With the increase of energy demand, the scale of power grid is expanding, and the difficulty of power grid fault diagnosis is increasing. Aiming at the problem of large power grid fault diagnosis, a method of partition fault diagnosis based on improved Probabilistic neural network (PNN) and gray relational analysis (GRA) integral is proposed. Firstly, the large power grid divided into small areas for fault diagnosis through power grid partition, which reduces the difficulty of fault diagnosis. Then the PNN diagnosis module is established by the PNN optimized by GA‐CPSO for diagnosing the power grid fault. Finally, the faults in the overlapping area are reanalyzed by the GRA method, in order to realize the accurate fault diagnosis of the whole power grid. The feasibility and effectiveness of the method are analyzed by two cases. The diagnosis results show that the method can effectively identify the faults in the nonoverlapping area and the overlapping area, and has strong fault tolerance and high diagnosis accuracy. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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基于改进的PNN和GRA的电网分区故障诊断

随着能源需求的增加，电网规模不断扩大，电网故障诊断的难度越来越大。针对大型电网故障诊断问题，提出了一种基于改进的概率神经网络（PNN）和灰色关联分析（GRA）积分的分区故障诊断方法。首先，将大型电网划分为小区域，通过电网划分进行故障诊断，降低了故障诊断的难度。然后由GA-CPSO优化的PNN建立PNN诊断模块，以诊断电网故障。最后，通过GRA方法对重叠区域的故障进行重新分析，以实现对整个电网的准确诊断。通过两种情况分析了该方法的可行性和有效性。诊断结果表明，该方法可以有效地识别非重叠区域和重叠区域的故障，具有较强的容错能力和较高的诊断精度。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。