Recognition Method of Voltage Sag Source Based on Optimized Extreme Learning Machine

Ying WANG; Huan WANG; Shu ZHANG

doi:10.7500/AEPS20190809004

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Recognition Method of Voltage Sag Source Based on Optimized Extreme Learning Machine

Application Research | 更新时间：2025-06-16

- Recognition Method of Voltage Sag Source Based on Optimized Extreme Learning Machine
- Automation of Electric Power Systems Vol. 44, Issue 9, Pages: 135-143(2020)
- 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：
- 基金信息：
- DOI：10.7500/AEPS20190809004
  CLC：
- Received：09 August 2019，
  
  Revised：2019-11-26，
  
  Published Online：04 March 2020，
  
  Published：10 May 2020
- 稿件说明：
移动端阅览
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

刘旭娜 , 肖先勇 , 汪颖 . 电压暂降严重程度及其测度、不确定性评估方法 [J]. 中国电机工程学报 , 2014 , 34 ( 4 ): 644 - 658 .

LIU Xuna , XIAO Xianyong , WANG Ying . Voltage sag severity and its measure and uncertainty evaluation [J]. Proceedings of the CSEE , 2014 , 34 ( 4 ): 644 - 658 .

徐永海 , 兰巧倩 , 洪旺松 . 交流接触器对电压暂降敏感度的试验研究 [J]. 电工技术学报 , 2015 , 30 ( 21 ): 136 - 146 .

XU Yonghai , LAN Qiaoqian , HONG Wangsong . Experimental research on AC contactor sensitivity during voltage sags [J]. Transactions of China Electrotechnical Society , 2015 , 30 ( 21 ): 136 - 146 .

崔灿 , 肖先勇 , 吴奎华 , 等 . 基于HHT和特征离散化的电压暂降扰动源分类特征提取 [J]. 电力系统保护与控制 , 2018 , 46 ( 24 ): 8 - 15 .

CUI Can , XIAO Xianyong , WU Kuihua , et al . Feature extraction in classification of voltage sag sources based on HHT and feature discretization [J]. Power System Protection and Control , 2018 , 46 ( 24 ): 8 - 15 .

王克星 , 宋政湘 , 陈德桂 , 等 . 基于小波变换的配电网电压暂降的干扰源辨识 [J]. 中国电机工程学报 , 2003 , 23 ( 6 ): 29 - 34 .

WANG Kexing , SONG Zhengxiang , CHEN Degui , et al . Interference source identification of voltage sag in distribution system based on wavelet transform [J]. Proceedings of the CSEE , 2003 , 23 ( 6 ): 29 - 34 .

祁博 , 邹金慧 , 范玉刚 , 等 . 基于Hilbert-Huang变换和小波包能量谱的电压暂降源识别 [J]. 中国电力 , 2013 , 46 ( 8 ): 112 - 117 .

QI Bo , ZOU Jinhui , FAN Yugang , et al . Identification of voltage sags source based on Hilbert-Huang transform and wavelet packet energy spectrum [J]. Electric Power , 2013 , 46 ( 8 ): 112 - 117 .

丁宁 , 蔡维 , 锁娟 , 等 . 电压暂降源识别方法研究 [J]. 电网技术 , 2008 , 32 ( 增刊2 ): 55 - 59 .

DING Ning , CAI Wei , SUO Juan , et al . Research on voltage sag sources recognition method [J]. Power System Technology , 2008 , 32 (Supplement 2 ): 55 - 59 .

李康瑞 , 杨洪耕 . 基于S变换标准模板相似度的电压暂降分类 [J]. 电工技术学报 , 2010 , 25 ( 12 ): 139 - 146 .

LI Kangrui , YANG Honggeng . S-transform based classification for voltage dips according to standard template similarity [J]. Transactions of China Electrotechnical Society , 2010 , 25 ( 12 ): 139 - 146 .

贾勇 , 何正友 , 赵静 . 基于小波熵和概率神经网络的配电网电压暂降源识别方法 [J]. 电网技术 , 2009 , 33 ( 16 ): 63 - 69 .

JIA Yong , HE Zhengyou , ZHAO Jing . A method to identify voltage sag sources in distribution network based on wavelet entropy and probability neural network [J]. Power System Technology , 2009 , 33 ( 16 ): 63 - 69 .

郑智聪 , 王红 , 齐林海 . 基于深度学习模型融合的电压暂降源识别方法 [J]. 中国电机工程学报 , 2019 , 39 ( 1 ): 97 - 104 .

ZHENG Zhicong , WANG Hong , QI Linhai . Recognition method of voltage sag sources based on deep learning models’ fusion [J]. Proceedings of the CSEE , 2019 , 39 ( 1 ): 97 - 104 .

李丹奇 , 梅飞 , 张宸宇 , 等 . 基于深度置信网络的电压暂降特征提取及源辨识方法 [J]. 电力系统自动化 , 2020 , 44 ( 4 ): 150 - 158 .

LI Danqi , MEI Fei , ZHANG Chenyu , et al . Deep belief network based method for feature extraction and source identification of voltage sag [J]. Automation of Electric Power Systems , 2020 , 44 ( 4 ): 150 - 158 .

李晨懿 , 杨家莉 , 徐永海 , 等 . 模糊综合评价在电压暂降源识别中的应用 [J]. 电网技术 , 2017 , 41 ( 3 ): 1022 - 1028 .

LI Chenyi , YANG Jiali , XU Yonghai , et al . Application of comprehensive fuzzy evaluation method on recognition of voltage sag disturbance sources [J]. Power System Technology , 2017 , 41 ( 3 ): 1022 - 1028 .

邱海峰 , 陈兵 , 袁晓冬 , 等 . 基于动态时间弯曲距离的电压暂降源辨识方法 [J]. 电力系统保护与控制 , 2017 , 45 ( 13 ): 7 - 13 .

QIU Haifeng , CHEN Bing , YUAN Xiaodong , et al . Identification of voltage sag sources based on DTW [J]. Power System Protection and Control , 2017 , 45 ( 13 ): 7 - 13 .

储佳伟 , 袁晓冬 , 陈兵 , 等 . 结合小波分析和改进型DTW距离的配电网电压暂降源辨识方法 [J]. 电网技术 , 2018 , 42 ( 2 ): 647 - 653 .

CHU Jiawei , YUAN Xiaodong , CHEN Bing , et al . A method for distribution network voltage sag source identification combining wavelet analysis and modified DTW distance [J]. Power System Technology , 2018 , 42 ( 2 ): 647 - 653 .

龚钢军 , 罗安琴 , 陈志敏 , 等 . 基于边缘计算的主动配电网信息物理系统 [J]. 电网技术 , 2018 , 42 ( 10 ): 3128 - 3135 .

GONG Gangjun , LUO Anqin , CHEN Zhimin , et al . Cyber physical system of active distribution network based on edge computing [J]. Power System Technology , 2018 , 42 ( 10 ): 3128 - 3135 .

杨帆 , 王干军 , 彭小圣 , 等 . 基于卷积神经网络的高压电缆局部放电模式识别 [J]. 电力自动化设备 , 2018 , 38 ( 5 ): 123 - 128 .

YANG Fan , WANG Ganjun , PENG Xiaosheng , et al . Partial discharge pattern recognition of high-voltage cables based on convolution neural network [J]. Electric Power Automation Equipment , 2018 , 38 ( 5 ): 123 - 128 .

陶维青 , 夏熠 , 陆鼎堃 . S变换熵理论及其在电力系统故障检测中的应用研究 [J]. 合肥工业大学学报(自然科学版) , 2016 , 39 ( 1 ): 40 - 45 .

TAO Weiqing , XIA Yi , LU Dingkun . Study of S-transform entropy theory and its application in fault detection of electric power system [J]. Journal of Hefei University of Technology (Natural Science) , 2016 , 39 ( 1 ): 40 - 45 .

陆俊 , 陈志敏 , 龚钢军 , 等 . 基于极限学习机的居民用电行为分类分析方法 [J]. 电力系统自动化 , 2019 , 43 ( 2 ): 97 - 104 .

LU Jun , CHEN Zhimin , GONG Gangjun , et al . Classification analysis method for electricity consumption behavior based on extreme learning machine algorithm [J]. Automation of Electric Power Systems , 2019 , 43 ( 2 ): 97 - 104 .

吕忠 , 周强 , 周琨 , 等 . 基于遗传算法改进极限学习机的变压器故障诊断 [J]. 高压电器 , 2015 , 51 ( 8 ): 49 - 53 .

LYU Zhong , ZHOU Qiang , ZHOU Kun , et al . Fault diagnosis of transformer based on extreme learning machine optimized by genetic algorithm [J]. High Voltage Apparatus , 2015 , 51 ( 8 ): 49 - 53 .

崔江 , 唐军祥 , 张卓然 , 等 . 基于极限学习机的航空发电机旋转整流器快速故障分类方法研究 [J]. 中国电机工程学报 , 2018 , 38 ( 8 ): 2458 - 2466 .

CUI Jiang , TANG Junxiang , ZHANG Zhuoran , et al . Fast fault classification method research of aircraft generator rotating rectifier based on extreme learning machine [J]. Proceedings of the CSEE , 2018 , 38 ( 8 ): 2458 - 2466 .

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