电力系统自动化2017，Vol.41Issue(4)：86-91,6.DOI:10.7500/AEPS20160504016
基于差量特征提取与模糊聚类的非侵入式负荷监测方法
Non-intrusive Load Monitoring Method Based on Delta Feature Extraction and Fuzzy Clustering
摘要
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.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Key words
non-intrusive load monitoring/appliance feature analysis/delta feature extraction/fuzzy clustering引用本文复制引用
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):86-91,6.基金项目
国家高技术研究发展计划(863计划)资助项目(SS2015AA050203) （863计划）
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).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). （2015XS05）
