Power Quality Dectection and Classification in Active Distribution Networks Based on Improved Empiricial Wavelet Transform and Disperson Entropy

Yanchun Xu; Shirong Fan; Shasha Xie; Mi Lu

doi:10.17775/CSEEJPES.2020.00110

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Power Quality Dectection and Classification in Active Distribution Networks Based on Improved Empiricial Wavelet Transform and Disperson Entropy

Regular Papers | 更新时间：2026-10-15

- Power Quality Dectection and Classification in Active Distribution Networks Based on Improved Empiricial Wavelet Transform and Disperson Entropy
- CSEE Journal of Power and Energy Systems Vol. 8, Issue 6, Pages: 1646-1658(2022)
- 作者机构：
  
  1. College of Electrical Engineering and New Energy, China Three Gorges University,Yichang,China
  2. State Grid Changchun Power Supply Company,Changchun,China
  3. Department of Electrical and Computer Engineering, Texas A&M University,Texas,USA
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.00110
  CLC：
- Published：01 November 2022
- 稿件说明：
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Yanchun Xu, Shirong Fan, Shasha Xie, et al. Power Quality Dectection and Classification in Active Distribution Networks Based on Improved Empiricial Wavelet Transform and Disperson Entropy[J]. CSEE Journal of Power and Energy Systems, 2022, 8(6): 1646-1658.
DOI：

Yanchun Xu, Shirong Fan, Shasha Xie, et al. Power Quality Dectection and Classification in Active Distribution Networks Based on Improved Empiricial Wavelet Transform and Disperson Entropy[J]. CSEE Journal of Power and Energy Systems, 2022, 8(6): 1646-1658. DOI： 10.17775/CSEEJPES.2020.00110.

摘要

Abstract

In this paper

a model including wind power generation

photovoltaic power generation and electric vehicle for high permeability active distribution network (ADN) is established. The power quality (PQ) disturbance signals in the high permeability are extracted

and the characteristics of disturbance signals are analyzed in the situation of grid connection

interruption and islanding. The multi-scale fluctuation dispersion entropy (MFDE) initialized by the improved empirical wavelet transform (IEWT) is utilized to detect and classify the disturbance signals in the high permeability ADN. First

the eigenvectors of the disturbance signals are obtained by using the multi-scale fluctuation dispersion entropy initiated by the IEWT

and then the reduced eigenvectors are put into the support vector machine to classify the PQ disturbances caused by the access of the different distributed generators accessed. The classification results are compared with that in the traditional methods and other similar ways; the effectiveness of the IEWT-MFDE system is verified.

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