Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree

Wenjing Zhao; Liqun Shang; Jinfan Sun

doi:10.1186/s41601-019-0139-z

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Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree

Contents | 更新时间：2025-12-19

- Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree
- Protection and Control of Modern Power Systems Vol. 4, Issue 4, Pages: 1-6(2019)
- 作者机构：
  
  1. School of Electrical and Control Engineering, Xi'an University of Science and Technology,Shaanxi Province,Xi'an,China
  2. Weihe Thermal Power Plant, Shaanxi Power Generation co., LTD.,Xianyang,China
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-019-0139-z
  CLC：
- Published：2019
- 稿件说明：
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Wenjing Zhao, Liqun Shang, Jinfan Sun. Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree[J]. Protection and Control of Modern Power Systems, 2019, 4(4): 1-6.
DOI：

Wenjing Zhao, Liqun Shang, Jinfan Sun. Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree[J]. Protection and Control of Modern Power Systems, 2019, 4(4): 1-6. DOI： 10.1186/s41601-019-0139-z.

摘要

Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal

and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals

with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform

the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

Abstract

the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

关键词

Keywords

references

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