作者:代子阔 ， 赵庆源 ， 李 飞 ， 翟 兴 ， 赵帅科 ， 谭玉华
Authors:DAI Zikuo , ZHAO Qingyuan, LI Fei , ZHAI Xing , ZHAO Shuaike , TAN Yuhua摘要:变压器油中溶解气体分析(DGA)是识别变压器的故障类型的 一 项重要技术 ,模糊聚类是 一 种有效的 分析手段 。但传统模糊聚类算法存在对随机初始化的聚类中心敏感、隶属度函数的有效度量范围较小使其容易陷 入局部极值点的问题 ,因而实际分类效果不佳 。针对传统 FCM 的不足 ,首先采用 Canopy 算法对 DGA 数据进行粗 聚类 ,将其结果作为后续FCM 聚类的初始聚类中心和最佳聚类数 , 降低了人为和随机初始化参数的主观性 ;然后 通过引入负指数函数形式的相似度指标重构了 FCM 隶属度的迭代函数 ,降低了算法陷入局部极值点的可能性 ;最 后通过对故障气体数据进行实例分析 ,验证了改进后的算法在识别变压器故障类别上的有效性和实用性。
Abstract:Dissolved gas analysis (DGA) in transformer oil is an important technology to identify transformer fault types, and fuzzy clustering is an effective analysis method. However, the traditional fuzzy clustering algorithm is sensitive to the randomly initialized cluster center and the effective measurement range of membership function is small, which makes it easy to fall into local extremum points, so the actual classification effect is not good. To address the shortcomings of the traditional FCM. Canopy algorithm was first used to conduct rough clustering of DGA data, and the results were used as the initial cluster center and optimal cluster number of subsequent FCM clustering, which reduced the subjectivity of artificial and random initialization parameters. Then, the iterative function of FCM membership was reconstructed by introducing the similarity index in the form of negative exponential function, which reduced the possibility of the algorithm falling into local extremum points. Finally, the effectiveness and practicability of the improved algorithm in transformer fault classification are verified by analyzing the fault gas data.

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