标题:Power quality disturbance identification method based on multi-domain feature fusion
作者:Lifen, Chen ;Ke, Zhu ;Guoping, Sun
通讯作者:Ke, Zhu
作者机构:[Lifen, Chen ;Ke, Zhu ] School of Electrical Engineering, Shandong University, Jinan; 250061, China;[Guoping, Sun ] Qihe County Power Supply Company Q 更多
会议名称:14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019
会议日期:19 June 2019 through 21 June 2019
来源:Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019
出版年:2019
页码:37-42
DOI:10.1109/ICIEA.2019.8833682
关键词:Cross-entropy; DS evidence fusion; Multi-domain feature fusion; Power quality disturbance identification
摘要:Power quality disturbance identification is vital for power quality study. However, noise, interference between disturbances and the effect of feature extraction method may lead to edge blurring of features extracted from different disturbances, thus affecting the accurate of disturbance recognition. Thence, the paper proposes a recognition method based on multi-domain feature fusion. Firstly, the neural network is trained preliminarily by mixed features of different domains, and then with the input characteristics of each domain, the action probability of hidden layer neurons in corresponding domain is determined according to the changes of cross-entropy before and after retention of the hidden layer neurons. Finally, work out the final results by the DS evidence theory, in which the independent evidence is transformed from identification results of unknown disturbances in different domains. The algorithm reduces the influence of errors in characteristics of a single domain on the identification accuracy, and is robust to noise and stable. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073050226&doi=10.1109%2fICIEA.2019.8833682&partnerID=40&md5=65fce157b791d1be4c176a00c7e362ac
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