[1]高金峰,庞昊,杜耀恒.基于GRU网络的配电网故障数量等级预测方法[J].郑州大学学报(工学版),2019,40(05):38-43.
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基于GRU网络的配电网故障数量等级预测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年05期
页码:
38-43
栏目:
出版日期:
2019-09-20

文章信息/Info

作者:
高金峰庞昊杜耀恒
郑州大学产业技术研究院,河南郑州450001; 国家电网烟台供电公司,山东烟台264000
关键词:
配电网故障数量等级循环神经网络GRU网络历史依赖性相关性
Keywords:
number of faults in distribution network RNN GRU neural network historical dependence rel�1Fevance
文献标志码:
A
摘要:
配电网故障数量的多少直接影响配电网的运行维护与用户的用电体验,目前业界关于配电网 故障数量等级预测的研究较少.给出了一种基于GRU网络的配电网故障数据分析与故障数量等级预测 方法.通过条件嫡来衡量配电网故障数量等级的历史依赖性,采用距离相关系数对诸多气象特征因素进 行相关性强弱考察,筛选出最优特征子集,最后通过训练GRU网络实现了配电网故障数量等级的预测. 算例结果证明了预测方法的有效性.
Abstract:
The number of faults in distribution network was a direct impact on the operation and maintenance of distribution network and the users power consumption experience. At present, there were few stadies on the prediction of the number of faults in distribution network. To measure the historical dependence of distribution network fault magnitude, the optimal feature subset was selected by using the distance correlation coefficient to investigate the correlation of many meteorological features. Finally, the GRU neural network was trained to predict the fault magnitude of distribution network accurately. The results proved the feasibility of this method.
更新日期/Last Update: 2019-10-26