Research Summary of Power Quality Disturbance Detection and Classification Recognition Method Based on Transform Domain

被引：1

作者：

Li-Ping, Qu ^{[1
]}

Chang-Long, He ^{[2
]}

Jie, Zhang ^{[2
]}

机构：

[1] Beihua Univ, Engn Training Ctr, Jilin, Jilin, Peoples R China

[2] Beihua Univ, Coll Elect & Informat Engn, Jilin, Jilin, Peoples R China

来源：

2020 19TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS ENGINEERING AND SCIENCE (DCABES 2020) | 2020年

关键词：

Transform domain; Power quality; Wavelet transform; Extreme learning machine; Short-time Fourier transform;

D O I：

10.1109/DCABES50732.2020.00022

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

With the diversification of power connection forms and increasing types of loads, the power quality of the power system is deteriorating. Various indicators of power quality are essential for the normal operation of the power grid, especially the increasing harmonic pollution caused by various nonlinear loads. Therefore, power quality disturbance detection and classification recognition is the key to improve power quality. This article combines the current domestic and foreign power quality related standards, summarizes the feature extraction of electric energy quality disturbance based on transform domain, meanwhile recognize and classify the extracted feature vectors.

引用

页码：50 / 53

页数：4

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