CAJ | 학술논문

油中溶解气体分析(DGA)是目前电力充油设备潜伏性故障诊断的重要手段.为了克服传统BP网络及其改进诊断算法所具有的隐层节点数多、收敛时间长的缺陷,减少算法运算量及提高变压器故障诊断的正确率,提出了一种新的诊断算法:线性分类器-BP神经网络(LC-BP)故障辨识方法.通过对变压器大量过热和放电两类典型故障数据的研究,发现其DGA故障数据的特征空间线性可分且分离度较好.基于以上特性,先用线性分类器诊断过热和放电故障,然后利用两个小型BP网络分别进行进一步诊断,得到最终诊断结果.实验结果表明.提出的LC-BP算法具有良好的分类能力,故障诊断的正确率达到94%,且网络结构简单,运算量小,从而为变压器的故障诊断提供了一条新的有效途径.
유중용해기체분석(DGA)시목전전력충유설비잠복성고장진단적중요수단.위료극복전통BP망락급기개진진단산법소구유적은층절점수다、수렴시간장적결함,감소산법운산량급제고변압기고장진단적정학솔,제출료일충신적진단산법:선성분류기-BP신경망락(LC-BP)고장변식방법.통과대변압기대량과열화방전량류전형고장수거적연구,발현기DGA고장수거적특정공간선성가분차분리도교호.기우이상특성,선용선성분류기진단과열화방전고장,연후이용량개소형BP망락분별진행진일보진단,득도최종진단결과.실험결과표명.제출적LC-BP산법구유량호적분류능력,고장진단적정학솔체도94%,차망락결구간단,운산량소,종이위변압기적고장진단제공료일조신적유효도경.
The dissolved gases analysis (DGA) problem of to diagnosing the internal faults of the electrical devices filled with oil is discussed.To overcome the disadvantages of the conventional BP neural network,and to increase the diagnostic correctness rate while lessen the calculation,an algorithm based on linear classifier and BP neural network (LC-BP) is presented.Based on the analysis of DGA data from the failed transformers,the characteristic space of the DGA data can be divided into too-hot space and discharge space efficiently.The faults of whether too-hot or discharge are diagnosed by the linear classifier firstly.Then two little BP neural networks are utilized to determine the eventual faults.The simulation results show that the proposed LC-BP algorithm proposed has good ability of classification,simple structure and little calculation,while the diagnostic correctness rate reaches at 94%.