Study on the Optimization of Power Quality Disturbance Diagnosis and Identification of ELM Based on Dragonfly Algorithm

doi:10.14044/j.1674-1757.pcrpc.2019.01.025

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Study on the Optimization of Power Quality Disturbance Diagnosis and Identification of ELM Based on Dragonfly Algorithm

更新时间：2024-09-14

- Study on the Optimization of Power Quality Disturbance Diagnosis and Identification of ELM Based on Dragonfly Algorithm
- POWER CAPACITOR & REACTIVE POWER COMPENSATION Vol. 40, Issue 1, Pages: 142-147(2019)
- 作者机构：
  
  1. 国家电网公司
  2. 南瑞集团(国网电力科学研究院)
  3. 国网浙江省电力公司
- 作者简介：
- 基金信息：
- DOI：10.14044/j.1674-1757.pcrpc.2019.01.025
  CLC： TM711
- Published：2019，
- 稿件说明：
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[1]王宏刚,田洪迅,谈军,周辉.基于蜻蜓算法优化ELM的电能质量扰动诊断与识别研究[J].电力电容器与无功补偿,2019,40(01):142-147.

Study on the Optimization of Power Quality Disturbance Diagnosis and Identification of ELM Based on Dragonfly Algorithm. [J]. Power capacitor & reactive power compensation, 2019, 40(1): 142-147.
[1]王宏刚,田洪迅,谈军,周辉.基于蜻蜓算法优化ELM的电能质量扰动诊断与识别研究[J].电力电容器与无功补偿,2019,40(01):142-147. DOI： 10.14044/j.1674-1757.pcrpc.2019.01.025.

Study on the Optimization of Power Quality Disturbance Diagnosis and Identification of ELM Based on Dragonfly Algorithm. [J]. Power capacitor & reactive power compensation, 2019, 40(1): 142-147. DOI： 10.14044/j.1674-1757.pcrpc.2019.01.025.

摘要

针对极限学习机预测结果易受其初始化输入权值和偏置值的影响

提出一种DA算法优化ELM的电能质量扰动诊断和识别模型。选择5种电能质量扰动信号为研究对象

研究结果表明

与GA

LM、PSO

LM和ELM相比

本文提出的算法DA

LM可以有效提高电能质量扰动识别的准确率

为电能质量扰动诊断和识别提供新的方法和途径。

Abstract

In view of the predicted result of the extreme learning machine being likely susceptible to the initial input weights and the offset value

a kind of DA algorithm is proposed to optimize power quality disturbance diagnosis and model identification of ELM.Five kinds of power quality disturbance signals are chosen as the study object.It is shown by the study result that compared with GA

PSO-ELM and ELM

the algorithm DA

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

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

关键词

蜻蜓算法极限学习机电能质量样本熵小波分解

Keywords

dragonfly algorithmextreme learning machinepower qualitysample entropywavelet decomposition

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