高电压技术
高電壓技術
고전압기술
HIGH VOLTAGE ENGINEERING
2012年
3期
553-559
,共7页
陈新岗%田晓霄%赵阳阳%张超峰
陳新崗%田曉霄%趙暘暘%張超峰
진신강%전효소%조양양%장초봉
局部放电%模式识别%信息融合%油中溶解气体分析%对应关系%神经网络
跼部放電%模式識彆%信息融閤%油中溶解氣體分析%對應關繫%神經網絡
국부방전%모식식별%신식융합%유중용해기체분석%대응관계%신경망락
partial discharge(PD)%pattern recognition%information fusion%dissolved gas analysis(DGA)%correspondence relationship%neural network
局部放电会引起变压器绝缘的老化和破坏,而变压器局部放电特性的研究能够很好反应变压器潜伏性缺陷,对其安全可靠运行具有重要意义,因而设计制作了模拟变压器沿面放电、气隙放电和电晕放电的3种缺陷模型,采用升压法进行相应的放电试验,通过分析油中溶解气体在局部放电发展过程中的变化规律,寻找出油中产生气体与不同局部放电的对应关系。引入局部放电的最大放电量相位特征谱图Hqmax（φ）和放电次数相位特征谱图Hn（φ）,并提取基于谱图的统计特征参量,构建反向传播（back propagation,BP）神经网络和径向基函数（radical ba-sis function,RBF）神经网络对局部放电的放电类型进行初级识别,在此基础上,应用信息融合方法将初级识别结果融合油中产气特征以综合识别局部放电类型。实验结果表明,同一种溶解气体在不同放电模型中发展变化趋势是不一样的,根据统计特征参量和油中溶解气体变化规律,应用信息融合方法对变压器局部放电模式具有足够的识别能力。
跼部放電會引起變壓器絕緣的老化和破壞,而變壓器跼部放電特性的研究能夠很好反應變壓器潛伏性缺陷,對其安全可靠運行具有重要意義,因而設計製作瞭模擬變壓器沿麵放電、氣隙放電和電暈放電的3種缺陷模型,採用升壓法進行相應的放電試驗,通過分析油中溶解氣體在跼部放電髮展過程中的變化規律,尋找齣油中產生氣體與不同跼部放電的對應關繫。引入跼部放電的最大放電量相位特徵譜圖Hqmax（φ）和放電次數相位特徵譜圖Hn（φ）,併提取基于譜圖的統計特徵參量,構建反嚮傳播（back propagation,BP）神經網絡和徑嚮基函數（radical ba-sis function,RBF）神經網絡對跼部放電的放電類型進行初級識彆,在此基礎上,應用信息融閤方法將初級識彆結果融閤油中產氣特徵以綜閤識彆跼部放電類型。實驗結果錶明,同一種溶解氣體在不同放電模型中髮展變化趨勢是不一樣的,根據統計特徵參量和油中溶解氣體變化規律,應用信息融閤方法對變壓器跼部放電模式具有足夠的識彆能力。
국부방전회인기변압기절연적노화화파배,이변압기국부방전특성적연구능구흔호반응변압기잠복성결함,대기안전가고운행구유중요의의,인이설계제작료모의변압기연면방전、기극방전화전훈방전적3충결함모형,채용승압법진행상응적방전시험,통과분석유중용해기체재국부방전발전과정중적변화규률,심조출유중산생기체여불동국부방전적대응관계。인입국부방전적최대방전량상위특정보도Hqmax（φ）화방전차수상위특정보도Hn（φ）,병제취기우보도적통계특정삼량,구건반향전파（back propagation,BP）신경망락화경향기함수（radical ba-sis function,RBF）신경망락대국부방전적방전류형진행초급식별,재차기출상,응용신식융합방법장초급식별결과융합유중산기특정이종합식별국부방전류형。실험결과표명,동일충용해기체재불동방전모형중발전변화추세시불일양적,근거통계특정삼량화유중용해기체변화규률,응용신식융합방법대변압기국부방전모식구유족구적식별능력。
Partial discharge characteristics is a very good response to internal insulation of latent defects,it has very important significance of safe and reliable operation for transformer.Aiming at the discharge properties of oil-paper insulation,we designed and experimentally researched 3 kinds of experimental models simulating discharges in electrical transformers.Moreover,we tested the corresponding discharge by the boost pressure method,collected the oil-gas data and partial discharge signal to analyze the variable law of the dissolved gases in oil during the development process of the partial discharge,and found the correspondence between the gas produced in oil and different discharge models.Using statistical method extracting characteristic parameters from phase spectrogram of maximum discharge capacity and discharge frequency,we constructed the BP neural network and RBF neural network to primarily recognize the discharge type of partial discharge in transformer.Meanwhile,the information fusion method was adopted to recognize results and oil gas features.Experimental results show that,development trend of the same kind of dissolved gas in different discharge models is different,and using information fusion method with the statistical characteristic parameter and the dissolved gases has enough ability to recognize different types of partial discharge in transformers.
