A rough set-based bio-inspired fault diagnosis method for smart substation protection equipment

被引：0
|
作者：
Liang W. [1 ]
Zhu W. [1 ]
Li H. [1 ]
Yan Y. [1 ]
Dong G. [1 ]
Wang X. [2 ]
Gong J. [3 ]
机构：
[1] State Grid Hunan Electric Power Co., Ltd. Research Institute, Changsha
[2] Chenzhou Power Supply Branch, State Grid Hunan Electric Power Co., Ltd., Chenzhou
[3] Shanghai University of Electric Power, Shanghai
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2021年 / 49卷 / 21期
基金：
中国国家自然科学基金;
关键词：
Fault diagnosis; Rough set; Smart substation; Spiking neural membrane system;
D O I：
10.19783/j.cnki.pspc.210041
中图分类号：
O144 [集合论]; O157 [组合数学（组合学）];
学科分类号：
070104 ;
摘要：
The inaccuracy and uncertainty of alarm information for substation protection equipment will seriously affect the accuracy and reliability of fault diagnosis results. A rough set-based bionic fault diagnosis method for substation protection equipment is proposed. The algorithm consists of four key parts: substation sub-area division method, rough set attribute reduction algorithm, Binary Reasoning Pulse Neural Membrane System (BRSNPS) and parallel reasoning algorithm. Specifically, the sub-region division method of the substation and the rough set reduction algorithm are used to find the reduced fault generation rule set for each sub-region. The complexity of the problem is simplified and the uncertainty of the fault alarm information can be handled. Then, BRSNPS and its reasoning algorithm are proposed. These realize the display and analysis of fault information and obtain accurate fault diagnosis results. Because of the collaboration of rough set and spike nervous system, historical statistics and professional knowledge are not required, and the scale of the problem is reduced. Finally, based on real 110 kV and 750 kV substations, the algorithm proposed is verified by experiment, and the results show that this method is better than other methods. © 2021 Power System Protection and Control Press.
引用
收藏
页码：132 / 140
页数：8
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