基于DGA的变压器故障诊断多专家融合策略

介绍了基于油中溶解气体分析(DGA)的电力变压器故障综合诊断。采用的诊断判据主要包括改良三比值法、大卫三角形法、神经网络和范例推理。在多专家(多诊断判据)的参与下,可能出现诊断结果相互冲突的问题,而如何融合不同诊断判据下的诊断结果仍是一个难点。为解决这一问题,在把各诊断结果分解为放电和过热故障的基础上,引入了多专家加权投票策略(加权多数算法)。权重系数根据各诊断判据的诊断正确率初步确定。实践证明了该方法的可行性。

Synthetic Multi-expert in Transformer Fault Diagnosis Based on DGA

The synthetic diagnosing of transformer faults based on dissolved gas-in-oil analysis (DGA) is introduced. The multi-expert is mainly composed of three-ratio method, Duval's triangle, neural network and case-based reasoning. However, the results of multi-expert diagnosing may be inconsistent, and how to successfully integrate those diagnosing results is still pendent. The weighted majority algorithm is then adopted in terms of decomposing each diagnosis result into discharge and over-heating. Furthermore, the weighted coefficient of a particular expert is tentatively determined by its probability of correct diagnosis. Lastly, the method proposed is proved feasible in practice.