中圖分類號(hào)： TN91；TM85 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.222525 中文引用格式： 宋佳駿，劉守豹，熊中浩. 基于邊緣計(jì)算的局部放電模式識(shí)別[J].電子技術(shù)應(yīng)用，2022，48(9)：55-58，62. 英文引用格式： Song Jiajun，Liu Shoubao，Xiong Zhonghao. Partial discharge pattern recognition based on edge computing[J]. Application of Electronic Technique，2022，48(9)：55-58，62.
Partial discharge pattern recognition based on edge computing
Song Jiajun，Liu Shoubao，Xiong Zhonghao
Datang Hydropower Science & Technology Research Institute Co．，Ltd.，Chengdu 610074，China
Abstract： Partial discharge is the phenomenon of dielectric discharge caused by uneven distribution of electric field under high electric field intensity. Partial discharge of equipment does great harm to the insulation layer. Rapid detection and identification of the discharge type of equipment is the guarantee of normal industrial operation. For electrical equipment for partial discharge type recognition problem, considering the electrical equipment monitoring system in the diagnosis of the timeliness and accuracy of recognition, this paper puts forward the partial discharge pattern recognition method based on edge calculation, using the advantage of edge computing architectures, edge of reasoning based on training, the clouds, the complex recognition algorithm training optimization deployment in the clouds. The recognition algorithm with large computation is offloaded to the edge layer, while the feature extraction with small computation is reserved to the terminal device layer. The statistical characteristic parameters of pd were extracted by constructing pd phase distribution spectrum, and the generalized regression neural network model was optimized by particle swarm optimization algorithm. Finally, the statistical characteristic parameters were used as the input of the neural network to identify the discharge types. The results show that the proposed pattern recognition method has high recognition accuracy and efficiency.
Key words : edge computing；partial discharge；pattern recognition；generalized regression neural network
