变压器故障诊断方法的研究

变压器的运行状况直接关系到整个电力系统的安全稳定运行，有效对变压器进行故障诊断具有重要的实际意义。电力变压器油中溶解气体分析（Dissolved Gas Analysis, DGA）已经成为油浸式变压器故障诊断的一种有效支持数据，本文在利用DGA数据的基础上，首先总结了常规IEC比值法的优缺点，并针对其边界问题总结了几种有效改进方法。其次，本文总结了人工神经网络，支持向量机，粗糙集，模糊数学、极限学习机、贝叶斯网络、聚类、人工免疫和petri网络等9种智能算法在变压器故障诊断中的运用，针对其固有问题总结了各自的优化方法。最后，本文介绍了以证据理论为主的综合诊断方法，分析了它优于单一智能算法的方面，并介绍了一些其他方法在变压器故障诊断中的应用。最终得出结论，相比于单一智能方法，信息融合的综合诊断办法能更好地对变压器故障进行诊断。

Research on Transformer Fault Diagnosis Method

The operation status of the transformer is directly related to the safe and stable operation of the entire power system, and it is of great practical significance to effectively diagnose the transformer. Dissolved Gas Analysis (DGA) in power transformer oil has become an effective support data for oil-immersed transformer fault diagnosis. Based on the DGA data, this paper first summarizes the advantages and disadvantages of conventional IEC ratio method. Several effective improvements have been summarized for their boundary problems. Secondly, this paper summarizes the application of artificial neural network, support vector machine, rough set, fuzzy mathematics, extreme learning machine, Bayesian network, clustering, artificial immune and petri network in transformer fault diagnosis. The inherent problems summarize their respective optimization methods. Finally, this paper introduces a comprehensive diagnostic method based on evidence theory, analyzes its advantages over a single intelligent algorithm, and introduces the application of some other methods in transformer fault diagnosis. It is concluded that the integrated diagnostic approach to i-nformation fusion can better diagnose transformer faults than a single intelligent approach.