn and Cu Bo / Systems Engneerng Proceda 4 (202) That s Support vector machne Radal base kernel functon s chosen n ths paper: 2 x x Kxx (, ) = exp 2 2σ (3) 5. Emprcal study 5.. data collectng and processng The data n ths paper comes from 00 Box-type substaton n the dstrbutng network of one power supply company. Wth the purpose of makng orgnal nputtng equpment data meet requrements of the model, the nputtng vectors attan standardzaton and are sutable for the model through preprocessng. Man steps of revsng the orgnal data of 00 on-post vacuum crcut breaker are the followng: Frst, make a data collectng table, that s to collect orgnal data of every on-post vacuum crcut breaker and those that are unable to collect nformaton can t be evaluated, and hence delete ncomplete nformaton sample (the seral number s 580 belongng to A-lster branch equpment of red-star street) wth 99 samples left. Next, process all the orgnal data of equpment and make them postve vector accordng to the followng formula: j j T x x ' = T (4) In the above formula, j x ' s the revse value, T s the total score, j x s the deducted score. Take number Z732 vacuum crcut breaker belongng to Hua Ln dstrct as an example, the postve vector nformaton s obtaned as s shown n table 2 Table 2 postve vector nformaton of Z372 on-post vacuum crcut breaker ndex postve vector ndex postve vector ndex postve vector ndex postve vector Attrbutes dscretzaton For better makng study, we randomly take out 20 equpment data as the selected sample for ths study. {, 2, 3, 20} Take U as the scope, that su =, whch ndcates a set of all the sample unt attrbutes wth one attrbute representng an ndex.

5 86 Cu Yanbn and Cu Bo / Systems Engneerng Proceda 4 (202) Dscretzaton ndex can be acheved by applyng FCM Cluster Algorthm, whch s supported n the Matlab fuzzy logc tool box. Wth the ad of Matlab, every attrbute value n the above fgure can be clustered and be dvded nto sx categores accordng to the above scores. Gve sample attrbutes, 2, 3, et al characterstc values respectvely because there are only 3 score values n the object of study wthout 0.4 and Attrbutes reducton Make reducton of ndex usng Rosetta (Verson.4.4) rough set Algorthmc Program and use characterstc value processed wth the dscrete way as the nput data. The reducton of attrbutes algorthmc manly conssts of Genetc Algorthm and Dynamc Reducton Algorthm, and makng reducton of ndex by combnng the two algorthms usng programs s effectve than usng one sngle algorthm. Ths paper adopts Johnson s Algorthm to make attrbute reducton for the decsve data and the reducton set {swtch n/on poston ndcaton, temperature of gude pole juncton, temperature of scaffold s auxlary equpments, groundng resstance, defecton of Pole s number plate, slope of Scaffold, accumulated short open crcut tmes, Surge arrester nstallaton}s acqured. Use the reduced attrbutes as the nput of supportng vector machne, and make tranng and test Classfcaton evaluaton of SVM Based on classfcaton requrements of Support vector machne, equpments of No.2-00 are chosen as tranng samples for Support vector machne, and No.-20 as test samples of evaluaton. Accordng to the requrements of equpment condton based mantenance of State grd companes, we set the evaluaton standard for the on-post vacuum crcut breaker as follows: {normal, notce, abnormal, serous}. Tranng set ncludes 59 normal samples and notce samples and 4 abnormal samples and 5 serous samples, whle test set ncludes 8 normal samples and notce samples and abnormal sample and 0 serous samples. Fast classfcaton machne structure, as s shown n fgure 2, s desgned wth two support vector machnes accordng to the requrements of classfcaton numbers. samples SVM SVM2 - - serous abnormal notce normal Fg 2: structure chart of fast classfcaton machne Tranng processes are as follows: SVM and SVM2 are parallel-structured support vector unts; Samples are classfed by parallel structures, and table 3 shows the classfcaton rules. Table 3 Reducton ndex

6 Cu Yanbn and Cu Bo / Systems Engneerng Proceda 4 (202) ndex normal notce abnormal serous result Radal bass functon maps the data to hgh dmenson characterstcs space non-lnearly, thus t takes effectve acton on feature varables and classfcaton varables when they are non-lnear. Parameters of RBF are less, whch lead to smplfyng the complexty of model choce. Due to less dfferences of RBF, we choose t as kernel functon of SVM, n whch δ s the wdth of RBF functon and the optmal value s The process of usng SVM algorthm software s as follows: nput test set to SVM by predesgned order {8 normal samples, notce sample, abnormal sample,0 serous sample }, then output s: {+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,-} Input test set to SVM2 by predesgned order, and the output s {+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,-,+} Evaluaton results {8 normal samples, notce sample, abnormal sample,0 serous sample}s ganed by correspondng two groups of output results to classfcaton rules shown n table 4. In addton, the method of makng classfcatons by SVM classfer through usng the same tranng samples as are used n makng ndex reducton by rough set can be evaluated by comparson. The evaluaton result s shown n table 4 Table 4 Classfcaton results contrast Class RS-SVM SVM tradton excellent 90% 85% 70% good 5% 0% 25% qualfed 5% 5% 5% precson It proves that SVM has good classfcaton effect n equpment state and rsk assessment. What`s more, t s found that tranng speed of ths software for tranng samples s fast. Usng less samples to Predct unknown samples explans that the method has much stronger actual applcaton prospect. 6. Concluson It s can be seen from the expermental results that faults of on-post crcut breaker caused by swtch on-off poston ndcaton, temperature of gude pole juncton, temperature of scaffold s auxlary equpments, groundng resstance, defecton of Pole s number plate, slope of Scaffold, accumulated short-crcut breakng operatons tmes, and Surge arrester nstallaton are the most common. Correspondng overhaul countermeasures are ntroduced n ths paper through evaluaton studes so that the overhaul procedures of on-post crcut breakers are optmzed enablng equpments well mantaned tmely, whch s of mportant sgnfcance to secure operaton of power supply enterprse.

7 88 Cu Yanbn and Cu Bo / Systems Engneerng Proceda 4 (202) The expermental results show that applyng the model of Rough Set and Support Vector Machne to condton based mantenance evaluaton of on-post crcut breaker solves dmensonal problems by buldng non-lner mappng relatons based on lmted tranng samples. Ths algorthm s smple and possesses hgh accuracy and t can meet the needs of practcal use. It provdes an effcent tool for the equpment condton and rsk evaluaton and t s a used reference for accurate comprehensve evaluaton wth less data samples. Reference []Zarko W. Introducton to the specal ssue on rough sets and knowledge dscovery [J]. Computatonal Intellgence,995,(2) [2]Zhang Wen-xu, Wu We-zh, Lang J-ye. Rough set theory and method[m].bejng Scence press,200 [3] Pawlak Z. Rough sets[j]. Internatonal Journal of Informaton and Computer Scence, 982, : [4] Nelllo Crstann, John Shawe-Taylor. An Introducton to Support Vector Machnes and Other Kernel-based Learnng Methods[M]. England: Cambrdge Unversty Press, 2000 [5] V.Vapnk. The Nature of Statstcal Learnng Theory [M].New York:Sprnger-Verlag,999 [6] L Jan-Png, Xu We-xuan, Lu Jng-l. The Study of Support Vector Machne n Consumer Credt Assessment. Systems Engneerng [7] Wang Qang; Shen Yong-png; Chen Yng-wu. Model and Algorthm for Multple Attrbute Decson Makng Based on Support Vector Machne[J], Control and Decson, 2006,2(2): [8] Zhu Yong-sheng, Zhang You-yun. The Study on Some Problems of Support Vector Classfer. Computer Engneerng and Applcatons