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首页> 外文期刊>Electric power systems research >Divisional fault diagnosis of large-scale power systems based on radial basis function neural network and fuzzy integral
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Divisional fault diagnosis of large-scale power systems based on radial basis function neural network and fuzzy integral

机译：基于径向基函数神经网络和模糊积分的大型电力系统分区故障诊断

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摘要

This paper proposes an effective method for fault diagnosis of large-scale power systems based on radial basis function (RBF) neural network (NN) and fuzzy integral. It aims at effectively diagnosing the tie lines which connect different adjacent sub-networks in the context of divisional fault diagnosis. First, an overlapping network division method is proposed to divide a large-scale power system into a desired number of eligible sub-networks. Then, for each sub-network, a local RBF NN diagnostic module which is constructed by an exhaustive search-assisted forward recursive algorithm is allocated. Finally, a Choquet fuzzy integral fusion module is constructed for any pair of connected sub-networks. When a fault occurs, local RBF NN diagnostic modules will be selectively triggered according to local alarm information. If it involves a tie line, the corresponding Choquet fuzzy integral fusion module will be triggered to fuse the diagnostic outputs derived from the adjacent sub-networks which are connected by the tie line. Case studies with a 14-bus power system are presented to evaluate the feasibility and efficiency of the proposed method under various complex fault scenarios. The diagnostic results demonstrate that this proposed method is efficient in identifying faults within local sub-networks as well as those on the tie lines with strong fault tolerance and high diagnostic accuracy.
机译：提出了一种基于径向基函数神经网络和模糊积分的大型电力系统故障诊断方法。其目的是在分区故障诊断的背景下，有效地诊断连接不同相邻子网的连接线。首先，提出了一种重叠的网络划分方法，以将大型电力系统划分为所需数量的合格子网。然后，为每个子网分配由穷举搜索辅助的前向递归算法构造的本地RBF NN诊断模块。最后，为任何一对相连的子网构建一个Choquet模糊积分融合模块。发生故障时，将根据本地警报信息有选择地触发本地RBF NN诊断模块。如果涉及联络线，则将触发相应的Choquet模糊积分融合模块，以融合由联络线连接的相邻子网得出的诊断输出。提出了一个14总线电力系统的案例研究，以评估该方法在各种复杂故障情况下的可行性和效率。诊断结果表明，该方法能够有效地识别本地子网内以及联络线上的故障，具有较强的容错能力和较高的诊断精度。

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  • 来源
    《Electric power systems research》 |2013年第12期|9-19|共11页
  • 作者

    Guojiang Xiong; Dongyuan Shi; Jinfu Chen; Lin Zhu; Xianzhong Duan;

  • 作者单位

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Choquet fuzzy integral; Divisional fault diagnosis; Forward recursive algorithm; Large-scale power system; Radial basis function neural network;

    机译：Choquet模糊积分;分区故障诊断;前向递归算法;大型电力系统;径向基函数神经网络;

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