主动配电网故障时刻和故障区域的同步数据统计特性识别方法
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摘要
针对主动配电网故障时刻确定和故障区域识别问题,将数据统计理论应用于同步数据的故障特征提取上,提出了一种使用同步数据统计特性进行主动配电网故障区域和故障时刻判别的方法。该方法首先使用微相量测量单元的量测数据构建历史数据集和在线数据集,然后通过求解在线数据相对历史数据的最大方差偏离方向来实现对系统运行状态的评价。为求解方向问题,文中将最大方差偏离方向问题转化为广义特征值求解问题,并建立基于协方差矩阵最大奇异值的比率的评价指标,最后根据综合评价指标确定故障发生时刻和故障区域。通过改进的IEEE 34母线系统对方法的有效性和准确性进行验证,结果表明所提时间确定和故障区域定位方法具有自适应性且是准确可靠的。
Aiming at fault time determination and fault section identification of active distribution grid, the data statistic theory is applied to extract fault characteristics of synchronization data. In this paper, a method using synchronization data statistic characteristics is proposed to discriminate the fault time and the fault section in active distribution grid. Firstly, data from micro-phase measurement units are employed to construct a historical data set and an online data set. Then, the method is used to evaluates system states by solving the direction of the maximum variance deviation of the online data versus the historical data. The direction problem is thus transformed into a generalized eigenvalue problem, and an evaluation index is proposed based on the ratio of the maximum singular values from two covariance matrices. Finally, the fault time and the fault section are discriminate by the comprehensive evaluation index. Validity and practicability of the proposed method are verified with the simulation results of modified IEEE 34-bus system, indicating that the proposed method is self-adaptive, accurate and reliable.
Aiming at fault time determination and fault section identification of active distribution grid, the data statistic theory is applied to extract fault characteristics of synchronization data. In this paper, a method using synchronization data statistic characteristics is proposed to discriminate the fault time and the fault section in active distribution grid. Firstly, data from micro-phase measurement units are employed to construct a historical data set and an online data set. Then, the method is used to evaluates system states by solving the direction of the maximum variance deviation of the online data versus the historical data. The direction problem is thus transformed into a generalized eigenvalue problem, and an evaluation index is proposed based on the ratio of the maximum singular values from two covariance matrices. Finally, the fault time and the fault section are discriminate by the comprehensive evaluation index. Validity and practicability of the proposed method are verified with the simulation results of modified IEEE 34-bus system, indicating that the proposed method is self-adaptive, accurate and reliable.
引文
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[19]Pignati M,Zanni L,Romano P,et al.Fault detection and faulted line identification in active distribution networks using synchrophasorsbased real-time state estimation[J].IEEE Transactions on Power Delivery,2017,32(1):381-392.
[20]Sarri S,Zanni L,Popovic M,et al.Performance assessment of linear state estimators using synchrophasor measurements[J].IEEETransactions on Instrumentation and Measurement,2016,65(3):535-548.
[21]Zhang J,Welch G,Bishop G,et al.A two-stage Kalman filtering approach for robust and real-time power system state estimation[J].IEEE Transactions on Sustainable Energy,2014,5(2):629-636.
[22]Haughton D,Heydt G.A linear state estimation formulation for smart distribution systems[J].IEEE Transactions on Power Systems,2013,28(2):1187-1195.
[23]Kersting W H.Radial distribution test feeders[J].IEEE Transactions on Power Systems,1991,19(3):1599-1606.
[24]IEEE standard for synchrophasor measurements for power systems,amendment 1:modification of selected performance requirements:IEEE Std.C37.118.1a-2014(Amendment to IEEE Std.C37.118.1-2011)[S].
[2]刘健,张小庆,同向前,等.含分布式电源配电网的故障定位[J].电力系统自动化,2013,37(2):36-42.Liu Jian,Zhang Xiaoqing,Tong Xiangqian,et al.Fault location for distribution systems with distributed generations[J].Automation of Electric Power Systems,2013,37(2):36-42(in Chinese).
[3]吴宁,许扬,陆于平.分布式发电条件下配电网故障区段定位新算法[J].电力系统自动化,2009,33(14):77-82.Wu Ning,Xu Yang,Lu Yuping.New fault section location algorithm for distribution network with DG[J].Automation of Electric Power Systems,2009,33(14):77-82(in Chinese).
[4]张颖,周韧,钟凯.改进蚁群算法在复杂配电网故障区段定位中的应用[J].电网技术,2011,35(1):224-228.Zhang Ying,Zhou Ren,Zhong Kai.Application of improved ant colony algorithm in fault-Section location of complex distribution network[J].Power System Technology,2011,35(1):224-228(in Chinese).
[5]Goudarzi M,Vahidi B,Naghizadeh R A,et al.Improved fault location algorithm for radial distribution systems with discrete and continuous wavelet analysis[J].International Journal of Electrical Power&Energy Systems,2015(7):423-430.
[6]牛耕,周龙,裴玮,等.低压有源配电网在线故障区间定位与识别方法[J].中国电机工程学报,2017,37(9):2525-2538.Niu Geng,Zhou Long,Pei Wei,et al.On-line fault section location and classification technique for low voltage active distribution networkJ].Proceedings of the CSEE,2017,37(9):2525-2538(in Chinese).
[7]何瑞江,胡志坚,李燕,等.含分布式电源配电网故障区段定位的线性整数规划方法[J].电网技术,2018,42(11):3684-3690.He Ruijiang,Hu Zhijian,Li Yan,et al.Fault section location method for DG-DNs based on integer linear programming[J].Power System Technology,2018,42(11):3684-3690(in Chinese).
[8]陈伟彪,陈亦平,姚伟,等.基于随机矩阵理论的故障时刻确定和故障区域定位方法[J].中国电机工程学报,2018,38(6):1655-1664.Chen Weibiao,Chen Yiping,Yao Wei,et al.A random matrix theoryased approach to fault time determination and fault area location J].Proceedings of the CSEE,2018,38(6):1655-1664(in Chinese).
[9]王宾,孙华东,张道农.配电网信息共享与同步相量测量应用技术评述[J].中国电机工程学报,2015,35(S1):1-7.Wang Bin,Sun Huadong,Zhang Daonong.Review on data sharing and synchronized phasor measurement technique with application in distribution systems[J].Proceedings of the CSEE,2015,35(51):1-7(in Chinese).
[10]戴志辉,崇志强,李川,等.基于电压偏差向量2-范数的主动配电网故障定位新方法[J].电力系统自动化,2015,39(15):96-102.Dai Zhihui,Chong Zhiqiang,Li Chuan,et al.Fault location method of active distribution networks based on 2-norm of voltage deviation vectorsJ].Automation of Electric Power System,2015,39(15):96-102(in Chinese).
[11]Von Meier A,Stewart E,McEachern A,et al.Precision microsynchrophasors for distribution systems:a summary of applications[J].IEEE Transactions on Smart Grid,2017,8(6):2926-2936.
[12]施微,黄磊,徐箭,等.基于有限同步量测数据的电力系统故障检测、辨识与定位[J].电网技术,2015,39(4):1067-1074.Shi Wei,Huang Lei,Xu Jian,et al.Fault detection,identification and location based on measurement data from finite synchronized phasor measurement[J].Power System Technology,2015,39(4):1067-1074(in Chinese).
[13]靳夏宁,汪芙平,王赞基.基于PMU动态同步相量测量的故障测距[J].电网技术,2013,37(10):2932-2937.Jin Xianing,Wang Fuping,Wang Zanji.Research on fault location based on dynamic synchronous phasor measurement by PMU[J].Power System Technology,2013,37(10):2932-2937(in Chinese).
[14]Cai L,Thornhill N F,Kuenzel S,et al.Wide-area monitoring of power systems using principal component analysis and k-nearest neighbor analysis[J].IEEE Transactions on Power Systems,2018,33(5):4913-4923.
[15]Al-Mohammed A H,Abido M A.A fully adaptive PMU-based fault location algorithm for series-compensated lines[J].IEEE Transactions on Power Systems,2014,29(5):2129-2137.
[16]罗深增,李银红,游昊,等.基于同步相量测量单元的串联补偿线路自适应故障定位算法[J].电工技术学报,2017,32(5):143-151.Luo Shenzeng,Li Yinhong,You Hao,et al.An adaptive fault location algorithm based on PMU measurement for series compensated transmission lines[J].Transactions of China Electrotechnical Society,2017,32(5):143-151(in Chinese).
[17]Usman M U,Faruque M O.Validation of a PMU-based fault location identification method for smart distribution network with photovoltaics using real-time data[J].IET Generation,Transmission&Distribution,2018,12(21):5824-5833.
[18]Pignati M,Popovic M,Andrade S B,et al.Real-time state estimation of the EPFL-campus medium-voltage grid by using PMUs[C]//Proc.IEEE Power Energy Society Innovative Smart Grid Technology Conference,Washington,DC,USA,IEEE:2015,1-5.
[19]Pignati M,Zanni L,Romano P,et al.Fault detection and faulted line identification in active distribution networks using synchrophasorsbased real-time state estimation[J].IEEE Transactions on Power Delivery,2017,32(1):381-392.
[20]Sarri S,Zanni L,Popovic M,et al.Performance assessment of linear state estimators using synchrophasor measurements[J].IEEETransactions on Instrumentation and Measurement,2016,65(3):535-548.
[21]Zhang J,Welch G,Bishop G,et al.A two-stage Kalman filtering approach for robust and real-time power system state estimation[J].IEEE Transactions on Sustainable Energy,2014,5(2):629-636.
[22]Haughton D,Heydt G.A linear state estimation formulation for smart distribution systems[J].IEEE Transactions on Power Systems,2013,28(2):1187-1195.
[23]Kersting W H.Radial distribution test feeders[J].IEEE Transactions on Power Systems,1991,19(3):1599-1606.
[24]IEEE standard for synchrophasor measurements for power systems,amendment 1:modification of selected performance requirements:IEEE Std.C37.118.1a-2014(Amendment to IEEE Std.C37.118.1-2011)[S].