基于S变换与PSO-GRNN的行波精确检测方法  

A precise detection method of traveling wave based on S-transform and PSO-GRNN

作　　者：王帅 李泽文[1] 吴骢羽 邹睿奇 肖雨嫣 WANG Shuai;LI Zewen;WU Congyu;ZOU Ruiqi;XIAO Yuyan(Ministry of Engineering Center for Power System Security and Supervisory Control Technology,Changsha University of Science&Technology,Changsha 410114,China)

机构地区：[1]长沙理工大学电网安全监控技术教育部工程研究中心,湖南长沙410114

出　　处：《电力科学与技术学报》2024年第6期11-21,共11页Journal of Electric Power Science And Technology

基　　金：湖南省科技创新人才计划科技创新团队项目(2021RC4061);长沙理工大学研究生“实践创新与创业能力提升计划”项目(SJCX202154)。

摘　　要：针对变电站设备产生反射行波,行波信号测量时存在入射波与反射波混叠的问题,提出一种基于S变换与粒子群优化广义回归神经网络(particle swarm optimization and generalized regression neural network,PSO-GRNN)算法的行波精确检测方法。首先,对混叠行波和真实入射行波信号分别进行S变换,得到两者的S矩阵;然后,对混叠行波和真实入射行波信号的S矩阵进行维数重构,将其转化为向量,并作为PSO-GRNN算法的输入和输出进行训练学习,建立分离混叠行波信号的网络模型;最后,根据此模型从混叠行波信号中分离出入射行波信号的S矩阵并进行S逆变换,得到纯净入射行波。仿真结果表明,分离出的入射行波陡度高、时频特征更突出,为提高现有行波保护的可靠性与行波定位的准确性提供了新思路。Reflected traveling waves are generated by substation equipment,and the incident waves and reflected waves are overlapped during traveling wave signal measurement.To address these issues,a precise detection method of traveling waves based on S-transform and particle swarm optimization and generated regression neural network algorithm(PSO-GRNN)is proposed.Firstly,the S matrixes of the overlapped traveling wave signal and the real incident traveling wave signal are obtained by the S transform,respectively.Secondly,the S matrixes of the overlapped traveling wave signal and the real incident traveling wave signal are reconstructed in terms of dimensionality into a vector,which is used as the input and output of PSO-GRNN for training and learning,and the network model for separating the overlapped traveling wave signal is established.Finally,according to this model,the S matrix of the incident traveling wave signal is separated from the overlapped traveling wave signal,and the S-inverse transform is performed to obtain the pure incident traveling wave.The simulation results show that the separated incident traveling wave has higher steepness and more prominent time-frequency characteristics,which provides a new idea to improve the reliability of existing traveling wave protection and the accuracy of traveling wave positioning.

关 键 词：变电站 行波检测 S变换 广义回归神经网络 S矩阵 

分 类 号：TM773[电气工程—电力系统及自动化]