基于优化极限学习机的电压暂降源识别方法

汪颖; 王欢; 张姝

doi:10.7500/AEPS20190809004

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基于优化极限学习机的电压暂降源识别方法

研制与开发 | 更新时间：2025-06-16

- 基于优化极限学习机的电压暂降源识别方法
- Recognition Method of Voltage Sag Source Based on Optimized Extreme Learning Machine
- 电力系统自动化-方正平台 2020年44卷第9期页码：135-143
- 作者机构：
  
  四川大学电气工程学院，四川省成都市 610065
- 作者简介：
  
  汪颖(1981—)，女，博士，副教授，硕士生导师，主要研究方向：电能质量与优质供电。E-mail：20312028@qq.com
  王欢(1993—)，男，硕士研究生，主要研究方向：电能质量与优质供电。E-mail：1173394186@qq.com
  张姝(1988—)，女，通信作者，博士，助理研究员，主要研究方向：配电网保护与定位、电能质量与优质供电。E-mail：ZS20061621@163.com
- 基金信息：
  
  国家自然科学基金资助项目(51807126)
- DOI：10.7500/AEPS20190809004
  中图分类号：
- 收稿日期：2019-08-09，
  
  修回日期：2019-11-26，
  
  网络出版日期：2020-03-04，
  
  纸质出版日期：2020-05-10
- 稿件说明：
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汪颖,王欢,张姝.基于优化极限学习机的电压暂降源识别方法[J].电力系统自动化,2020,44(9):135-143.

WANG Ying,WANG Huan,ZHANG Shu.Recognition Method of Voltage Sag Source Based on Optimized Extreme Learning Machine[J].Automation of Electric Power Systems,2020,44(9):135-143.
汪颖,王欢,张姝.基于优化极限学习机的电压暂降源识别方法[J].电力系统自动化,2020,44(9):135-143. DOI： 10.7500/AEPS20190809004.

WANG Ying,WANG Huan,ZHANG Shu.Recognition Method of Voltage Sag Source Based on Optimized Extreme Learning Machine[J].Automation of Electric Power Systems,2020,44(9):135-143. DOI： 10.7500/AEPS20190809004.

摘要

准确识别电压暂降源对暂降责任分摊和治理决策至关重要。文中提出一种基于优化极限学习机的电压暂降源识别方法。通过直接提取电压暂降波形的时域特征和经S变换提取能量熵和奇异熵2种时频域特征，构建基于时域和时频域的特征向量，弥补现有方法仅采用时频变换提取特征，可能丢失仅存在于时域内的重要信息而影响识别精度的不足。针对极限学习机输入权值和隐含层偏置随机产生的不足，采用遗传算法对其进行优化，构建优化极限学习机模型，解决利用模式识别存在模型复杂和耗时较长，难以实现快速识别的问题。应用仿真数据和实测数据验证了所提特征向量和优化极限学习机模型的有效性；并与其他方法相比，证明所提模型简单、训练和分类识别速度快，识别精度更高，适用于边缘计算，可实现电压暂降源的快速准确识别。

Abstract

It is important to accurately identify voltage sag sources for sag responsibility allocation and mitigation decision-making. This paper proposes a recognition method of voltage sag sources based on the optimized extreme learning machine (ELM). The time-domain features are directly extracted from the voltage sag waveforms

and the time-frequency domain features (including energy entropy and singular entropy) are extracted by S-transform. Then the feature vectors are built based on the time-domain and time-frequency domain features. The feature vectors make up for the shortcomings that the existing methods only use time-frequency transform to extract features and may lose the important information only existing in time domain

which will affect the recognition accuracy. The genetic algorithm is used to optimize the input weight and hidden layer bias of the ELM. The optimized ELM model is proposed to solve the problem of the pattern recognition

whose model is complex and time-consuming. The validity of the proposed feature vectors and optimized ELM model are verified by the simulated data and the measured data. Compared with other methods

it is proved that the proposed model is simple and fast in training and classification

and has higher recognition accuracy. It is suitable for edge calculation and can identify the voltage sag sources accurately and fast.

关键词

Keywords

references

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