中圖分類(lèi)號(hào)： TN915 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211451 中文引用格式： 王毅，徐元源，李松濃. 基于DAG-SVMS的非侵入式負(fù)荷識(shí)別方法[J].電子技術(shù)應(yīng)用，2021，47(10)：107-112. 英文引用格式： Wang Yi，Xu Yuanyuan，Li Songnong. Non-intrusive load identification method based on improved directed acyclic graph support vector machines[J]. Application of Electronic Technique，2021，47(10)：107-112.
Non-intrusive load identification method based on improved directed acyclic graph support vector machines
Wang Yi1，Xu Yuanyuan1，Li Songnong2
1.School of Communication and Information Engineering，Chongqing University of Posts and Telecommunications， Chongqing 400065，China； 2.Chongqing Electric Power Research Institute，Chongqing 404100，China
Abstract： Embedding non-intrusive load identification technology in the power supply entrance is conducive to promote building energy saving, realize power grid load forecasting, develop intelligent buildings and improve the construction of smart grid system. Therefore, this paper proposes a non-intrusive power load identification method based on directed acyclic graph support vector machines(DAG-SVMS). Firstly, the event detection of power system bus current signal is carried out. After the transient event is detected, the transient current waveform of the target load is separated and the features are extracted. Then, the features are input into the pre trained DAG-SVMS model for classification and identification. In order to improve the performance of the classifier, particle awarm optimization(PSO) algorithm is used to optimize the parameters of the DAG-SVMS model. In order to reduce the cumulative error, Gini index is proposed to optimize the node order of DAG-SVMS. The experimental results show that the proposed method has high recognition accuracy, fast recognition speed and feasibility.
Key words : non-intrusive load identification；transient event；DAG-SVMS model；Gini index；PSO algorithm
