基于混沌序列的变压器油色谱数据并行聚类分析

doi:10.19595/j.cnki.1000-6753.tces.181894

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摘要

变压器油中溶解气体分析（DGA）是变压器运行和维护的重要技术手段,聚类算法是油色谱分析的一种重要智能算法。但是聚类算法的目标函数是一个典型的非凸函数,其寻优求解过程是局部搜索的爬山算法,迭代过程容易陷入局部极值点,因而无法实现有效的油色谱数据分类。混沌变量具有随机性和遍历性,使得全局寻优成为可能。该文在聚类迭代的过程中,利用混沌序列对聚类中心进行“人工突变”,同时在聚类的过程中设置多条并行的寻优轨迹,在迭代过程中,每条寻优轨迹除了按照自身的梯度信息进行推演外,同时还共享其余轨迹的寻优信息。可在迭代寻优的过程改变原有的寻优轨迹,从而避免寻优过程终止于局部极值点,实现全局寻优。实例分析表明,该文的方法促进了聚类分析的全局寻优,提高了模糊聚类算法进行DGA故障模式识别的能力,具有现实应用价值。

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关键词 ：全局收敛, 模糊聚类分析, 变压器, 故障诊断, 溶解气体分析

Abstract：

Transformer oil chromatographic analysis (DGA) is an important technical method for transformer operation and maintenance, and Fuzzy C-Means clustering is an important intelligent algorithm for DGA. However, the objective function of the clustering algorithm is a typical non-convex function and the optimization process is a hill-climbing algorithm based on local search. Consequently, the iterative process is easy to fall into local extremum, and the algorithm cannot classify effective oil chromatographic data. Chaotic variables have randomness and ergodicity, making global optimization possible. In this paper, the chaotic sequence is used to perform “artificial mutation” on the cluster center in the process of cluster iteration, and multiple optimization trajectories are set in parallel. In the iterative process, each of the optimized trajectories is deduced according to its own gradient information, and also the optimization information of the remaining trajectories is shared. The original optimization trajectory can be changed in the process of iterative optimization, thereby avoiding the optimization process from terminating at the local extreme point and achieving global optimization. The case analysis shows that the proposed method achieves the global optimization of clustering analysis and improves the ability of fuzzy clustering algorithm to identify DGA fault patterns, which has practical application value.

Key words： Global convergence fuzzy clustering analysis transformer fault diagnosis dissolved gas analysis

收稿日期: 2018-12-06 出版日期: 2019-12-30

PACS:

TM72

通讯作者: 王力农，男,1976年生,研究员,博士生导师,研究方向为高电压与绝缘技术。E-mail:wangln@whu.edu.cn

作者简介: 李恩文，男,1990年生,博士研究生,研究方向为变压器故障诊断与状态维修。E-mail:lienwen@whu.edu.cn

引用本文:

李恩文, 王力农, 宋斌, 方雅琪. 基于混沌序列的变压器油色谱数据并行聚类分析[J]. 电工技术学报, 2019, 34(24): 5104-5114. Li Enwen, Wang Linong, Song Bin, Fang Yaqi. Parallel Clustering Analysis of Dissolved Gas Analysis Data Based on Chaotic Sequences. Transactions of China Electrotechnical Society, 2019, 34(24): 5104-5114.

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https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.181894 https://dgjsxb.ces-transaction.com/CN/Y2019/V34/I24/5104

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