摘要
现有的基于特高频（UHF）信号的局部放电时延定位方法研究重点多集中于提高时差的计算精度,而对系统定位误差的校正鲜有涉及。为此,论文直接从系统定位误差入手,提出了一种基于多神经网络的定位误差修正算法。在极径r∈[2 m, 6 m]、r∈[6 m, 12 m]及r∈[12 m, 18 m]这3个区间分段内建立了相应的误差补偿网络,利用有限个标定点的时延误差来训练径向基（RBF）神经网络,以模拟系统定位误差的分布特性,并对实际定位结果进行修正。仿真及实验结果表明,通过误差补偿网络的修正,提高了定位精确度、降低了定位结果的离散程度,最终可将定位距离误差控制在0.5 m以内,方向角误差控制在6°以内。研究结果验证了所提算法的误差修正能力。
Current researches on partial discharge localization method which based on the time delay of ultra high frequency（UHF） signals mainly focus on improving the precision of signal time-delays, while the error correction of the localization system is seldom involved.We proposed a localization error correction algorithm based on multiple neural network. When the radius vector（r） ranges from 2 m to 6 m, 6 m to 12 m, and 12 m to 18 m, respectively, a corresponding error compensation network was established. The radial basis function（RBF） neural network was trained by a finite number of time delay errors measured at the calibration points. The established neural network can simulate the error distribution and can be used to correct the actual localization results. The simulation and test results show that, by utilizing the error correction network, the localization accuracy can be improved and the discretization degree of the results can be reduced as well. The localization distance error can be controlled within 0.5 meters and the error of orientation angle is less than 6°, which can verify the error correction capability of the proposed algorithm.
作者
周南
罗林根
高兆丽
沈大千
盛戈皞
江秀臣
ZHOU Nan;LUO Lingen;GAO Zhaoli;SHEN Daqian;SHENG Gehao;JIANG Xiuchen(Department of Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Jinan Power Supply Company,State Grid Shandong Electric Power Company,Jinan 250012,China;Guang＇an Power Supply Company,State Grid Sichuan Elec-tric Power Company,Guang＇an 638500,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2018年第11期3641-3648,共8页
High Voltage Engineering
基金
国家重点研发计划(2016YFB0902500)
2016年国家电网公司科技项目(基于特高频无线传感技术的变电站局放在线监测和定位关键技术研究)~~
关键词
局部放电
特高频信号
定位
时差法
RBF神经网络
多神经网络
误差修正
partial discharge
ultra-high frequency signal
localization
time difference method
RBF neural network
multiple neural network
error correction
