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基于差量特征提取与模糊聚类的非侵入式负荷监测方法
引用本文:孙毅,崔灿,陆俊,郝建红,刘向军.基于差量特征提取与模糊聚类的非侵入式负荷监测方法[J].电力系统自动化,2017,41(4):86-91.
作者姓名:孙毅  崔灿  陆俊  郝建红  刘向军
作者单位:华北电力大学电气与电子工程学院, 北京市 102206,华北电力大学电气与电子工程学院, 北京市 102206,华北电力大学电气与电子工程学院, 北京市 102206,华北电力大学电气与电子工程学院, 北京市 102206,华北电力大学电气与电子工程学院, 北京市 102206
基金项目:国家高技术研究发展计划(863计划)资助项目(SS2015AA050203);国家电网公司科技项目“智能电网用户行为理论与互动化模式研究”;中央高校基本科研业务费专项资金资助项目(2015XS05)
摘    要:现有非侵入式负荷监测(NILM)方法主要将电器功率大小作为特征值,对于低功率电器识别的准确性不够,无法满足精细化智能用电的应用需求。文中分析了多种家用电器的功率和谐波特征,并选取低功率电器差异最大的频域谐波幅值作为新的特征。在此基础上提出一种新的NILM方法,该方法采用差量特征提取方法获取任意时刻的特征值变化量并引入信息熵的方法,通过计算簇间熵来确定最佳聚类数和负荷相似度;再通过模糊聚类实现电器负荷数量及种类的聚类识别。实验结果表明,文中提出的NILM方法在不同场景下均具有良好的可靠性和鲁棒性,采用谐波特征后识别准确性有明显提升。

关 键 词:非侵入式负荷监测  电器特征分析  差量特征提取  模糊聚类
收稿时间:2015/12/2 0:00:00
修稿时间:2016/12/21 0:00:00

Non-intrusive Load Monitoring Method Based on Delta Feature Extraction and Fuzzy Clustering
SUN Yi,CUI Can,LU Jun,HAO Jianhong and LIU Xiangjun.Non-intrusive Load Monitoring Method Based on Delta Feature Extraction and Fuzzy Clustering[J].Automation of Electric Power Systems,2017,41(4):86-91.
Authors:SUN Yi  CUI Can  LU Jun  HAO Jianhong and LIU Xiangjun
Affiliation:School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China,School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China,School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China,School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China and School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Abstract:Power value is a main feature in existing non-intrusive load monitoring(NILM)methods, but it is not suitable for low power appliances to meet the fine demand of intelligent electricity. The current waveform, power and harmonic feature of multiple appliances are firstly analyzed. The most distinct harmonic amplitude in the frequency domain, for the low power appliances, is chosen as a new feature. A new NILM method is presented, in which the delta feature extraction is used to get variations of load features, with information entropy adopted to determine optimal cluster number and load similarity by calculating inter-cluster entropy. Fuzzy clustering is also used to monitor the quantity and kind of appliances. Finally, experiment results have proved that the proposed method has higher accuracy and stability, and identification accuracy of low power appliances is improved observably. This work is supported by National High Technology Research and Development Program of China(863 Program)(No. SS2015AA050203), State Grid Corporation of China and Fundamental Research Funds for the Central Universities(No. 2015XS05).
Keywords:non-intrusive load monitoring  appliance feature analysis  delta feature extraction  fuzzy clustering
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