标题:Machine Learning Based Fault Type Identification In the Active Distribution Network
作者:Sun, Baicong; Zhang, Hengxu; Shi, Fang
通讯作者:Zhang, HX
作者机构:[Sun, Baicong; Zhang, Hengxu; Shi, Fang] Shandong Univ, Sch Elect Engn, Jinan, Shandong, Peoples R China.
会议名称:IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)
会议日期:MAR 15-17, 2019
来源:PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019)
出版年:2019
页码:1330-1334
DOI:10.1109/ITNEC.2019.8729054
关键词:machine learning; fault type identification; active distribution; network; batch simulation; feature extraction
摘要:To realize the intelligent of the distribution network, it is necessary to identify the fault type accurately. This paper presents the fault type identification method based on machine learning in active distribution networks. The process of machine learning is divided into four steps: data preparation, data preprocessing, feature extraction and model training. When preparing data, a method of generating fault scenarios in the batch of simulation experiments is presented. The IEEE34 Bus System is built in PSCAD to complete the data preparation for machine learning. Variation multiples of voltage and current are extracted as the features to describe the fault type. Various machine learning models are trained by cross-validation method to get the accuracy of identification. The application of decision tree in fault type identification is presented in the form of a tree diagram. The result of fault type identification is shown by the confusion matrix of the decision tree. All the test results show that the proposed fault identifiers can identify all kinds of fault types in the distribution network.
收录类别:CPCI-S;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-s2.0-85067886463&doi=10.1109%2fITNEC.2019.8729054&partnerID=40&md5=24ae64220b7c423a79740b9fed4a782b
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