高压局部放电定位模型转换求解与改进K-means聚类优化方法
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  • 英文篇名:Model conversion solution and improved K-means clustering optimizationmethod for partial discharge location of high voltage
  • 作者:王署东 ; 尹柏强 ; 何怡刚 ; 李兵 ; 佐磊 ; 侯金波
  • 英文作者:Wang Shudong;Yin Baiqiang;He Yigang;Li Bing;Zuo Lei;Hou Jinbo;School of Electrical Engineering and Automation, Hefei University of Technology;School of Electrical Engineering, Wuhan University;
  • 关键词:局部放电 ; 超声波 ; 非线性 ; K-means聚类 ; 定位
  • 英文关键词:partial discharge;;ultrasonic;;nonlinear;;K-means clustering;;location
  • 中文刊名:DZIY
  • 英文刊名:Journal of Electronic Measurement and Instrumentation
  • 机构:合肥工业大学电气与自动化工程学院;武汉大学电气工程学院;
  • 出版日期:2018-11-15
  • 出版单位:电子测量与仪器学报
  • 年:2018
  • 期:v.32;No.215
  • 基金:国家自然科学基金青年科学基金(61501162);; 无损检测技术教育部重点实验室(南昌航空大学)开放基金(JZ2015SJSY0018);; 国家自然科学基金(51577046);; 国家自然科学基金重点项目(51637004);; 国家重点研发计划“重大科学仪器设备开发”项目(2016YFF0102200)资助
  • 语种:中文;
  • 页:DZIY201811025
  • 页数:9
  • CN:11
  • ISSN:11-2488/TN
  • 分类号:183-191
摘要
非线性定位方程组的求解是变压器局部放电(PD)定位技术的重要手段。针对目前非线性定位方程组求解时出现的迭代算法的选择困难、迭代不收敛、运算时间长、对到时误差敏感等问题,提出了局部放电定位模型转换求解与改进K-means聚类优化方法,建立多超声波传感器定位模型。首先通过消除非线性定位方程组的二阶项,将非线性方程组转换为线性方程组,从而避免了求解方程组对迭代算法选择的依赖,运算时间短,不会出现迭代不收敛。当传感器数量大于6个时,一次测量并求解得到多样本初值,最后采用改进的K-means聚类算法对样本初值进行聚类优化,确定最优的PD源坐标,有效解决了因到时误差导致定位结果较差的难题。仿真与模拟实验表明,当到时误差小于0.03 ms时,定位误差为3.07 cm。
        The solution of nonlinear location equations is an important means of transformer partial discharge location technology. At present, there are many problems in solving nonlinear location equations, such as the difficulty of selection of iterative algorithm, the non-convergence of iteration, the long calculation time and the sensitivity to arrival error. Based on this, a partial discharge location model conversion solution and an improved K-means cluster optimization method were proposed, and a multi-ultrasonic sensor location model was established. Firstly, by eliminating the second-order terms of nonlinear location equations, they are transformed into linear equations, thus avoiding the dependence of solving equations on the selection of iterative algorithms. In this way, the calculation time is short and the iteration does not converge. When the number of sensors is more than 6, the initial values of multiple samples are obtained. Finally, the improved K-means clustering algorithm is used to cluster and optimize the initial value of the samples, to determine the optimal PD source coordinates, which effectively solves the problem of poor location result due to the arrival time error. The simulation experiments show that the location error is 3.07 cm when the arrival time error is less than 0.03 ms.
引文
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