基于模糊粒子群算法的变压器故障自动识别①

提出了一种新的基于模糊粒子群算法的电力变压器故障自动识别方法。首先对基于油中溶解气体分析得到五种关键气体含量数据进行特殊预处理,得到识别四种故障需要的六个关键特征。然后给出了一个新的模糊聚类目标函数,在此基础上,根据已有的故障样本利用粒子群算法得到各类故障的最优聚类中心;并由此计算出各测试样本到各个聚类中心之间的距离以及相应的隶属度,最后识别出样本的变压器故障类型。测试结果显示,该方法能有效诊断识别出变压器高能放电、过热、低能放电和正常状态,精度可达92%。

Fault Diagnosis of Power Transformer Based on Fuzzy PSO Algorithm

A fuzzy particle swarm optimization (PSO) method is applied to fault diagnosis of power transformer for the first time. Content of five diagnostic gases dissolved in oil obtained by dissolved gas analysis (DGA) is preprocessed through a special data processing, and six features are extracted for fuzzy PSO algorithm. Then a new objective function is proposed for fuzzy clustering algorithm. Based on the function, PSO algorithm is trained for get the optimized clustering centers of all fault type. With the optimized clustering centers, the distantance of the testing sample to centers are calculated, and then the the membership degree is abtained. Finally, the four fault types of transformer are identified. The algorithms perform well in the testing, and the correct ratios of fault diagnosis reach an average of 92%.