Multi-source Partial Discharge Identification of Power Equipment Based on Random Forest

4th International Conference on Advances in Energy Resources and Environment Engineering

能源学;生态环境科学

Deng, Ran^1 ; Zhu, Yongli^1 ; Liu, Xuechun^1 ; Zhai, Yujia^1

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Source, North China Electric Power University, Baoding, Hebei, China^1

关键词: Anti-noise ability;    Identification method;    Information resource;    Partial discharge identifications;    Pulse separations;    Signal separation;    Statistical characteristics;    Strong classifiers;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/237/6/062039/pdf
DOI  :  10.1088/1755-1315/237/6/062039

学科分类：环境科学（综合）

来源: IOP

【 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%.