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基于TDOA和TS-PSO的变压器特高频局部放电空间定位方法 被引量:28

A Transformer Partial Discharge UHF Localization Method Based on TDOA and TS-PSO
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摘要 局部放电(partialdischarge,PD)诊断与定位有助于在老化早期发现高压电力设备故障位置,对制定检修方案具有重要参考价值。目前常利用多个特高频(ultra high frequency,UHF)传感器组成传感器阵列,并定义三维坐标系对变压器进行局部放电空间定位。该文研究基于能量积累法捕捉信号起始脉冲和基于到达时间差(timedifferenceofarrival,TDOA)算法实现定位的原理,构建以局部放电位置点坐标为未知数的非线性规划问题,并利用禁忌搜索-粒子群优化(tabu search particle swarm optimization,TS-PSO)算法进行最优解求解。该算法可以避免非线性方程组求解时不收敛、解不唯一以及最小二乘法对初值要求高等问题,既保证了求解的速度,又能保证解的唯一性与准确性。实验室测试和现场测试验证了定位结果的有效性。 Partial discharge(PD)diagnosis and location are beneficial to find fault positions of high-voltage power equipment in the early stage of the aging,which is of importance for the development of maintenance program.An array composed by multiple ultra high frequency(UHF)sensors is used to combine a three-dimensional coordinate system,to spatially localize the PD of transformers.In this paper,the principles of capturing the initial pulses based on the energy accumulation method and implementing the location algorithm based on time difference of arrival(TDOA)were mainly studied.A nonlinear programming problem set with the coordinates of the PD position as a variable was established,and the tabu search particle swarm optimization(TS-PSO)algorithm was used to solve the optimal solution.The algorithm can avoid problems,such as non-convergence,non-unique solutions and high initial value requirements.It not only ensures the speed of the solution,but also guarantees the uniqueness and accuracy of the solution.Laboratory tests and field tests verified the effectiveness of the positioning results.
作者 李沐 冯新岩 蔄晓琨 LI Mu;FENG Xinyan;MAN Xiaokun(Economic & Technology Research Institute,State Grid Shandong Electric Power Company,Ji’nan 250000,Shandong Province,China;State Grid Shandong Maintenance Company,Ji’nan 250018,Shandong Province,China;State Grid Shandong Electric Power Construction Company,Ji’nan 250000,Shandong Province,China)
出处 《中国电机工程学报》 EI CSCD 北大核心 2019年第6期1834-1842,共9页 Proceedings of the CSEE
关键词 局部放电 特高频(UHF) 定位算法 到达时间差(TDOA) 禁忌搜索-粒子群优化(TS-PSO) partial discharge(PD) ultra high frequency(UHF) location algorithm time difference of arrival(TDOA) tabu search particle swarm optimization(TS-PSO)
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