Disturbance Identification Method on Power Quality Based on Multi-feature Combination

MA Jie; SUN Ning; REN Zhiqiang; HU Yang

doi:10.14044/j.1674-1757.pcrpc.2019.02.023

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Disturbance Identification Method on Power Quality Based on Multi-feature Combination

更新时间：2024-09-14

- Disturbance Identification Method on Power Quality Based on Multi-feature Combination
- POWER CAPACITOR & REACTIVE POWER COMPENSATION Vol. 40, Issue 2, Pages: 125-130(2019)
- 作者机构：
  
  1. 国网张家口供电公司
  2. 国网廊坊供电公司
- 作者简介：
- 基金信息：
- DOI：10.14044/j.1674-1757.pcrpc.2019.02.023
  CLC： TM711
- Published：2019，
- 稿件说明：
移动端阅览
[1]马杰,孙宁,任志强,胡洋.基于多特征组合的电能质量扰动识别方法[J].电力电容器与无功补偿,2019,40(02):125-130.

MA JIE, SUN NING, REN ZHIQIANG, et al. Disturbance Identification Method on Power Quality Based on Multi-feature Combination. [J]. Power capacitor & reactive power compensation, 2019, 40(2): 125-130.
[1]马杰,孙宁,任志强,胡洋.基于多特征组合的电能质量扰动识别方法[J].电力电容器与无功补偿,2019,40(02):125-130. DOI： 10.14044/j.1674-1757.pcrpc.2019.02.023.

MA JIE, SUN NING, REN ZHIQIANG, et al. Disturbance Identification Method on Power Quality Based on Multi-feature Combination. [J]. Power capacitor & reactive power compensation, 2019, 40(2): 125-130. DOI： 10.14044/j.1674-1757.pcrpc.2019.02.023.

摘要

为解决电力系统电能质量复合扰动识别困难的问题

同时为电网无功功率补偿提供依据

提出了一种基于多特征组合的电能质量复合扰动识别方法。应用小波包变换和S变换对电能质量扰动信号进行时频分析

提取信号的幅值特征和频率特征

构建电能质量扰动的多特征组合。然后利用概率神经网络构建多特征组合分类器实现电能质量扰动的分类识别。仿真实验证明

该方法能够识别包括两种复合扰动在内的7种电能质量扰动信号

并且具有较高的分类准确度和抗干扰能力

具有工程应用价值。

Abstract

For solving such problem as difficult identification of complex disturbance of the power quality of power system and

at the same time evidence provision of reactive power compensation of grid

a kind of complex disturbance identification method of power quality based on multi-feature combination is proposed. Wavelet packet transform（WPT） and S-transform are used to perform time-frequency analysis of the power quality disturbance signal

extract amplitude and frequency characteristics of signal and establish multi-feature combination of power quality disturbance. Then

the probability neutral is adopted to establish multi-feature combination classifier so to achieve classification identification of power quality disturbance. It is proved by the simulation experiment that the method can identify seven kinds of power quality disturbances including two kinds of compound disturbances

have higher classification accuracy and interference resistance ability as well as engineering application value.

关键词

电能质量无功功率补偿小波包变换S变换多特征组合概率神经网络

Keywords

power qualityreactive power compensationwavelet packet transformS-transformmulti-feature combinationprobability neural network

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