电力变压器BP神经网络故障诊断法的比较研究

将BPNN应用于电力变压器故障诊断,并对变压器绝缘油常用的5种溶解气体分析标准进行了神经网络效率的比较研究。这些标准是改进的Rogers,IEC,Doernenburg,Duva和CSUS。研究显示,所运用的诊断标准或方法不同,神经网络诊断电力变压器故障的效率也不相同,其值在88.3%～96.7%范围内;根据这些标准所设计的四比值法(FGR)和6种特征气体法(SKG)具有更高的诊断效率。验证结果显示,BP神经网络诊断法适合于变压器潜伏性故障的诊断。

Research on Power Transformer Fault Diagnosis with BPNN Method

Back propagation neural network is applied in transformer fault diagnosis. And a comparative study of neural network efficiency is also presented according to five diagnosis criteria commonly used for dissolved gas analysis in transformer insulation oil. These criteria are modified Rogers, IEC, Doernenburg, Duval and CSUS. The study shows that the efficiency of diagnosis is different when the criteria or methods under consideration are different, with values in the range of 88.3%～96.7%. The four gas ratios (FGR) method and six key gases (SKG) method based on the criteria are designed, reaching the highest efficiency. The verification shows that the BPNN diagnosis method is fit for the potential fault diagnosis of transformer.