|FENG Changsen,LIU Pan,WANG Jiaying,WEN Fushuan,ZHANG Youbing.Non-intrusive load monitoring algorithm of residential users using limited low-frequency information[J].Electric Power Automation Equipment,2023,43(11):181-187
|关键词: 非侵入式负荷监测 事件检测 互补集合经验模态分解 卷积神经网络 负荷识别
|Non-intrusive load monitoring algorithm of residential users using limited low-frequency information
FENG Changsen1, LIU Pan1, WANG Jiaying2, WEN Fushuan3,4, ZHANG Youbing1
1.College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China;2.Marketing Service Center of State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 311121, China;3.Hainan Institute, Zhejiang University, Sanya 572000, China;4.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
|The load monitoring algorithm which uses more information features or high-frequency sampling technology to improve the identification accuracy will increase the cost of information sampling stage and the difficulty of edge data processing, a non-intrusive load monitoring algorithm based on limited low-frequency information is proposed. An optimal event detector is designed, which collects aggregated load data according to a sliding window and judges the switching position of electrical appliances according to the statistical characteristic index. The power sequences before and after event occurrence are taken as the identification features, and the complementary ensemble empirical mode decomposition algorithm is used to decompose the power sequences into multi-order intrinsic mode functions and a final trend, a two-dimensional image of the decomposition results is drawn and input into the convolution neural network for training and identification, thus high-accuracy load identification is realized by only using limited low-frequency sampling information. The simulative results based on public dataset verify the effectiveness of the proposed algorithm.
|Key words: non-intrusive load monitoring event detection complementary ensemble empirical mode decomposition convolutional neural network load identification