特征优化和模糊理论在变压器故障诊断中的应用

Method of fault diagnosis for power transformer based on optimizing characteristics and the fuzzy theory

针对变压器故障特征与故障类型关系模糊造成的三比值法编码缺失、临界值判据缺损以及同时发生的多种故障难以区分问题,提出了基于特征优化和模糊理论的变压器故障诊断方法。将测量空间中的每种故障数据分别通过高斯核函数映射至希尔伯特空间,利用主成分分析法提取主元,以主元张成的特征子空间作为最优故障特征,据此构造该种故障下的故障测度隶属度函数,根据最大隶属度原则判断故障类型。特征子空间既保留了测量空间的故障特征,同时根据核理论维度拓展特点,又能生成更有效度量故障的新特征,从而建立最优故障特征与故障类型的一一对应关系。实例分析表明,该方法的准确率高,能够弥补三比值法的不足。通过比较故障数据对于每种故障的隶属度,能够获知诊断结果的可靠性,当多种故障同时发生时,诊断结果能够为维修人员提供有益参考。

For the problem of the lack of encoding, critical value difficult to determine and faults at the same time difficult to distinguish in the three-ratio method caused by the fuzzy relationship between transformer feature and fault types, a fault diagnosis method for power transformer based on optimizing characteristics and the fuzzy theory is proposed. Each fault data in the measurement-space is transformed to Hilbert space through the Gaussian kernel function firstly. Then, the method of PCA is used to extract fault features, which is regarded as the most effective features. Membership functions describing the fault character is built, and the type of fault can be identified according to the maximum membership degree. Feature subspace not only contains the information of fault characters from measurement space, but also generates the new characters as the ability of the kernel theory. Thus, the clear relation between fault characteristics and fault type is built. Example analysis shows that this method can make up for the inadequacy of three-ratio method. Similarities between fault data and its own fault and differences between fault data and not its own fault can be observed. When faults occur at the same time, the method can help the worker.