基于ε-模糊树方法的电力系统状态估计
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摘要
电力系统状态估计在能量管理系统中起着重要的作用。为了提高状态估计的整体性能,提出了基于ε-模糊树方法(ε-FT)的电力系统状态估计方法。以福州电网500 kV母线为研究对象,通过网络结构分析,对各量测量进行信息提取和变量选择,将最优的变量子集作为ε-FT的输入变量,建立了各母线电压幅值的ε-FT模型,并与其他方法进行了对比。随后,在量测量中加入不良数据,验证所提状态估计方法的鲁棒性。结果表明,该方法能够有效地抵抗量测量中的不良数据,具有较高的估计精度和较强的鲁棒性,并且能将不良数据限制在局部,减少对整个电网状态估计的影响。
Power system state estimation plays an important role in Energy Management System(EMS). To improve the performance of state estimation, a power system state estimation method based on ε-fuzzy tree(ε-FT) is proposed. Taken500 k V bus of Fuzhou power grid as the research target, the network structure is analyzed to extract the measurement variables information and select the variables, the obtained feature matrix is used as the input of ε-FT to establish theε-FT model of 500 kV bus voltage amplitude, which is made a comparison with other modeling methods. Then, the bad data of measurement variables is used to validate the robustness of the proposed state estimation method. The results show that the method can effectively resist the bad data of measurement variables, and have higher prediction accuracy and robustness.Besides, the bad data can be limited locally to reduce the impact on the entire power grid state estimation.
Power system state estimation plays an important role in Energy Management System(EMS). To improve the performance of state estimation, a power system state estimation method based on ε-fuzzy tree(ε-FT) is proposed. Taken500 k V bus of Fuzhou power grid as the research target, the network structure is analyzed to extract the measurement variables information and select the variables, the obtained feature matrix is used as the input of ε-FT to establish theε-FT model of 500 kV bus voltage amplitude, which is made a comparison with other modeling methods. Then, the bad data of measurement variables is used to validate the robustness of the proposed state estimation method. The results show that the method can effectively resist the bad data of measurement variables, and have higher prediction accuracy and robustness.Besides, the bad data can be limited locally to reduce the impact on the entire power grid state estimation.
引文
[1]于尔铿.电力系统状态估计[M].北京:水利电力出版社,1985.
[2]丁宏恩,戴则梅,霍雪松,等.基于混合量测的二次线性状态估计方法及其工程应用[J].电力系统保护与控制,2016,44(13):163-170.DING Hongen,DAI Zemei,HUO Xuesong,et al.Hybrid measurement based secondary linear state estimation method and its engineering application[J].Power System Protection and Control,2016,44(13):163-170.
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[7]刘科研,盛万兴,何开元,等.基于拉格朗日松弛技术的复杂有源配电网分布式状态估计[J].电力系统保护与控制,2017,45(15):125-132.LIU Keyan,SHENG Wanxing,HE Kaiyuan,et al.Distributed state estimation of complex active distribution network based on Lagrange relaxation technique[J].Power System Protection and Control,2017,45(15):125-132.
[8]谢仕炜,胡志坚,吴方劼,等.基于递推最小二乘法的多端口外网静态等值参数辨识方法[J].电力系统保护与控制,2018,46(3):26-34.XIE Shiwei,HU Zhijian,WU Fangjie,et al.Static equivalent parameter identification method of multi-port external network based on recursive least squares algorithm[J].Power System Protection and Control,2018,46(3):26-34.
[9]李春,卫志农,孙国强,等.考虑风力发电波动引起频率偏差的电力系统状态估计[J].电网技术,2015,39(5):1301-1306.LI Chun,WEI Zhinong,SUN Guoqiang,et al.State estimation of power system considering frequency deviation caused by fluctuation of wind power generation[J].Power System Technology,2015,39(5):1301-1306.
[10]牛胜锁,张达,梁志瑞,等.基于抗差总体最小二乘法的电力系统谐波状态估计[J].电力系统保护与控制,2014,42(11):106-111.NIU Shengsuo,ZHANG Da,LIANG Zhirui,et al.Power system harmonic state estimation based on robust total least squares[J].Power System Protection and Control,2014,42(11):106-111.
[11]黄知超,谢霞,王斌.结合模糊综合评价与决策的电力系统状态估计[J].电力系统保护与控制,2015,43(7):65-69.HUANG Zhichao,XIE Xia,WANG Bin.Power system state estimation combined with fuzzy comprehensive evaluation and decision-making[J].Power System Protection and Control,2015,43(7):65-69.
[12]喻圣,邹红波,余凡,等.模糊神经网络在电力系统短期负荷预测中的应用[J].智慧电力,2018,46(11):88-91.YU Sheng,ZOU Hongbo,YU Fan,et al.Application of fuzzy neural network in power short-term load forecasting[J].Smart Power,2018,46(11):88-91.
[13]HE Jinghan,LIU Lin,DING Fanfan,et al.A new coordinated backup protection scheme for distribution network containing distributed generation[J].Protection and Control of Modern Power Systems,2017,2(2):102-110.DOI:10.1186/s41601-017-0043-3.
[14]李国平,赵平,张晋维,等.考虑通讯时延的电力系统分布式经济调度[J].智慧电力,2018,46(11):14-20.LI Guoping,ZHAO Ping,ZHANG Jinwei,et al.Distributed economic dispatch of power system considering communication delay[J].Smart Power,2018,46(11):14-20.
[15]韩富春,王娟娟.基于神经网络的电力系统状态估计[J].电力系统及其自动化学报,2002,14(6):49-51.HAN Fuchun,WANG Juanjuan.State estimation in power system based on neural network[J].Proceedings of the CSU-EPSA,2002,14(6):49-51.
[16]祝滨,刘耀年,朱洪博.神经网络的同伦算法与电力系统状态估计[J].电力系统及其自动化学报,2000,12(1):11-14.ZHU Bin,LIU Yaonian,ZHU Hongbo.Homotopy method of neural network and power system state estimation[J].Proceedings of the CSU-EPSA,2000,12(1):11-14.
[17]陈刚,闫飞,龚啸,等.基于参数优化的最小二乘支持向量机状态估计方法[J].电力系统保护与控制,2011,39(19):83-88.CHEN Gang,YAN Fei,GONG Xiao,et al.State estimate based on parameter-optimized least square support vector machines[J].Power System Protection and Control,2011,39(19):83-88.
[18]贾嵘,蔡振华,刘晶,等.基于主成分分析和最小二乘支持向量机的电力系统状态估计[J].电网技术,2006,30(21):75-78.JIA Rong,CAI Zhenhua,LIU Jing,et al.Power system state estimation based on principal component analysis and least square support vector machines[J].Power System Technology,2006,30(21):75-78.
[19]罗玉春,王毅,戴则梅,等.基于多调度控制中心的分解协调状态估计[J].电网技术,2016,40(5):1516-1521.LUO Yuchun,WANG Yi,DAI Zemei,et al.Decomposition and coordination state estimation for multi dispatching and control centers[J].Power System Technology,2010,30(23):1516-1521.
[20]胡春潮,何杰,马凯.基于信息冗余的电力系统分布式状态估计[J].电力系统保护与控制,2017,45(16):111-115.HU Chunchao,HE Jie,MA Kai.Power system distributed state estimation based on information redundancy of secondary system[J].Power System Protection and Control,2017,45(16):111-115.
[21]ZHANG Wenguang,ZHANG Yue,BAI Xuejian,et al.Arobust fuzzy tree method with outlier detection for combustion models and optimization[J].Chemometrics and Intelligent Laboratory Systems,2016,158:130-137.
[22]MAO Jianqin,ZHANG Jiangang,YUE Yufang,et al.Adaptive-tree-structure-based fuzzy inference system[J].IEEE Transactions on Fuzzy Systems,2005,13(1):1-12.
[23]张伟,毛剑琴.基于最小Wilcoxon学习方法的模糊树模型[J].北京航空航天大学学报,2013,39(7):973-977.ZHANG Wei,MAO Jianqin.Least Wilcoxon learning method based fuzzy tree model[J].Journal of Beijing University of Aeronautics and Astronautics,2013,39(7):973-977.
[24]ZHANG Wei,MAO Jianqin.Fuzzy tree modeling based onε-insensitive learning method[C]//Proceedings of Conference on Industrial Electronics and Applications,IEEE,2011,Piscataway,NJ,United States:2074-2078.
[25]张文广,张越.基于PLS和ε-FT的电站锅炉NOx排放特性研究[J].动力工程学报,2017,37(3):186-191.ZHANG Wenguang,ZHANG Yue.Study on NOx emission of utility boilers based on partial least squares andε-fuzzy tree method[J].Journal of Chinese Society of Power Engineering,2017,37(3):186-191.
[2]丁宏恩,戴则梅,霍雪松,等.基于混合量测的二次线性状态估计方法及其工程应用[J].电力系统保护与控制,2016,44(13):163-170.DING Hongen,DAI Zemei,HUO Xuesong,et al.Hybrid measurement based secondary linear state estimation method and its engineering application[J].Power System Protection and Control,2016,44(13):163-170.
[3]成锐,刘文霞,金秋龙,等.基于分布估计和智能存储的复杂电网可靠性评估方法[J].中国电机工程学报,2017,37(19):5541-5548.CHENG Rui,LIU Wenxia,JIN Qiulong,et al.Reliability evaluation in composite power system based on estimation of distribution algorithm and intelligent storage[J].Proceedings of the CSEE,2017,37(19):5541-5548.
[4]卢伟,林建泉,刘柏林.一种鲁棒预测辅助电力系统状态估计算法[J].现代电力,2017,34(4):33-39.LU Wei,LIN Jianquan,LIU Bolin.A robust forecasting-aided state estimation algorithm for power system[J].Modern Electric Power,2017,34(4):33-39.
[5]公茂法,柳岩妮,姜文,等.基于可信状态集合的状态估计方法在最优潮流中的应用[J].电力系统保护与控制,2016,44(17):78-82.GONG Maofa,LIU Yanni,JIANG Wen,et al.State estimation method based on trusted state set in the application of the optimal power flow[J].Power System Protection and Control,2016,44(17):78-82.
[6]蔡永智,陈皓勇,万楚林.基于最小二乘估计融合的分布式电力系统动态状态估计[J].电力系统自动化,2016,40(8):29-35.CAI Yongzhi,CHEN Haoyong,WAN Chulin.Distributed dynamic state estimation for power systems based on least square estimation fusion[J].Automation of Electric Power Systems,2016,40(8):29-35.
[7]刘科研,盛万兴,何开元,等.基于拉格朗日松弛技术的复杂有源配电网分布式状态估计[J].电力系统保护与控制,2017,45(15):125-132.LIU Keyan,SHENG Wanxing,HE Kaiyuan,et al.Distributed state estimation of complex active distribution network based on Lagrange relaxation technique[J].Power System Protection and Control,2017,45(15):125-132.
[8]谢仕炜,胡志坚,吴方劼,等.基于递推最小二乘法的多端口外网静态等值参数辨识方法[J].电力系统保护与控制,2018,46(3):26-34.XIE Shiwei,HU Zhijian,WU Fangjie,et al.Static equivalent parameter identification method of multi-port external network based on recursive least squares algorithm[J].Power System Protection and Control,2018,46(3):26-34.
[9]李春,卫志农,孙国强,等.考虑风力发电波动引起频率偏差的电力系统状态估计[J].电网技术,2015,39(5):1301-1306.LI Chun,WEI Zhinong,SUN Guoqiang,et al.State estimation of power system considering frequency deviation caused by fluctuation of wind power generation[J].Power System Technology,2015,39(5):1301-1306.
[10]牛胜锁,张达,梁志瑞,等.基于抗差总体最小二乘法的电力系统谐波状态估计[J].电力系统保护与控制,2014,42(11):106-111.NIU Shengsuo,ZHANG Da,LIANG Zhirui,et al.Power system harmonic state estimation based on robust total least squares[J].Power System Protection and Control,2014,42(11):106-111.
[11]黄知超,谢霞,王斌.结合模糊综合评价与决策的电力系统状态估计[J].电力系统保护与控制,2015,43(7):65-69.HUANG Zhichao,XIE Xia,WANG Bin.Power system state estimation combined with fuzzy comprehensive evaluation and decision-making[J].Power System Protection and Control,2015,43(7):65-69.
[12]喻圣,邹红波,余凡,等.模糊神经网络在电力系统短期负荷预测中的应用[J].智慧电力,2018,46(11):88-91.YU Sheng,ZOU Hongbo,YU Fan,et al.Application of fuzzy neural network in power short-term load forecasting[J].Smart Power,2018,46(11):88-91.
[13]HE Jinghan,LIU Lin,DING Fanfan,et al.A new coordinated backup protection scheme for distribution network containing distributed generation[J].Protection and Control of Modern Power Systems,2017,2(2):102-110.DOI:10.1186/s41601-017-0043-3.
[14]李国平,赵平,张晋维,等.考虑通讯时延的电力系统分布式经济调度[J].智慧电力,2018,46(11):14-20.LI Guoping,ZHAO Ping,ZHANG Jinwei,et al.Distributed economic dispatch of power system considering communication delay[J].Smart Power,2018,46(11):14-20.
[15]韩富春,王娟娟.基于神经网络的电力系统状态估计[J].电力系统及其自动化学报,2002,14(6):49-51.HAN Fuchun,WANG Juanjuan.State estimation in power system based on neural network[J].Proceedings of the CSU-EPSA,2002,14(6):49-51.
[16]祝滨,刘耀年,朱洪博.神经网络的同伦算法与电力系统状态估计[J].电力系统及其自动化学报,2000,12(1):11-14.ZHU Bin,LIU Yaonian,ZHU Hongbo.Homotopy method of neural network and power system state estimation[J].Proceedings of the CSU-EPSA,2000,12(1):11-14.
[17]陈刚,闫飞,龚啸,等.基于参数优化的最小二乘支持向量机状态估计方法[J].电力系统保护与控制,2011,39(19):83-88.CHEN Gang,YAN Fei,GONG Xiao,et al.State estimate based on parameter-optimized least square support vector machines[J].Power System Protection and Control,2011,39(19):83-88.
[18]贾嵘,蔡振华,刘晶,等.基于主成分分析和最小二乘支持向量机的电力系统状态估计[J].电网技术,2006,30(21):75-78.JIA Rong,CAI Zhenhua,LIU Jing,et al.Power system state estimation based on principal component analysis and least square support vector machines[J].Power System Technology,2006,30(21):75-78.
[19]罗玉春,王毅,戴则梅,等.基于多调度控制中心的分解协调状态估计[J].电网技术,2016,40(5):1516-1521.LUO Yuchun,WANG Yi,DAI Zemei,et al.Decomposition and coordination state estimation for multi dispatching and control centers[J].Power System Technology,2010,30(23):1516-1521.
[20]胡春潮,何杰,马凯.基于信息冗余的电力系统分布式状态估计[J].电力系统保护与控制,2017,45(16):111-115.HU Chunchao,HE Jie,MA Kai.Power system distributed state estimation based on information redundancy of secondary system[J].Power System Protection and Control,2017,45(16):111-115.
[21]ZHANG Wenguang,ZHANG Yue,BAI Xuejian,et al.Arobust fuzzy tree method with outlier detection for combustion models and optimization[J].Chemometrics and Intelligent Laboratory Systems,2016,158:130-137.
[22]MAO Jianqin,ZHANG Jiangang,YUE Yufang,et al.Adaptive-tree-structure-based fuzzy inference system[J].IEEE Transactions on Fuzzy Systems,2005,13(1):1-12.
[23]张伟,毛剑琴.基于最小Wilcoxon学习方法的模糊树模型[J].北京航空航天大学学报,2013,39(7):973-977.ZHANG Wei,MAO Jianqin.Least Wilcoxon learning method based fuzzy tree model[J].Journal of Beijing University of Aeronautics and Astronautics,2013,39(7):973-977.
[24]ZHANG Wei,MAO Jianqin.Fuzzy tree modeling based onε-insensitive learning method[C]//Proceedings of Conference on Industrial Electronics and Applications,IEEE,2011,Piscataway,NJ,United States:2074-2078.
[25]张文广,张越.基于PLS和ε-FT的电站锅炉NOx排放特性研究[J].动力工程学报,2017,37(3):186-191.ZHANG Wenguang,ZHANG Yue.Study on NOx emission of utility boilers based on partial least squares andε-fuzzy tree method[J].Journal of Chinese Society of Power Engineering,2017,37(3):186-191.