中圖分類號(hào)： TM501.2 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.201209 中文引用格式： 王毅,，陳進(jìn),，李松濃，等. 基于Stacking模型融合的串聯(lián)故障電弧檢測(cè)[J].電子技術(shù)應(yīng)用,，2021,，47(11)：53-57. 英文引用格式： Wang Yi，Chen Jin,，Li Songnong,，et al. Series fault arc detection based on Stacking model fusion[J]. Application of Electronic Technique，2021,，47(11)：53-57.
Series fault arc detection based on Stacking model fusion
Wang Yi1,，Chen Jin1，Li Songnong2,，Chen Tao2,，Dai Liandan3，Xuan Shu3
1.School of Communication and Information Engineering,，Chongqing University of Posts and Telecommunications,， Chongqing 400065,，China； 2.Chongqing Electric Power Research Institute,，Chongqing 400014,，China； 3.State Grid Chongqing Electric Power Company Marketing Service Center,，Chongqing 400023,，China
Abstract： Aiming at the problems of missed detection and false detection due to different arc combustion levels and current distortion levels in low-voltage AC distribution networks, a time-domain arc fault detection method based on Stacking model fusion is proposed. The time domain features are extracted from the loop current, and the time domain features are formed into a feature matrix to optimize the parameters of the machine learning algorithm decision tree and the integrated learning algorithm random forest. Finally, the integrated learning algorithm is used as the base learner instead of the machine learning algorithm to build a low-voltage AC fault detection model through Stacking model fusion. The experiment collected a total of 96 970 groups of parallel currents of six electrical appliances. The results show that compared with non-integrated algorithms and other integrated algorithms, the proposed method has higher accuracy, precision and F1 index, and its model is more robust.
Key words : arc fault；current acquisition,；arc detection,；integrated machine learning；Stacking model fusion
