CAJ | 학술논문

针对基于传统智能学习方法的变压器故障诊断存在训练速度慢、需调整的参数多及参数确定困难的问题，提出了基于极限学习机(Extreme Learning Machine，ELM)的变压器故障诊断方法。文中根据变压器故障的特点选取输入特征向量，分析了激活函数、隐含层节点数目对诊断性能的影响，并与基于BP神经网络和SVM的诊断方法进行了对比。实验结果表明，文中提出的变压器故障诊断方法性能明显优于BP神经网络，与SVM的诊断正确率相当，需要预先设置的参数更少，训练速度更快，更加便于工程应用。
침대기우전통지능학습방법적변압기고장진단존재훈련속도만、수조정적삼수다급삼수학정곤난적문제，제출료기우겁한학습궤(Extreme Learning Machine，ELM)적변압기고장진단방법。문중근거변압기고장적특점선취수입특정향량，분석료격활함수、은함층절점수목대진단성능적영향，병여기우BP신경망락화SVM적진단방법진행료대비。실험결과표명，문중제출적변압기고장진단방법성능명현우우BP신경망락，여SVM적진단정학솔상당，수요예선설치적삼수경소，훈련속도경쾌，경가편우공정응용。
Transformer fault diagnosis based on conventional learning methods faces some drawbacks like slow learning speed, trivial tuned parameters and difficult parameter determination. A transformer fault diagnosis based on extreme learning machine (ELM) is proposed in this paper to overcome these drawbacks. Input feature vector is selected according to the characteristic of transformer fault, and then the influence of active functions and hidden layer node number to the diagnosis performance are studied in detail. Comparison between the diagnosis based on BP neural network (BPNN) and SVM are performed, and the experimental results show that, the proposed diagnosis method is better than diagnosis based on BPNN, similar to SVM at correct diagnosis rate but more convenient to engineering application with quicker learning speed and less human tuned parameters.