采用GVWFCM算法的变压器故障诊断

Oil-immersed Transformer Fault Diagnosis Based on GVWFCM Algorithm

以变压器的DGA数据为初始特征向量,提出了一种基于遗传算法的动态模糊c均值聚类算法,建立新的交叉算子和变异算子以适合变长遗传编码,使用FCM局部优化算子,加强了遗传算法的局部寻优能力,提高算法效率和求解质量。实验表明该算法能有效地提高变压器故障诊断效率。

This paper proposes a genetic algorithm-based dynamic fuzzy c-means clustering algorithm which takes DGA data as initial feature vector, establishes a new crossover operator and mutation operator for variable length genetic coding, strengthens the genetic algorithm local optimization capability by using the coding FCM local optimization operator, and improves the efficiency and solution quality. Experiments show that the proposed algorithm can effectively improve the efficiency of the transformer fault diagnosis.