遗传算法优化的BP神经网络在配网故障诊断中的应用
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作者单位:

1.国网宁夏电力有限公司电力科学研究院;2.国网宁夏电力有限公司

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国网宁夏电力有限公司科技项目资助,(FWSQCG-DKY-2020-2920C7)


Application of BP Neural Network Optimized by Genetic Algorithm in Fault Diagnosis of Distribution Network
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1.State Grid Ningxia Electric Power Technical Research Institute,Ning Xia,Yin Chuan;2.State Grid Ningxia Electric Power Co,Ltd,Ning Xia,Yin Chuan

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    摘要:

    BP神经网络作为人工神经网络中的典型网络模型,在配电网故障诊断中也有广泛的应用,但由于其初始权值和初始阈值的随机性,应用中存在诊断精度不高等问题。本文提出一种基于遗传算法优化的BP神经网络的配网故障诊断方法,利用遗传算法对BP神经网络的初始权值和阈值进行优化,并在算例中与传统神经网络进行故障诊断结果对比,最后分析两者仿真误差值,验证其可行性。结果表明:利用遗传算法可得到BP神经网络较为理想的初始权值和阈值,可有效降低运行结果的误差,使诊断结果更加准确。

    Abstract:

    As a typical network model of artificial neural network, BP neural network has been widely used in fault diagnosis of distribution network. However, due to the randomness of initial weight and initial threshold, the diagnosis accuracy is not high in application. In this paper, a distribution network fault diagnosis method based on genetic algorithm optimization of BP neural network is proposed. The initial weight and threshold of BP neural network are optimized by genetic algorithm, and the fault diagnosis results are compared with those of traditional neural network in the calculation example. Finally, the simulation error values of the two are analyzed to verify the feasibility. The results show that the optimal initial weight and threshold of BP neural network can be obtained by using genetic algorithm, which can effectively reduce the error of operation results and make the diagnosis more accurate.

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  • 收稿日期:2020-12-10
  • 最后修改日期:2021-03-24
  • 录用日期:2021-04-11
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