中圖分類號： TN911.72,；TM714 文獻標識碼： A DOI：10.16157/j.issn.0258-7998.212334 中文引用格式： 王毅，王蕭陽,，李松濃,，等. 基于單分類結合模糊寬度學習的負荷辨識方法[J].電子技術應用，2022,，48(5)：51-55,，60. 英文引用格式： Wang Yi，Wang Xiaoyang，Li Songnong,，et al. Load identification method based on one class classification combined with fuzzy broad learning[J]. Application of Electronic Technique,，2022，48(5)：51-55,，60.
Load identification method based on one class classification combined with fuzzy broad learning
Wang Yi1,，Wang Xiaoyang1，Li Songnong2,，Chen Tao2,，Hou Xingzhe2，F(xiàn)u Xiuyuan3
1.Communication and Information Engineering College,，Chongqing University of Posts and Telecommunications,， Chongqing 400065，China,； 2.Chongqing Electric Power Research Institute,，Chongqing 400014，China,； 3.State Power Investment Group Digital Technology Co.,，Ltd.，Beijing 100080,，China
Abstract： Non-Intrusive Load Monitoring(NILM) is a key technology for smart electricity consumption, which helps strengthen load-side management and improve electricity efficiency. With the rapid increase of power load types and quantities, when unknown electrical appliances outside the training sample are connected to the model, it will cause the model to misjudge and reduce the accuracy of load identification. In order to improve the stability and accuracy of the load identification model, a power load identification method combining single classification and fuzzy broad learning is proposed. The one-class K-nearest neighbor method is used to screen samples to detect unknown electrical appliances and control the risk of misjudgment. Considering the recognition rate and model complexity, the fuzzy broad learning system is used to classify and recognize the screened samples. The experimental results show that the algorithm proposed in this paper can effectively detect unknown electrical appliances, prevent model misjudgment, and get better results for both single-load and multi-load switching.
Key words : non-intrusive load identification,；steady-state feature of load current；fuzzy broad learning system,；one class K-nearest neighbor,；TS fuzzy syste
