摘要
提出一种基于双树复小波变换(DTCWT)-深度信念网络(DBN)的配电网内部过电压识别方法。将10 kV母线三相过电压信号进行双树复小波变换,再通过奇异值分解降维,将所得奇异值作为特征值输入训练好的深度信念网络分类器,实现对7种典型的内部过电压的类型识别。利用ATP/EMTP仿真数据和物理实验平台上的故障波形对所提算法进行训练和测试,并将之与其他分类算法进行对比。结果表明,相较于所列举的其他方法,所提算法具有更强的特征提取能力和更高的识别准确率。
A method based on Double Tree Complex Wavelet Transform(DTCWT)-Deep Belief Network(DBN)for overvoltage identification in distribution network is proposed.The three-phase overvoltage signal of the 10 kV bus is subjected to the dual-tree complex wavelet transform,and then the singular value is reduced by the singular value decomposition.The resulting singular value is input into the trained deep belief network classifier as the eigenvalue,and the seven typical internal overvoltage type identification types are realized.The proposed algorithm is trained and tested using ATP/EMTP simulation data and fault waveforms on the physics experiment platform,and compared with other classification algorithms.The results show that compared with other methods listed in this paper,the proposed algorithm has stronger feature extraction capability and higher recognition accuracy.
作者
高伟
杨耿杰
郭谋发
杨川
GAO Wei;YANG Gengjie;GUO Moufa;YANG Chuan(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;China Energy Construction Group Yunnan Electric Power Design Institute Co.,Ltd.,Kunming 350002,China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2019年第9期80-89,共10页
Power System Protection and Control
基金
国家自然科学基金项目资助(51677030)
福建省自然科学基金项目资助(2016J01218)~~
关键词
配电网
内部过电压
类型识别
双树复小波
深度信念网络
distribution network
internal overvoltage
type identification
double tree complex wavelet
deep belief network
