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。
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,
出处
《电力系统自动化》
EI
CSCD
北大核心
2017年第4期86-91,共6页
Automation of Electric Power Systems
基金
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)~~
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
non-intrusive load monitoring
appliance feature analysis
delta feature extraction
fuzzy clustering
