Study on Power Quality Disturbance Identification and Diagnosis of SVM Based on Sample Entropy and Dragonfly Algorithm

QU Gaoqiang; WANG Chengmin; QI Caijuan; ZHAO Liang; LIU Yong; CHEN Wanxi

doi:10.14044/j.1674-1757.pcrpc.2019.01.021

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Study on Power Quality Disturbance Identification and Diagnosis of SVM Based on Sample Entropy and Dragonfly Algorithm

更新时间：2024-09-14

- Study on Power Quality Disturbance Identification and Diagnosis of SVM Based on Sample Entropy and Dragonfly Algorithm
- POWER CAPACITOR & REACTIVE POWER COMPENSATION Vol. 40, Issue 1, Pages: 115-122(2019)
- 作者机构：
  
  1. 国网宁夏电力公司经济技术研究院
  2. 上海交通大学
  3. 上海博英信息科技有限公司
- 作者简介：
- 基金信息：
- DOI：10.14044/j.1674-1757.pcrpc.2019.01.021
  CLC： TM711
- Published：2019，
- 稿件说明：
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[1]屈高强,王承民,齐彩娟,赵亮,刘涌,陈万喜.基于样本熵和蜻蜓算法优化SVM的电能质量扰动识别和诊断研究[J].电力电容器与无功补偿,2019,40(01):115-122.

QU GAOQIANG, WANG CHENGMIN, QI CAIJUAN, et al. Study on Power Quality Disturbance Identification and Diagnosis of SVM Based on Sample Entropy and Dragonfly Algorithm. [J]. Power capacitor & reactive power compensation, 2019, 40(1): 115-122.
[1]屈高强,王承民,齐彩娟,赵亮,刘涌,陈万喜.基于样本熵和蜻蜓算法优化SVM的电能质量扰动识别和诊断研究[J].电力电容器与无功补偿,2019,40(01):115-122. DOI： 10.14044/j.1674-1757.pcrpc.2019.01.021.

QU GAOQIANG, WANG CHENGMIN, QI CAIJUAN, et al. Study on Power Quality Disturbance Identification and Diagnosis of SVM Based on Sample Entropy and Dragonfly Algorithm. [J]. Power capacitor & reactive power compensation, 2019, 40(1): 115-122. DOI： 10.14044/j.1674-1757.pcrpc.2019.01.021.

摘要

为了避免支持向量机预测结果易受惩罚因子和核函数参数参数选择的影响

提出一种DA算法优化SVM的电能质量扰动诊断和识别模型

实现电能质量扰动最优化诊断和识别。首先运用EMD将电能质量扰动信号进行分解

之后计算各尺度下的IMF分量的样本熵

并将其作为电能质量扰动信号的特征向量

建立SVM的电能质量扰动信号的识别模型。实验结果表明

与GA

VM、PSO

VM和DE

VM相比

本文提出的算法DA

VM可以有效提高电能质量扰动识别的准确率

收敛速度快

为电能质量扰动诊断和识别提供新的方法和途径。

Abstract

For preventing the prediction result of the support vector machine（SVM for short）from the punishment factor and parameter selection of kernel function parameter optimal

a kind of algorithm is proposed to optimize power quality disturbance diagnosis and identification of SVM so to the most optimal diagnosis and identification of the power quality.Firstly

power quality disturbance signal is decomposed by EMD method

then the sample entropy of different scale signal of EMD decomposition is calculated and is considered as a signal of power quality disturbance characteristic vector and the identification model based on SVM for the power quality disturbance signal is est

ablished.It is shown by the experimental result that compared with GA

PSO

VM and DE

the algorithm DA

VM proposed in this paper can effectively improve the accuracy of the power quality disturbance identification

has fast convergence rate and provide new methods and means for the power quality disturbance diagnosis and identification.

关键词

样本熵支持向量机蜻蜓算法电能质量经验模态分解

Keywords

sample entropysupport vector machinedragonfly algorithmpower qualityempirical mode decomposition

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