中圖分類號(hào)：TM721 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223024 中文引用格式： 王毅,，易歡,，李松濃，等. 基于VMD-LSTM的非侵入式負(fù)荷識(shí)別方法[J]. 電子技術(shù)應(yīng)用,，2023,，49(2)：127-132. 英文引用格式： Wang Yi，Yi Huan,，Li Songnong,，et al. Non-intrusive load identification method based on VMD-LSTM[J]. Application of Electronic Technique,，2023，49(2)：127-132.
Non-intrusive load identification method based on VMD-LSTM
Wang Yi1,，Yi Huan1,，Li Songnong2，F(xiàn)eng Ling3,，Liu Qilie1,，Song Runan4
1.Communication and Information Engineering College， Chongqing University of Posts and Telecommunications,， Chongqing 400067,， China；2.Chongqing Electric Power Research Institute,， Chongqing 400014,， China； 3.Postdoctoral Workstation of the Chongqing Electric Power Corporation,， Chongqing 400014,， China； 4.China Electric Power Research Institute,，Beijing100192,，China
Abstract： Non-intrusive load monitoring (NILM) technology is only based on the current and voltage information of the main entrance of home power supply to obtain the electrical information of indoor electrical equipment. Improving the accuracy of load identification is of great significance to optimize the energy structure, improve the efficiency of power utilization and reduce energy consumption. Firstly, the normalized current signal is decomposed by using variational mode decomposition (VMD), and then the correlation coefficients between each component and the original current signal are calculated. The two components with the largest correlation coefficients are selected as the load characteristics and input into the trained LSTM neural network for identification. The test results of an example show that the recognition rate of this method is up to 99% on public data set PLAID and 96.6% on laboratory data set, which proves the effectiveness of this method.
Key words : variational mode decomposition；smart grid,；LSTM,；correlation coefficient
