Divisional fault diagnosis of power grids based on RBF neural network and fuzzy integral fusion

被引：0
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作者：
机构：
[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei Province
来源：
Shi, D. (dongyuanshi@hust.edu.cn) | 1600年 / Chinese Society for Electrical Engineering卷 / 34期
关键词：
Fault diagnosis; Fuzzy integral; Grid division; Large-scale power grid; Radial basis function neural network;
D O I：
10.13334/j.0258-8013.pcsee.2014.04.007
中图分类号：
学科分类号：
摘要：
This paper presented an effective method for fault diagnosis of large-scale power grids based on radial basis function(RBF) neural network and fuzzy integral fusion. The study aims at effectively solving the diagnosis problem about the tie lines connecting regional sub-grids in the divisional fault diagnosis scheme. An overlapping network division method was proposed to divide a large-scale power grid into several sub-grids. When faults occur, regional RBF neural network diagnostic modules corresponding to different sub-grids are selectively triggered according to local alarm information which implies the faults exist in the sub-grids. Then faults of tie lines can be diagnosed by applying fuzzy integral to fuse the diagnostic outputs of two connected sub-grids about the tie lines. The method can not only be efficient in diagnosing the faults within local regions, but also in diagnosing the faults of tie lines well. The simulation results show that the proposed method is simple, efficient and can make up for the shortcoming of existing divisional fault diagnosis methods in diagnosis of tie lines. Moreover, it can diagnose different complex faults with good fault tolerance capability. © 2014 Chin. Soc. for Elec. Eng.
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页码：562 / 569
页数：7
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