基于声阵列时空关联特征融合的不平衡局部放电类型识别方法  

Pattern Recognition of Partial Discharge Using Imbalanced Acoustic Array Data Based on Spatial Correlation and Temporal Correlation Feature Fusion Method

作　　者：王红霞 王波[1,2] 张嘉鑫 尚宇炜[4] 周莉梅 刘畅 WANG Hongxia;WANG Bo;ZHANG Jiaxin;SHANG Yuwei;ZHOU Limei;LIU Chang(Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network,Wuhan 430072,China;School of Electrical Engineering and Automation,Wuhan University,Wuhan 430074,China;Electrical and Computer Engineering Department,University of Denver,Denver 80208,USA;China Electric Power Research Institute Co.,Ltd.,Beijing 100080,China;Chengdu Power Supply Company,State Grid Sichuan Electric Power Company,Chengdu 610041,China)

机构地区：[1]交直流智能配电网湖北省工程中心,武汉430072 [2]武汉大学电气与自动化学院,武汉430074 [3]丹佛大学电气与计算机工程系,丹佛80208 [4]中国电力科学研究院有限公司,北京100080 [5]国网四川省电力公司成都供电公司,成都610041

出　　处：《高电压技术》2024年第5期1913-1922,共10页High Voltage Engineering

基　　金：国家电网有限公司科技项目(5400-202155497A-0-5-ZN)。

摘　　要：麦克风阵列能非接触且灵活地对电力设备局部放电现象进行检测,但现有方法对麦克风阵列的数据特点考虑不足,对局放类型识别的研究不足。针对麦克风阵列数据的关联性特征和不平衡分布特点,首先对麦克风阵列数据的时间关联性和空间关联性特征进行深入分析。然后,以1维卷积神经网络和压缩-激活关联性挖掘方法为基础,提出基于时空关联特征融合的声阵列数据局部放电类型识别模型。最后,针对麦克风阵列数据类别间分布不平衡问题,使用损失函数调整法和数据分布调整法进行应对。仿真结果表明:相对不考虑关联性的方法,该文所提方法的精确率、召回率提升均大于12%;相对不考虑样本不均衡性方法,该文所用方法在精确率和召回率均提高大于60%,验证了基于声阵列数据的局放类型识别中考虑数据关联性和不平衡性的必要性。Microphone array can detect partial discharge(PD)of power equipment in a non-contact and flexible way.However,existing methods lack the consideration of data characteristics of acoustic array,and the researches on identification of PD type are insufficient.Considering the correlation and imbalanced distribution features,this paper firstly analyzes the temporal and spatial correlation characteristics of microphone array data.Secondly,based on one-dimensional convolutional neural network and“squeeze-and-excitation”correlation extraction method,a PD pattern recognition model based on spatial and temporal correlation feature fusion strategy is proposed.Finally,the loss function adjustment method and data distribution adjustment method are used to deal with the imbalance between different PD classes.Simulations show that,compared with the methods in which the correlations are not taken into consideration,the methods proposed in this paper enhance both the precision and recall by more than 12%.Compared with the methods in which the data imbalance is not taken into consideration,the methods improve the precision and recall by over 60%,respectively.These results affirm the essential need to consider both correlation and imbalance characteristics in acoustic array based PD recognition.

关 键 词：声阵列 局部放电 时空关联性 特征融合 不平衡数据 

分 类 号：TM855[电气工程—高电压与绝缘技术] TN641[电子电信—电路与系统]