计及低频减载动作的最大暂态频率偏移快速估计
|
摘要
随着大容量远距离高压直流输电工程建设和大规模可再生能源的接入,受端电网频率安全风险增大。针对大容量直流闭锁等可能触发低频减载的严重扰动,文中提出基于机器学习的电力系统最大暂态频率偏移快速估计方法。将问题分解为低频减载响应判断和最大频率偏移估计两个子问题,通过子模型交替求解估计最大暂态频率偏移;基于支持向量回归方法构建最大频率偏移估计子模型,以支持向量机为个体学习器构建基于Bagging集成学习的低频减载响应判断子模型;以运行方式信息和扰动信息为输入,采用ReliefF算法和主成分分析法对输入特征进行选择和提取,降低模型复杂度。以某多直流馈入受端系统为例构建最大暂态频率偏移估计模型,验证所提方法的准确性和适应性。
With integration of high voltage direct current(HVDC)transmission with large capacity and long distance and large scale of renewable generation,the risk of frequency security at the receiving-end of power systems is rising.Aiming at severe disturbances such as large-scale HVDC blocking which may trigger under-frequency load shedding(UFLS),a method for fast estimation of maximum transient frequency deviation is proposed based on machine learning.The problem is decomposed into two sub-problems i.e.UFLS response judgment and maximum frequency deviation estimation,respectively.The maximum transient frequency deviation is estimated by solving the sub-models alternately.Support vector regression method is used to establish the sub-model of maximum frequency deviation estimation,the Bagging ensemble learning method based on support vector machine is used to establish the sub-model of UFLS response judgement.Operation condition and disturbance information are regarded as inputs.ReliefF method and principal component analysis are introduced to select and extract input features to reduce the model complexity.A receiving-end power system with multiple HVDC links is taken as an example to build a maximum transient frequency deviation model and verify the accuracy and adaptability of the proposed method.
With integration of high voltage direct current(HVDC)transmission with large capacity and long distance and large scale of renewable generation,the risk of frequency security at the receiving-end of power systems is rising.Aiming at severe disturbances such as large-scale HVDC blocking which may trigger under-frequency load shedding(UFLS),a method for fast estimation of maximum transient frequency deviation is proposed based on machine learning.The problem is decomposed into two sub-problems i.e.UFLS response judgment and maximum frequency deviation estimation,respectively.The maximum transient frequency deviation is estimated by solving the sub-models alternately.Support vector regression method is used to establish the sub-model of maximum frequency deviation estimation,the Bagging ensemble learning method based on support vector machine is used to establish the sub-model of UFLS response judgement.Operation condition and disturbance information are regarded as inputs.ReliefF method and principal component analysis are introduced to select and extract input features to reduce the model complexity.A receiving-end power system with multiple HVDC links is taken as an example to build a maximum transient frequency deviation model and verify the accuracy and adaptability of the proposed method.
引文
[1]李明节.大规模特高压交直流混联电网特性分析与运行控制[J].电网技术,2016,40(4):985-991.LI Mingjie.Characteristic analysis and operational control of large-scale hybrid UHV AC/DC power grids[J].Power System Technology,2016,40(4):985-991.
[2]李卫东,晋萃萃,温可瑞,等.大功率缺失下主动频率响应控制初探[J].电力系统自动化,2018,42(8):22-30.DOI:10.7500/AEPS20170930010.LI Weidong,JIN Cuicui,WEN Kerui,et al.Active frequency response control under high-power loss[J].Automation of Electric Power Systems,2018,42(8):22-30.DOI:10.7500/AEPS20170930010.
[3]CHAN M L,DUNLOP R D,SCHWEPPE F.Dynamic equivalents for average system frequency behavior following major disturbances[J].IEEE Transactions on Power Apparatus and Systems,1972,PAS-91(4):1637-1642.
[4]ANDERSON P M,MIRHEYDAR M.A low-order system frequency response model[J].IEEE Transactions on Power Systems,1990,5(3):720-729.
[5]李常刚,刘玉田,张恒旭,等.基于直流潮流的电力系统频率响应分析方法[J].中国电机工程学报,2009,29(34):36-41.LI Changgang,LIU Yutian,ZHANG Hengxu,et al.Power system frequency response analysis based on the direct current load flow[J].Proceedings of the CSEE,2009,29(34):36-41.
[6]EGIDO I,FERNANDEZ-BERNALl F,CENTENO P,et al.Maximum frequency deviation calculation in small isolated power systems[J].IEEE Transactions on Power Systems,2009,24(4):1731-1738.
[7]MEDINA D R,RAPPOLD E,SANCHEZ O,et al.Fast assessment of frequency response of cold load pickup in power system restoration[J].IEEE Transactions on Power Systems,2016,31(4):3249-3256.
[8]KOTTICK D,OR O.Neural-networks for predicting the operation of an under-frequency load shedding system[J].IEEETransactions on Power Systems,1996,11(3):1350-1358.
[9]WU Yuankang.Frequency stability for an island power system:developing an intelligent preventive-corrective control mechanism for offshore location[J].IEEE Industry Applications Magazine,2017,23(2):74-87.
[10]DJUKANOVIC M B,POPOVIC D P,SOBAJIC D J.Prediction of power system frequency response after generator outages using neural nets[J].IEE Proceedings C-Generation,Transmission and Distribution,1993,140(5):389-398.
[11]薄其滨,王晓茹,刘克天.基于v-SVR的电力系统扰动后最低率预测[J].电力自动化设备,2015,35(7):83-88.BO Qibin,WANG Xiaoru,LIU Ketian.Minimum frequency prediction based on v-SVR for post-disturbance power system[J].Electric Power Automation Equipment,2015,35(7):83-88.
[12]MOHAMMAD A,TURAJ A.Adaptive scheme for local prediction of post-contingency power system frequency[J].Electric Power System Research,2014,107:240-249.
[13]文云峰,赵荣臻,肖友强,等.基于多层极限学习机的电力系统频率安全评估方法[J].电力系统自动化,2019,43(1):133-140.DOI:10.7500/AEPS20180629012.WEN Yunfeng,ZHAO Rongzhen,XIAO Youqiang,et al.Frequency safety assessment of power system based on multilayer extreme learning machine[J].Automation of Electric Power Systems,2019,43(1):133-140.DOI:10.7500/AEPS20180629012.
[14]畅广辉,刘涤尘,熊浩.基于多分辨率SVM回归估计的短期负荷预测[J].电力系统自动化,2007,31(9):37-41.CHANG Guanghui,LIU Dichen,XIONG Hao.Short term load forecasting based on multi-resolution SVM regression[J].Automation of Electric Power Systems,2007,31(9):37-41.
[15]XU Yan,ZHANG Rui,ZHAO Junhua.Assessing short-term voltage stability of electric power systems by a hierarchical intelligent system[J].IEEE Transactions on Neural Networks and Learning Systems,2016,27(8):1686-1696.
[16]周志华.机器学习[M].北京:清华大学出版社,2016.ZHOU Zhihua.Machine learning[M].Beijing:Tsinghua University Press,2016.
[17]WU Wenbin,PENG Mugen.A data mining approach combining k-means clustering with Bagging neural network for shot-term wind power forecasting[J].IEEE Internet of Things Journal,2017,4(4):979-986.
[18]骆柏锋,穆云飞,贾宏杰,等.基于负荷特征库的大用户供电接入决策方法[J].电力系统自动化,2018,42(6):66-72.DOI:10.7500/AEPS20170805001.LUO Bofeng,MU Yunfei,JIA Hongjie,et al.Decision method of power supply access for large consumers based on load feature library[J].Automation of Electric Power Systems,2018,42(6):66-72.DOI:10.7500/AEPS20170805001.
[19]黄建明,李晓明,瞿合祚,等.考虑小波奇异信息与不平衡数据集的输电线路故障识别方法[J].中国电机工程学报,2017,37(11):3099-3107.HUANG Jianming,LI Xiaoming,QU Hezuo,et al.Method for fault type identification of transmission line considering wavelet singular information and unbalanced data set[J].Proceedings of the CSEE,2017,37(11):3099-3107.
[20]王皓,孙宏斌,张伯明.基于混合互信息的特征选择方法及其在静态电压稳定评估中的应用[J].中国电机工程学报,2006,26(7):77-81.WANG Hao,SUN Hongbin,ZHANG Boming.Hybrid mutual information based feature selection method as applied to static voltage stability assessment in power systems[J].Proceedings of the CSEE,2006,26(7):77-81.
[21]王海燕,刘天琪,李兴源.基于蚁群优化算法的电力系统暂态稳定评估特征选择[J].电力系统保护与控制,2008,36(21):1-6.WANG Haiyan,LIU Tianqi,LI Xingyuan.Feature selection based on ant colony optimization for power system transient stability assessment[J].Power System Protection and Control,2008,36(21):1-6.
[22]陈晓琳,姬波,叶阳东.一种基于ReliefF特征加权的R-NIC算法[J].计算机工程,2015,41(4):161-165.CHEN Xiaolin,JI Bo,YE Yangdong.A R-NIC algorithm based on ReliefF feature weighting[J].Computer Engineering,2015,41(4):161-165.
[23]牟婷婷,陆微,王兰君,等.基于主成分分析的用电模式稳定性分析[J].电力系统自动化,2017,41(19):59-65.DOI:10.7500/AEPS20161222001.MOU Tingting,LU Wei,WANG Lanjun,et al.Stability analysis of consumption mode based on principal component analysis[J].Automation of Electric Power Systems,2017,41(19):59-65.DOI:10.7500/AEPS20161222001.
[24]司风琪,周建新,仇晓智,等.独立成分分析方法在电站热力过程数据检验中的应用[J].中国电机工程学报,2008,28(26):77-81.SI Fengqi,ZHOU Jianxin,QIU Xiaozhi,et al.Application of independent component analysis on the data validation of thermodynamic system in power plant[J].Proceedings of the CSEE,2008,28(26):77-81.
[25]管霖,王同文,唐宗顺.电网安全监测的智能化关键特征识别及稳定分区算法[J].电力系统自动化,2006,30(21):22-27.GUAN Lin,WANG Tongwen,TANG Zongshun.Intelligent algorithm for kernel feature identification and stability-based system division in power grid security monitoring[J].Automation of Electric Power Systems,2006,30(21):22-27.
[2]李卫东,晋萃萃,温可瑞,等.大功率缺失下主动频率响应控制初探[J].电力系统自动化,2018,42(8):22-30.DOI:10.7500/AEPS20170930010.LI Weidong,JIN Cuicui,WEN Kerui,et al.Active frequency response control under high-power loss[J].Automation of Electric Power Systems,2018,42(8):22-30.DOI:10.7500/AEPS20170930010.
[3]CHAN M L,DUNLOP R D,SCHWEPPE F.Dynamic equivalents for average system frequency behavior following major disturbances[J].IEEE Transactions on Power Apparatus and Systems,1972,PAS-91(4):1637-1642.
[4]ANDERSON P M,MIRHEYDAR M.A low-order system frequency response model[J].IEEE Transactions on Power Systems,1990,5(3):720-729.
[5]李常刚,刘玉田,张恒旭,等.基于直流潮流的电力系统频率响应分析方法[J].中国电机工程学报,2009,29(34):36-41.LI Changgang,LIU Yutian,ZHANG Hengxu,et al.Power system frequency response analysis based on the direct current load flow[J].Proceedings of the CSEE,2009,29(34):36-41.
[6]EGIDO I,FERNANDEZ-BERNALl F,CENTENO P,et al.Maximum frequency deviation calculation in small isolated power systems[J].IEEE Transactions on Power Systems,2009,24(4):1731-1738.
[7]MEDINA D R,RAPPOLD E,SANCHEZ O,et al.Fast assessment of frequency response of cold load pickup in power system restoration[J].IEEE Transactions on Power Systems,2016,31(4):3249-3256.
[8]KOTTICK D,OR O.Neural-networks for predicting the operation of an under-frequency load shedding system[J].IEEETransactions on Power Systems,1996,11(3):1350-1358.
[9]WU Yuankang.Frequency stability for an island power system:developing an intelligent preventive-corrective control mechanism for offshore location[J].IEEE Industry Applications Magazine,2017,23(2):74-87.
[10]DJUKANOVIC M B,POPOVIC D P,SOBAJIC D J.Prediction of power system frequency response after generator outages using neural nets[J].IEE Proceedings C-Generation,Transmission and Distribution,1993,140(5):389-398.
[11]薄其滨,王晓茹,刘克天.基于v-SVR的电力系统扰动后最低率预测[J].电力自动化设备,2015,35(7):83-88.BO Qibin,WANG Xiaoru,LIU Ketian.Minimum frequency prediction based on v-SVR for post-disturbance power system[J].Electric Power Automation Equipment,2015,35(7):83-88.
[12]MOHAMMAD A,TURAJ A.Adaptive scheme for local prediction of post-contingency power system frequency[J].Electric Power System Research,2014,107:240-249.
[13]文云峰,赵荣臻,肖友强,等.基于多层极限学习机的电力系统频率安全评估方法[J].电力系统自动化,2019,43(1):133-140.DOI:10.7500/AEPS20180629012.WEN Yunfeng,ZHAO Rongzhen,XIAO Youqiang,et al.Frequency safety assessment of power system based on multilayer extreme learning machine[J].Automation of Electric Power Systems,2019,43(1):133-140.DOI:10.7500/AEPS20180629012.
[14]畅广辉,刘涤尘,熊浩.基于多分辨率SVM回归估计的短期负荷预测[J].电力系统自动化,2007,31(9):37-41.CHANG Guanghui,LIU Dichen,XIONG Hao.Short term load forecasting based on multi-resolution SVM regression[J].Automation of Electric Power Systems,2007,31(9):37-41.
[15]XU Yan,ZHANG Rui,ZHAO Junhua.Assessing short-term voltage stability of electric power systems by a hierarchical intelligent system[J].IEEE Transactions on Neural Networks and Learning Systems,2016,27(8):1686-1696.
[16]周志华.机器学习[M].北京:清华大学出版社,2016.ZHOU Zhihua.Machine learning[M].Beijing:Tsinghua University Press,2016.
[17]WU Wenbin,PENG Mugen.A data mining approach combining k-means clustering with Bagging neural network for shot-term wind power forecasting[J].IEEE Internet of Things Journal,2017,4(4):979-986.
[18]骆柏锋,穆云飞,贾宏杰,等.基于负荷特征库的大用户供电接入决策方法[J].电力系统自动化,2018,42(6):66-72.DOI:10.7500/AEPS20170805001.LUO Bofeng,MU Yunfei,JIA Hongjie,et al.Decision method of power supply access for large consumers based on load feature library[J].Automation of Electric Power Systems,2018,42(6):66-72.DOI:10.7500/AEPS20170805001.
[19]黄建明,李晓明,瞿合祚,等.考虑小波奇异信息与不平衡数据集的输电线路故障识别方法[J].中国电机工程学报,2017,37(11):3099-3107.HUANG Jianming,LI Xiaoming,QU Hezuo,et al.Method for fault type identification of transmission line considering wavelet singular information and unbalanced data set[J].Proceedings of the CSEE,2017,37(11):3099-3107.
[20]王皓,孙宏斌,张伯明.基于混合互信息的特征选择方法及其在静态电压稳定评估中的应用[J].中国电机工程学报,2006,26(7):77-81.WANG Hao,SUN Hongbin,ZHANG Boming.Hybrid mutual information based feature selection method as applied to static voltage stability assessment in power systems[J].Proceedings of the CSEE,2006,26(7):77-81.
[21]王海燕,刘天琪,李兴源.基于蚁群优化算法的电力系统暂态稳定评估特征选择[J].电力系统保护与控制,2008,36(21):1-6.WANG Haiyan,LIU Tianqi,LI Xingyuan.Feature selection based on ant colony optimization for power system transient stability assessment[J].Power System Protection and Control,2008,36(21):1-6.
[22]陈晓琳,姬波,叶阳东.一种基于ReliefF特征加权的R-NIC算法[J].计算机工程,2015,41(4):161-165.CHEN Xiaolin,JI Bo,YE Yangdong.A R-NIC algorithm based on ReliefF feature weighting[J].Computer Engineering,2015,41(4):161-165.
[23]牟婷婷,陆微,王兰君,等.基于主成分分析的用电模式稳定性分析[J].电力系统自动化,2017,41(19):59-65.DOI:10.7500/AEPS20161222001.MOU Tingting,LU Wei,WANG Lanjun,et al.Stability analysis of consumption mode based on principal component analysis[J].Automation of Electric Power Systems,2017,41(19):59-65.DOI:10.7500/AEPS20161222001.
[24]司风琪,周建新,仇晓智,等.独立成分分析方法在电站热力过程数据检验中的应用[J].中国电机工程学报,2008,28(26):77-81.SI Fengqi,ZHOU Jianxin,QIU Xiaozhi,et al.Application of independent component analysis on the data validation of thermodynamic system in power plant[J].Proceedings of the CSEE,2008,28(26):77-81.
[25]管霖,王同文,唐宗顺.电网安全监测的智能化关键特征识别及稳定分区算法[J].电力系统自动化,2006,30(21):22-27.GUAN Lin,WANG Tongwen,TANG Zongshun.Intelligent algorithm for kernel feature identification and stability-based system division in power grid security monitoring[J].Automation of Electric Power Systems,2006,30(21):22-27.