文章基本信息

标题：Multi-source Partial Discharge Identification of Power Equipment Based on Random Forest
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作者：Ran Deng ; Ran Deng ; Yongli Zhu 等
期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：237
期号：6
页码：062039
DOI：10.1088/1755-1315/237/6/062039
出版社：IOP Publishing
摘要：At present, research on partial discharge type identification of power equipment mainly focused on single source discharge, and a small amount of multi-source discharge research also focused on pulse separation of multi-source signals. Signal separation could lose a lot of valid signals and wasted information resources. In this paper, a multi-source partial discharge type identification method based on Random Forest (RF) is proposed. Firstly, in the feature extraction, the statistical characteristics of the multi-source data are extracted directly instead of signal separation, and the discharge information is fully utilized. Secondly, in terms of the choice of classifier, considering that the traditional SVM method can not handle the value of 0, this paper selects the random forest strong classifier with good anti-noise ability to identify the multi-source discharge type. The test results show that this method is effective and the recognition rate of PD is as high as 98%.