科技通报
科技通報
과기통보
BULLETIN OF SCIENCE AND TECHNOLOGY
2015年
8期
272-276
,共5页
径向基神经网络%变压器%故障诊断%仿真
徑嚮基神經網絡%變壓器%故障診斷%倣真
경향기신경망락%변압기%고장진단%방진
RBF neural network%transformer%fault diagnosis%simulation
研究基于径向基神经网络的变压器故障诊断方法.以绝缘油中6种特征气体作为神经网路的输入,建立了可对变压器低温过热、中温过热、高温过热、低能放电、高能放电和局部放电等6种故障进行故障诊断的径向基神经网络模型.仿真实验研究表明,基于径向基神经网络的变压器故障诊断模型对于超出三比值法编码规则的故障也能进行故障诊断,故障诊断准确率达到91.67%,远远高于三比值法故障诊断准确率.基于径向基神经网络的故障诊断模型建立方法简单,便于在实际中应用.
研究基于徑嚮基神經網絡的變壓器故障診斷方法.以絕緣油中6種特徵氣體作為神經網路的輸入,建立瞭可對變壓器低溫過熱、中溫過熱、高溫過熱、低能放電、高能放電和跼部放電等6種故障進行故障診斷的徑嚮基神經網絡模型.倣真實驗研究錶明,基于徑嚮基神經網絡的變壓器故障診斷模型對于超齣三比值法編碼規則的故障也能進行故障診斷,故障診斷準確率達到91.67%,遠遠高于三比值法故障診斷準確率.基于徑嚮基神經網絡的故障診斷模型建立方法簡單,便于在實際中應用.
연구기우경향기신경망락적변압기고장진단방법.이절연유중6충특정기체작위신경망로적수입,건립료가대변압기저온과열、중온과열、고온과열、저능방전、고능방전화국부방전등6충고장진행고장진단적경향기신경망락모형.방진실험연구표명,기우경향기신경망락적변압기고장진단모형대우초출삼비치법편마규칙적고장야능진행고장진단,고장진단준학솔체도91.67%,원원고우삼비치법고장진단준학솔.기우경향기신경망락적고장진단모형건립방법간단,편우재실제중응용.
According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults:low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67%which is also much higher than the traditional three ratio method. At the same time, RBF neural network model is easy to establish and applicable to use.
