基于Tikhonov正则化算法的低压电网中电力设备工频电场逆向监测
|
摘要
针对遗传算法在电力设备工频电场逆向监测迭代中未获取最优参数,导致寻找精确解效率低问题,提出基于遗传算法的改进Tikhonov正则化方法,用于低压电网中电力设备工频电场逆向监测。利用专用探头与仪器测量工频电场,确定工频电场逆向监测参数,将该逆向监测参数作为遗传算法优化目标;选用2范式,确定Tikhonov正则化参数初始值,将参数初始值作为低压电网中电力设备工频电场搜索区间的初始种群,通过求解参数目标函数评估适应度函数值,利用遗传算法获取下一代个体,重复迭代获取最优参数,实现工频电场最佳逆向监测。经实验证明,该方法监测效率高,可在测量点出现故障时,准确监测到故障位置,在正常情况下、偏移情况下以及噪声情况下,该方法监测误差保持在1%～2%之间,监测精度高。
In order to solve the problem that genetic algorithm does not obtain the optimal parameters in the iteration of power equipment power frequency electric field reverse monitoring,which leads to the low efficiency of finding accurate solutions,an improved Tikhonov regularization method based on genetic algorithm is proposed to monitor power equipment power frequency electric field in low voltage power network. Using the special probe and instrument to measure the power frequency electric field,determine the reverse monitoring parameter of the power frequency electric field,and use the reverse monitoring parameter as the genetic algorithm optimization target; The 2 paradigm is selected to determine the initial value of the Tikhonov regularization parameter. The initial value of the parameter is used as the initial population of the power frequency electric field search interval of the power equipment in the low-voltage power grid,and the fitness function value is evaluated by solving the parameter objective function. The genetic algorithm is used to obtain the next generation of individuals,and the iterative process is used to obtain the optimal parameters to realize the optimal reverse monitoring of the power frequency electric field. The experimental results show that the method has a high monitoring efficiency and can accurately monitor the fault location when the measuring point is faulty. Under normal conditions,offset and noise conditions,the monitoring error of this method is kept between 1% and 2%,and the monitoring error of the method is between 1% and 2% under normal condition,offset condition and noise condition. The monitoring precision is high.
In order to solve the problem that genetic algorithm does not obtain the optimal parameters in the iteration of power equipment power frequency electric field reverse monitoring,which leads to the low efficiency of finding accurate solutions,an improved Tikhonov regularization method based on genetic algorithm is proposed to monitor power equipment power frequency electric field in low voltage power network. Using the special probe and instrument to measure the power frequency electric field,determine the reverse monitoring parameter of the power frequency electric field,and use the reverse monitoring parameter as the genetic algorithm optimization target; The 2 paradigm is selected to determine the initial value of the Tikhonov regularization parameter. The initial value of the parameter is used as the initial population of the power frequency electric field search interval of the power equipment in the low-voltage power grid,and the fitness function value is evaluated by solving the parameter objective function. The genetic algorithm is used to obtain the next generation of individuals,and the iterative process is used to obtain the optimal parameters to realize the optimal reverse monitoring of the power frequency electric field. The experimental results show that the method has a high monitoring efficiency and can accurately monitor the fault location when the measuring point is faulty. Under normal conditions,offset and noise conditions,the monitoring error of this method is kept between 1% and 2%,and the monitoring error of the method is between 1% and 2% under normal condition,offset condition and noise condition. The monitoring precision is high.
引文
[1]许波.新形势下对电气设备用电安全的探讨[J].现代工业经济和信息化,2017,33(3):115-116.
[2]丁坚勇,周凯,田世明,等.基于大数据技术的重要用户供电安全分析[J].电网技术,2016,40(8):2491-2495.
[3]肖峻,祖国强,贺琪博,等.配电网安全域的实证分析[J].电力系统自动化,2017,41(3):153-160.
[4]贾登,周志雄,骆学理,等.基于NI数据采集卡的ACFM无损检测样机设计[J].国外电子测量技术,2017,36(5):50-54.
[5] Li M. Research on High Impedance Fault Detection Method of Distribution Network Based on S Transform[C]//International Conference on Intelligent&Interactive Systems&Applications. 2017.
[6]陈宁,牛蔚然,张健,等.基于遗传算法的配电网无功补偿优化研究[J].电子设计工程,2017,25(6):58-61.
[7] Wen L M,Zhou M M,Li M,et al. Improved Tikhonov Regularization Method for Image Reconstruction[J]. Acta Metrologica Sinica,2018,39(5):679-683.
[8]陆俊,朱炎平,徐志强,等.面向输电线路监测的无线传感网络可靠路由方法研究[J].电网技术,2017,41(02):318-324.
[9]陈曦,朱洪昌,李香银,等.基于电场法的复合绝缘子内绝缘带电测试方法探讨[J].广西电力,2017,40(1):41-43.
[10] Zhang Z B,Zhu H C,Li Y X,et al. Load Identification of Virtual Iteration Based on Tikhonov Regularization and Model Reduction[J]. Journal of Hunan University(Natural Sciences),2017,44(2):53-59.
[11]李莉,朱永利,宋亚奇.电力设备监测数据的流式计算与动态可视化展示[J].电力建设,2017,38(5):91-97.
[12]余运俊,张燕飞,万晓凤,等.基于CAN总线的分布式光伏电力设备监测系统[J].电源技术,2016,40(7):1450-1453.
[13]陈敏维,邱文锋,张孔林,等.偏最小二乘回归法在介损在线监测误差分析中的应用[J].电力系统及其自动化学报,2018,30(2):50-55.
[14] Liu O,Jing X R. A Regularized OMP Detection Algorithm for Generalized Spatial Modulation Systems[J].Telecommunication Engineering,2018,58(1):78-83.
[15]冯兴田,孙添添,马文忠.一种基于智能改进算法的定制电力设备优化配置策略[J].电力系统保护与控制,2016,44(21):16-21.
[16]郭宁明,杨飞,覃剑,等.基于遗传算法及信号谱分析的电网故障定位方法[J].电力系统自动化,2016,40(15):79-85.
[17] Yang J F. An algorithmic review for total variation regularized data fitting problems in image processing[J]. Operations Research Transactions,2017,21(4):69-83.
[18]潘超,谌骏哲,刘士利.三维边界元法计算变电站空间工频电场环境[J].电测与仪表,2017,54(13):44-48.
[19] Guo N,Yang F,Qin J,et al. Grid Fault Location Method Based on Genetic Algorithm and Signal Spectrum Analysis[J]. Automation of Electric Power Systems,2016.
[2]丁坚勇,周凯,田世明,等.基于大数据技术的重要用户供电安全分析[J].电网技术,2016,40(8):2491-2495.
[3]肖峻,祖国强,贺琪博,等.配电网安全域的实证分析[J].电力系统自动化,2017,41(3):153-160.
[4]贾登,周志雄,骆学理,等.基于NI数据采集卡的ACFM无损检测样机设计[J].国外电子测量技术,2017,36(5):50-54.
[5] Li M. Research on High Impedance Fault Detection Method of Distribution Network Based on S Transform[C]//International Conference on Intelligent&Interactive Systems&Applications. 2017.
[6]陈宁,牛蔚然,张健,等.基于遗传算法的配电网无功补偿优化研究[J].电子设计工程,2017,25(6):58-61.
[7] Wen L M,Zhou M M,Li M,et al. Improved Tikhonov Regularization Method for Image Reconstruction[J]. Acta Metrologica Sinica,2018,39(5):679-683.
[8]陆俊,朱炎平,徐志强,等.面向输电线路监测的无线传感网络可靠路由方法研究[J].电网技术,2017,41(02):318-324.
[9]陈曦,朱洪昌,李香银,等.基于电场法的复合绝缘子内绝缘带电测试方法探讨[J].广西电力,2017,40(1):41-43.
[10] Zhang Z B,Zhu H C,Li Y X,et al. Load Identification of Virtual Iteration Based on Tikhonov Regularization and Model Reduction[J]. Journal of Hunan University(Natural Sciences),2017,44(2):53-59.
[11]李莉,朱永利,宋亚奇.电力设备监测数据的流式计算与动态可视化展示[J].电力建设,2017,38(5):91-97.
[12]余运俊,张燕飞,万晓凤,等.基于CAN总线的分布式光伏电力设备监测系统[J].电源技术,2016,40(7):1450-1453.
[13]陈敏维,邱文锋,张孔林,等.偏最小二乘回归法在介损在线监测误差分析中的应用[J].电力系统及其自动化学报,2018,30(2):50-55.
[14] Liu O,Jing X R. A Regularized OMP Detection Algorithm for Generalized Spatial Modulation Systems[J].Telecommunication Engineering,2018,58(1):78-83.
[15]冯兴田,孙添添,马文忠.一种基于智能改进算法的定制电力设备优化配置策略[J].电力系统保护与控制,2016,44(21):16-21.
[16]郭宁明,杨飞,覃剑,等.基于遗传算法及信号谱分析的电网故障定位方法[J].电力系统自动化,2016,40(15):79-85.
[17] Yang J F. An algorithmic review for total variation regularized data fitting problems in image processing[J]. Operations Research Transactions,2017,21(4):69-83.
[18]潘超,谌骏哲,刘士利.三维边界元法计算变电站空间工频电场环境[J].电测与仪表,2017,54(13):44-48.
[19] Guo N,Yang F,Qin J,et al. Grid Fault Location Method Based on Genetic Algorithm and Signal Spectrum Analysis[J]. Automation of Electric Power Systems,2016.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |