摘要
局部放电是设备处于高电场强下,由于电场分布不均而导致的绝缘介质放电现象,设备产生局部放电对于绝缘层的危害很大,迅速检测识别设备的放电类型是工业正常运作的保障。针对电气设备局部放电类型识别问题,考虑到电气设备监测系统在诊断识别方面的时效性及精度,提出了基于边缘计算的局部放电模式识别方法,利用边缘计算架构的优势,基于云层训练、边缘推理思路,将复杂的识别算法训练优化过程部署在云层,将计算量大的识别算法卸载到边缘层,而计算量小的特征提取保留在终端设备层处理。通过构造局部放电相位分布谱图提取局部放电的统计特征参数,采用粒子群优化算法对广义回归神经网络模型进行优化,最后将统计特征参数作为神经网络的输入量,对放电类型进行识别。结果表明,所提模式识别方法识别准确率高,识别效率高。
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.
作者
宋佳骏
刘守豹
熊中浩
Song Jiajun;Liu Shoubao;Xiong Zhonghao(Datang Hydropower Science&Technology Research Institute Co.,Ltd.,Chengdu 610074,China)
出处
《电子技术应用》
2022年第9期55-58,62,共5页
Application of Electronic Technique
基金
中国大唐集团有限公司科技项目(2021SD026)。
关键词
边缘计算
局部放电
模式识别
广义回归神经网络
edge computing
partial discharge
pattern recognition
generalized regression neural network
作者简介
宋佳骏(1995-),男,硕士,助理工程师,主要研究方向:电力设备状态监测;刘守豹(1983-),男,博士,高级工程师,主要研究方向:电力系统暂态分析与工程电磁场数值计算;熊中浩(1994-),男,硕士,专责工程师,主要研究方向:智能算法、优化控制。
