基于组合神经网络模型的电力变压器故障诊断方法

Fault Diagnosis of Power Transformer Using a Combinatorial Neural Network

对故障空间的划分以及组合神经网络的构造方式 ,是利用组合神经网络进行变压器故障识别的关键。在讨论变压器故障空间划分方法及其存在问题的基础上 ,针对已积累的故障变压器的大量溶解气体数据 ,考察了各类故障的气体特征及聚类分析结果 ,并在此基础上构造了组合神经网络分层结构模型 ,实现了对变压器故障由粗到细的逐级划分 ,以提高诊断的准确性 ,为制定维修策略提供了依据。最后 。

Methods of fault classification and organization of combinatorial neural network(CNN)are keys to the diagnosis of power transformer faults with CNN.In this paper,based on the discussion of fault classification methods and a cluster analysis of dissolved gas data of thirteen usual transformer faults,a CNN is introduced to realize the multi resolution recognition of the insulation faults,which not only can make the fault diagnosis be more exace,but also is helpful to establish a significant strategy for the repair work.Finally,the recognition results show that this model is effective.