基于智能算法的10kV配电网线损评估
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摘要
为更全面、准确地评估10kV配电网线损水平,提出了一种基于粒子群算法(PSO)优化BP神经网络(BPNN)的较为准确有效的10kV配电网理论线损预测方法。首先筛选和构建5个电气特征指标描述10kV配电网结构和运行状态;其次采用惯性因子和学习因子动态调整的粒子群算法,全局搜索BP神经网络的权值和阈值来构建PSO-BPNN线损评估模型;通过对训练样本集的学习,拟合电气特征指标与线损之间的非线性关系,进而对测试样本集线损进行预测。最后应用某地区10kV配电网的实际样本数据验证了所提方法的有效性与合理性。
To estimate the level of 10kV distribution network line losses more integrally and precisely,an assessment method based on BP neural network(BPNN)improved by particle swarm optimization(PSO)is proposed.Firstly,5 electrical characteristic indexes are selected to reflect the structure and operation state of 10kV distribution network.Secondly,the inertia weight and the acceleration coefficient of PSO are dynamically adjusted so that the weights and biases of BPNN can be searched more effectively.Then,nonlinear relation between electrical characteristic indexes and line losses is fitted through the learning of training sample sets to predict the line losses of test sample sets.Finally,the PSO-BPNN is proved to be effective and proper through an actual 10kV distribution network sample data.
To estimate the level of 10kV distribution network line losses more integrally and precisely,an assessment method based on BP neural network(BPNN)improved by particle swarm optimization(PSO)is proposed.Firstly,5 electrical characteristic indexes are selected to reflect the structure and operation state of 10kV distribution network.Secondly,the inertia weight and the acceleration coefficient of PSO are dynamically adjusted so that the weights and biases of BPNN can be searched more effectively.Then,nonlinear relation between electrical characteristic indexes and line losses is fitted through the learning of training sample sets to predict the line losses of test sample sets.Finally,the PSO-BPNN is proved to be effective and proper through an actual 10kV distribution network sample data.
引文
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[17]牛东晓,谷志红,邢棉,等.基于数据挖掘的SVM短期负荷预测方法研究[J].中国电机工程学报,2006,26(18):6-12.NIU Dongxiao,GU Zhihong,XING Mian,et al.Study on forecasting approach to short-term load of SVMbased on data mining[J].Proceedings of the CSEE,2006,26(18):6-12.
[18]苏学能,刘天琪,曹鸿谦,等.基于Hadoop架构的多重分布式BP神经网络的短期负荷预测方法[J].中国电机工程学报,2017,37(17):4966-4973.SU Xueneng,LIU Tianqi,CAO Hongqian,et al.Amultiple distributed BP neural networks approach for short-term load forecasting based on hadoop framework[J].Proceedings of the CSEE,2017,37(17):4966-4973.
[19]李娜,李郁侠.基于粒子群-BP神经网络算法的电价预测[J].武汉大学学报(工学版),2008,41(4):102-105.LI Na,LI Yuxia.Electricity price forecast based on PSO-BP neural network[J].Engineering Journal of Wuhan University,2008,41(4):102-105.
[20]陈美伊,张鲲.粒子群算法优化特征和神经网络的模拟电路故障诊断[J].现代电子技术,2016,39(19):140-143.CHEN Meiyi,ZHANG Kun.Analog circuit fault diagnosis based on particle swarm optimization feature and neural network[J].Modern Electronics Technique,2016,39(19):140-143.
[21]姜雲腾,李萍.基于改进粒子群神经网络短期负荷预测[J].电气技术,2018,19(2):87-91.JIANG Yunteng,LI Ping.Short-term load forecasting based on improved particle swarm neural network[J].Electrical Engineering,2018,19(2):87-91
[2]张恺凯,杨秀媛,卜从容,等.基于负荷实测的配电网理论线损分析及降损对策[J].中国电机工程学报,2013,33(S1):92-97.ZHANG Kaikai,YANG Xiuyuan,BU Congrong,et al.Theoretical analysis on distribution network loss based on load measurement and countermeasures to reduce the loss[J].Proceedings of the CSEE,2013,33(S1):92-97.
[3]鲍海,马千.电网线损的物理分布机理[J].中国电机工程学报,2005,25(21):85-89.BAO Hai,MA Qian.Physical distribution mechanism of network loss for power system[J].Proceedings of the CSEE,2005,25(21):85-89.
[4]王昌照.基于大数据的“三统一”配电网规划辅助决策系统[J].供用电,2017,34(1):32-37,23.WANG Changzhao.“Three Unified”decision-making support system of distribution network planning based on big data[J].Distribution&Utilization,2017,34(1):32-37,23.
[5]袁慧梅,郭喜庆,于海波.中压配电网线损计算新方法[J].电力系统自动化,2002,26(11):50-53.YUAN Huimei,GUO Xiqing,YU Haibo.New method for calculation energy losses[J].Automation of Electric Power Systems,2002,26(11):50-53
[6]张东霞,王继业,刘科研,等.大数据技术在配用电系统的应用[J].供用电,2015,32(8):6-11.ZHANG Dongxia,WANG Jiye,LIU Keyan,et al.Application of big data technologies in power distribution and utilization system[J].Distribution&Utilization,2015,32(8):6-11.
[7]张勇军,石辉,翟伟芳,等.基于层次分析法-灰色综合关联及多灰色模型组合建模的线损率预测[J].电网技术,2011,35(6):71-76.ZHANG Yongjun,SHI Hui,ZHAI Weifang,et al.Line loss rate prediction based on combinatorial modeling by analytic hierarchy process-gray comprehensive relationship and multi grey models[J].Power System Technology,2011,35(6):71-76.
[8]刘伟,李恒,孙福杰,等.基于GIS和区间FTU的配电网网损计算方法[J].电网技术,2003,27(4):68-71.LIU Wei,LI Heng,SUN Fujie,et al.A new GIS and sectional FTU based calculation method for transmission losses in distribution networks[J].Power System Technology,2003,27(4):68-71.
[9]文福拴,韩祯祥.基于分群算法和人工神经元网络的配电网线损计算[J].中国电机工程学报,1993,13(3):43-53.WEN Fushuan,HAN Zhenxiang.The calculation of energy losses in distribution systems based on upon a clustering algorithm and an artificial neural network model[J].Proceedings of the CSEE,1993,13(3):41-50.
[10]卢志刚,李爽.基于直接神经动态规划的电网状态估计及线损理论计算[J].电网技术,2008,32(23):50-55.LU Zhigang,LI Shuang.Power system state estimation and theoretical line loss calculation based on direct neural dynamic programming[J].Power System Technology,2008,32(23):50-55.
[11]王吉权.BP神经网络的理论及其在农业机械化中的应用研究[D].沈阳:沈阳农业大学,2011.
[12]李亚,刘丽平,李柏青,等.基于改进K-Means聚类和BP神经网络的台区线损率计算方法[J].中国电机工程学报,2016,36(17):4543-4552.LI Ya,LIU Liping,LI Baiqing,et al.Calculation of line loss rate in transformer district based on improved K-means clustering algorithm and BP neural network[J].Proceedings of the CSEE,2016,36(17):4543-4552.
[13]KIM H,KO Y,JUNG K H.Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems[J].IEEE Transactions on Power Delivery,1993,8(3):1356-1366.
[14]李姝,龚佳怡,莫颖涛.10 kV及以下电网线损精细化管理[J].供用电,2009,26(3):65-77.LI Shu,GONG Jiayi,MO Yingtao.Meticulous management for the line loss of 10 kV and below power network[J].Distribution&Utilization,2009,26(3):65-77.
[15]邓芳.配网线损实时统计与分析系统[J].电网技术,2007,31(S1):186-188.DENG Fang.Real time measurement and analysis system of line losses in distribution networks[J].Power System Technology,2007,31(S1):186-188.
[16]郭小莜,王传旭,刘洋.基于SCADA系统的电网线损实时计算功能设计[J].供用电,2013,30(1):67-70.GUO Xiaoyou,WANG Chuanxu,LIU Yang.Realtime line loss calculation of power grid based on SCADAsystem[J].Distribution&Utilization,2013,30(1):67-70.
[17]牛东晓,谷志红,邢棉,等.基于数据挖掘的SVM短期负荷预测方法研究[J].中国电机工程学报,2006,26(18):6-12.NIU Dongxiao,GU Zhihong,XING Mian,et al.Study on forecasting approach to short-term load of SVMbased on data mining[J].Proceedings of the CSEE,2006,26(18):6-12.
[18]苏学能,刘天琪,曹鸿谦,等.基于Hadoop架构的多重分布式BP神经网络的短期负荷预测方法[J].中国电机工程学报,2017,37(17):4966-4973.SU Xueneng,LIU Tianqi,CAO Hongqian,et al.Amultiple distributed BP neural networks approach for short-term load forecasting based on hadoop framework[J].Proceedings of the CSEE,2017,37(17):4966-4973.
[19]李娜,李郁侠.基于粒子群-BP神经网络算法的电价预测[J].武汉大学学报(工学版),2008,41(4):102-105.LI Na,LI Yuxia.Electricity price forecast based on PSO-BP neural network[J].Engineering Journal of Wuhan University,2008,41(4):102-105.
[20]陈美伊,张鲲.粒子群算法优化特征和神经网络的模拟电路故障诊断[J].现代电子技术,2016,39(19):140-143.CHEN Meiyi,ZHANG Kun.Analog circuit fault diagnosis based on particle swarm optimization feature and neural network[J].Modern Electronics Technique,2016,39(19):140-143.
[21]姜雲腾,李萍.基于改进粒子群神经网络短期负荷预测[J].电气技术,2018,19(2):87-91.JIANG Yunteng,LI Ping.Short-term load forecasting based on improved particle swarm neural network[J].Electrical Engineering,2018,19(2):87-91