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文章摘要
基于广义S变换和DE-ELM的电能质量扰动信号分类*
Power Quality Disturbances Classification Using Generalized S-transform and Differential Evolution Extreme Learning Machine
Received:July 06, 2015  Revised:August 14, 2015
DOI:
中文关键词: 电能质量扰动  广义S变换  差分进化  极限学习机
英文关键词: power quality disturbances, generalized S-transform, Differential Evolution, Extreme Learning Machine
基金项目:国家自然科学基金项目(51307020);吉林省科技发展计划项目(20150520114JH);吉林市科技发展计划资助项目(201464052)
Author NameAffiliationE-mail
Zhang Weihui* College of Electrical Engineering,Northeast Dianli University zhangweihui_nedu@163.com 
Huang Nantian College of Electrical Engineering,Northeast Dianli University  
Yang Jincheng Electric Power Research Institute,Xinjiang Power Company  
Yang Yongjian Electric Power Research Institute,Xinjiang Power Company  
Wang Xinku Dezhou Power supply Company,Shandong Power Company  
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中文摘要:
      电能质量扰动信号分类对电能质量综合评估、扰动源定位治理具有重要意义。本文提出了一种基于广义S变换和差分进化优化极限学习机的电能质量扰动信号分类方法。首先改变S变换在不同频段的窗宽因子,以提高特征表现能力;然后,采用极限学习机作为扰动分类器,引入具有全局寻优功能的差分进化算法,优化极限学习机输入权值和隐藏层结点偏置,增强极限学习机的泛化能力,提高分类准确率。仿真对比实验表明,较支持向量机和极限学习机,新方法准确率高,抗噪性强,更适用于电能质量扰动识别工作。
英文摘要:
      The classification of power quality disturbance is important for the comprehensive evaluation of power quality and the management of disturbance source location. In this paper, a classification method for power quality disturbance based on generalized S transform and Differential Evolution Extreme Learning Machine is proposed. Firstly, window width factor of different frequency area in the S transform is changed to improve the performance of features. Secondly, Differential Evolution Extreme Learning Machine is used as the disturbance classifier, which adopting the differential evolution algorithm with global optimization. The Differential Evolution Extreme Learning Machine optimize the input weight and bias of hidden layer nodes to enhance the generalization ability of Extreme Learning Machine, and further improve the classification accuracy. As simulation experiments shown, comparing with the Support Vector Machine and Extreme Learning Machine, the new method can get higher accuracy and stronger noise resistance, so it is suitable for the identification of power quality disturbances.
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