A classification based on random forest for partial discharge sources

被引：1

作者：

Pu, Senlin ^{[1
]}

Zhang, Huajun ^{[1
]}

Mao, Cuimin ^{[2
]}

Yang, Guang ^{[2
]}

机构：

[1] Wuhan Univ Technol, Sch Automat, Wuhan, Peoples R China

[2] Hubei Elect Power Survey & Design Inst Co LTD, Yichang, Peoples R China

来源：

PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年

关键词：

Partial discharge; Classification; Random Forest;

D O I：

10.1109/CCDC52312.2021.9602056

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The identification of Partial Discharge Sources (PD) is an important task in the monitoring and diagnosis of high voltage components, and the classification of their discharge sources is extremely important. In this paper, three major features of Partial Discharge Sources have been extracted and various machine learning algorithms are applied to classify them. The final experiments in implementing the classification of partial discharge sources show that Random Forest is more robust to noise compared to decision trees and AdaBoost, and runs at a speed comparable to AdaBoost.

引用

页码：2307 / 2311

页数：5

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