基于PMU的配电网潮流雅可比矩阵鲁棒估计与拓扑辨识
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摘要
通过量测数据实现潮流雅可比矩阵的估计计算,进而辨识配电网的运行拓扑,能够有效避免由于线路参数不准确等问题造成的误差。提出了基于同步相量量测单元PMU的配电网潮流雅可比矩阵鲁棒估计与拓扑辨识方法。首先,在估计雅可比矩阵时考虑了矩阵的稀疏性,并利用估计问题中传感矩阵各列的相关性,对现有的稀疏恢复算法进行改进;进一步引入了更具鲁棒性的最大化相关熵方法;最后,在估计时利用了雅可比矩阵的特殊性,有效提高了算法的计算效率和鲁棒性,提升了雅可比矩阵估计和拓扑辨识的成功率。通过在IEEE 33节点配电系统上的算例分析证明了所提出算法的正确性和有效性。
The estimation of power flow Jacobian matrix is realized using measurement data,thus the operation topology of distribution network can be further identified. In this way,the errors caused by the inaccuracy of line parameters canbe effectively avoided. In this paper,a robust method for power flow Jacobian matrix estimation and topology identification of distribution network is proposed based on phasor measurement unit(PMU). First,the sparsity of matrix is considered when estimating the Jacobian matrix,and the coherence of the sensing matrix'columns in the estimation prob-lem is used to improve the existing sparse recovery algorithm. Then,a maximum correntropy based method,which ismuch more robust,is introduced. Finally,the characteristics of Jacobian matrix are utilized to improve the calculationefficiency and robustness of the algorithm,thus enhancing the success rates of Jacobian matrix estimation and topologyidentification. A case study on an IEEE 33-node distribution system verifies the correctness and effectiveness of the proposed method.
The estimation of power flow Jacobian matrix is realized using measurement data,thus the operation topology of distribution network can be further identified. In this way,the errors caused by the inaccuracy of line parameters canbe effectively avoided. In this paper,a robust method for power flow Jacobian matrix estimation and topology identification of distribution network is proposed based on phasor measurement unit(PMU). First,the sparsity of matrix is considered when estimating the Jacobian matrix,and the coherence of the sensing matrix'columns in the estimation prob-lem is used to improve the existing sparse recovery algorithm. Then,a maximum correntropy based method,which ismuch more robust,is introduced. Finally,the characteristics of Jacobian matrix are utilized to improve the calculationefficiency and robustness of the algorithm,thus enhancing the success rates of Jacobian matrix estimation and topologyidentification. A case study on an IEEE 33-node distribution system verifies the correctness and effectiveness of the proposed method.
引文
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[17] Li P,Su H,Wang C,et al. PMU-based estimation of voltage-to-power sensitivity for distribution networks considering the sparsity of Jacobian matrix[J]. IEEE Access,2018,6(1):31307-31316.
[18] Li W T,Wen C K,Chen J C,et al. Location identification of power line outages using PMU measurements with bad data[J]. IEEE Transactions on Power Systems,2016,31(5):3624-3635.
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[25] Liu T,Qiu T,Dai R,et al. Nonlinear regression A*OMP for compressive sensing signal reconstruction[J]. Digital Signal Processing,2017,69:11-21.
[26] Tropp J A. Greed is good:Algorithmic results for sparse approximation[J]. IEEE Transactions on Information Theory,2004,50(10):2231-2242.
[27] Needell D,Vershynin R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Signal Processing,2007,4(2):310-316.
[28] Needell D,Tropp J A. CoSaMP:Iterative signal recovery from incomplete and inaccurate samples[J]. Applied&Computational Harmonic Analysis,2009,26(3):301-321.
[29] Candes E,Romberg J. I1-magic:Recovery of sparse signals via convex programming[EB/OL]. https://download.csdn.net/download.skcsdn28/8751521,2005,1-19.
[30] G?l M,Abur A. A robust PMU based three-phase state estimator using modal decoupling[J]. IEEE Transactions on Power Systems,2014,29(5):2292-2299.
[31] Wang Y,Tang Y Y,Li L. Correntropy matching pursuit with application to robust digit and face recognition[J].IEEE Transactions on Cybernetics,2016,47(6):1354-1366.
[32] Baran M E,Wu F F. Optimal capacitor placement on radial distribution systems[J]. IEEE Transactions on Power Delivery,1989,4(1):725-734.
[33] Arghandeh R,Gahr M,Von M A,et al. Topology detection in microgrids with micro-synchrophasors[C]//Power&Energy Society General Meeting. Denver,USA,2015.
[2]李从善,刘天琪,李兴源,等(Li Congshan,Liu Tianqi,Li Xingyuan,et al).用于电力系统状态估计的WAMS/SCADA混合量测数据融合方法(Data fusion method of WAMS/SCADA hybrid measurements in power system state estimation)[J].高电压技术(High Voltage Engineering),2013,39(11):2686-2691.
[3] Nielsen J J,Ganem H,Jorguseski L,et al. Secure realtime monitoring and management of smart distribution grid using shared cellular networks[J]. IEEE Wireless Communications,2017,24(2):10-17.
[4] Pourramezan R,Seyedi Y,Karimi H,et al. Design of an advanced phasor data concentrator for monitoring of distributed energy resources in smart microgrids[J]. IEEE Transactions on Industrial Informatics,2017,6(13):3027-3036.
[5] Asprou M,Kyriakides E. Identification and estimation of erroneous transmission line parameters using PMU measurements[J]. IEEE Transactions on Power Delivery,2017,6(32):2510-2519.
[6] Dragosavac J,Janda Z,Milanovic J. Coordinated reactive power-voltage controller for multimachine power plant[J].IEEE Transactions on Power Systems,2012,27(3):1540-1549.
[7] Paramasivam M,Salloum A,Ajjarapu V,et al. Dynamic optimization based reactive power planning to mitigate slow voltage recovery and short term voltage instability[J].IEEE Transactions on Power Systems,2013,28(4):3865-3873.
[8] Kai E V H,Domínguez-García A D,Sauer P W. Measurement based real-time security-constrained economic dispatch[J]. IEEE Transactions on Power Systems,2016,31(5):3548-3560.
[9] Zhang J,Zheng X,Wang Z,et al. Power system sensitivity identification-inherent system properties and data quality[J]. IEEE Transactions on Power Systems,2017,32(4):2756-2766.
[10] Wang X,Bialek J,Turitsyn K. PMU-based estimation of dynamic state jacobian matrix and dynamic system state matrix in ambient conditions[J]. IEEE Transactions on Power Systems,2018,33(1):681-690.
[11] Phadke A G,Thorp J S. Synchronized Phasor Measurements and Their Applications[M]. New York:Springer,2008.
[12] Chen Y C,Dominguez-Garcia A D,Sauer P W. Measurement-based estimation of linear sensitivity distribution factors and applications[J]. IEEE Transactions on Power Systems,2014,29(3):1372-1382.
[13] Zhao X,Zhou H,Shi D,et al. On-line PMU-based transmission line parameter identification[J]. CSEE Journal of Power and Energy Systems,2015,1(2):68-74.
[14] Saadeh M,Mccann R,Alsarray M,et al. A new approach for evaluation of the bus admittance matrix from synchrophasors:A statistical Ybus estimation approach[J]. International Journal of Electrical Power&Energy Systems,2017,93(12):395-405.
[15] Babakmehr M,Sim?es M G,Wakin M B,et al. Smart-grid topology identification using sparse recovery[J]. IEEE Transactions on Industry Applications,2016,52(5):4375-4384.
[16] Chen Y C,Wang J,Domínguez-García A D,et al. Measurement-based estimation of the power flow Jacobian matrix[J]. IEEE Transactions on Smart Grid,2016,7(5):2507-2515.
[17] Li P,Su H,Wang C,et al. PMU-based estimation of voltage-to-power sensitivity for distribution networks considering the sparsity of Jacobian matrix[J]. IEEE Access,2018,6(1):31307-31316.
[18] Li W T,Wen C K,Chen J C,et al. Location identification of power line outages using PMU measurements with bad data[J]. IEEE Transactions on Power Systems,2016,31(5):3624-3635.
[19] Costa I S,Leao J A. Identification of topology errors in power system state estimation[J]. IEEE Transactions onPower Systems,1993,8(4):1531-1538.
[20] Mili L,Steeno G,Dobraca F,et al. A robust estimation method for topology error identification-reply[J]. IEEE Transactions on Power Systems,1999,14(4):1469-1476.
[21] Tian Z,Wu W,Zhang B. A mixed integer quadratic programming model for topology identification in distribution network[J]. IEEE Transactions on Power Systems,2015,31(1):823-824.
[22] Babakmehr M,Sim?es M G,Wakin M B,et al. Compressive sensing-based topology identification for smart grids[J]. IEEE Transactions on Industrial Informatics,2016,12(2):532-543.
[23] Ekanadham C,Tranchina D,Simoncelli E. Recovery of sparse translation-invariant signals with continuous basis pursuit[J]. IEEE Transactions on Signal Processing A:Publication of the IEEE Signal Processing Society,2011,59(10):4735-4744.
[24] Tropp J A,Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[25] Liu T,Qiu T,Dai R,et al. Nonlinear regression A*OMP for compressive sensing signal reconstruction[J]. Digital Signal Processing,2017,69:11-21.
[26] Tropp J A. Greed is good:Algorithmic results for sparse approximation[J]. IEEE Transactions on Information Theory,2004,50(10):2231-2242.
[27] Needell D,Vershynin R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Signal Processing,2007,4(2):310-316.
[28] Needell D,Tropp J A. CoSaMP:Iterative signal recovery from incomplete and inaccurate samples[J]. Applied&Computational Harmonic Analysis,2009,26(3):301-321.
[29] Candes E,Romberg J. I1-magic:Recovery of sparse signals via convex programming[EB/OL]. https://download.csdn.net/download.skcsdn28/8751521,2005,1-19.
[30] G?l M,Abur A. A robust PMU based three-phase state estimator using modal decoupling[J]. IEEE Transactions on Power Systems,2014,29(5):2292-2299.
[31] Wang Y,Tang Y Y,Li L. Correntropy matching pursuit with application to robust digit and face recognition[J].IEEE Transactions on Cybernetics,2016,47(6):1354-1366.
[32] Baran M E,Wu F F. Optimal capacitor placement on radial distribution systems[J]. IEEE Transactions on Power Delivery,1989,4(1):725-734.
[33] Arghandeh R,Gahr M,Von M A,et al. Topology detection in microgrids with micro-synchrophasors[C]//Power&Energy Society General Meeting. Denver,USA,2015.