Power System State Estimation Using PMUs With Imperfect Synchronization

Phasor measurement units (PMUs) are time synchronized sensors primarily used for power system state estimation. Despite their increasing incorporation and the ongoing research on state estimation using measurements from these sensors, estimation with imperfect phase synchronization has not been sufficiently investigated. Inaccurate synchronization is an inevitable problem that large scale deployment of PMUs has to face. In this paper, we introduce a model for power system state estimation using PMUs with phase mismatch. We propose alternating minimization and parallel Kalman filtering for state estimation using static and dynamic models, respectively, under different assumptions. Numerical examples demonstrate the improved accuracy of our algorithms compared with traditional algorithms when imperfect synchronization is present. We conclude that when a sufficient number of PMUs with small delays are employed, the imperfect synchronization can be largely compensated in the estimation stage.

[1]  Gustavo Valverde,et al.  Unscented kalman filter for power system dynamic state estimation , 2011 .

[2]  P. Marino,et al.  Parallel Kalman filter algorithm for state estimation in bilinear systems , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[3]  A. Gomez-Exposito,et al.  Bilinear Power System State Estimation , 2012, IEEE Transactions on Power Systems.

[4]  B. Moor.  Structured total least squares and L2 approximation problems , 1993 .

[5]  Branislav Djokic,et al.  Issues Related to the Implementation of Synchrophasor Measurements , 2008, Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008).

[6]  K. Shih,et al.  Application of a Robust Algorithm for Dynamic State Estimation of a Power System , 2002, IEEE Power Engineering Review.

[7]  B. Gou,et al.  Generalized Integer Linear Programming Formulation for Optimal PMU Placement , 2008, IEEE Transactions on Power Systems.

[8]  Dragan Obradovic,et al.  A probabilistic approach to clock synchronization of cascaded network elements , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Edward J. Coyle,et al.  Observing the Power Grid: Working Toward a More Intelligent, Efficient, and Reliable Smart Grid with Increasing User Visibility , 2012, IEEE Signal Processing Magazine.

[10]  Burkhard Schaffrin,et al.  TOWARDS TOTAL KALMAN FILTERING FOR MOBILE MAPPING , 2008 .

[11]  A. Monticelli.  State estimation in electric power systems : a generalized approach , 1999 .

[12]  E. Handschin,et al.  Static state estimation in electric power systems , 1974 .

[13]  A. Jain,et al.  A Review of Power System Dynamic State Estimation Techniques , 2008, 2008 Joint International Conference on Power System Technology and IEEE Power India Conference.

[14]  L Vanfretti,et al.  A Phasor-Data-Based State Estimator Incorporating Phase Bias Correction , 2011, IEEE Transactions on Power Systems.

[15]  Vassilis Kekatos,et al.  Optimal Placement of Phasor Measurement Units via Convex Relaxation , 2012, IEEE Transactions on Power Systems.

[16]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .

[17]  Mladen Kezunovic,et al.  Signal processing, communication, and networking requirements for synchrophasor systems , 2012, 2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[18]  Greg Welch,et al.  Observability and estimation uncertainty analysis for PMU placement alternatives , 2010, North American Power Symposium 2010.

[19]  Atif S. Debs,et al.  A Dynamic Estimator for Tracking the State of a Power System , 1970 .

[20]  James S. Thorp,et al.  Synchronized Phasor Measurement Applications in Power Systems , 2010, IEEE Transactions on Smart Grid.

[21]  Georgios B. Giannakis,et al.  Distributed Robust Power System State Estimation , 2012, IEEE Transactions on Power Systems.

[22]  Gene H. Golub,et al.  An analysis of the total least squares problem , 1980, Milestones in Matrix Computation.

[23]  David G. Hart,et al.  PMUs - A new approach to power network monitoring , 2001 .

[24]  A.G. Phadke,et al.  An Alternative for Including Phasor Measurements in State Estimators , 2006, IEEE Transactions on Power Systems.

[25]  Catalina Gomez-Quiles,et al.  Equality-constrained bilinear state estimation , 2013, IEEE Transactions on Power Systems.

[26]  E. Kyriakides,et al.  PMU Measurement Uncertainty Considerations in WLS State Estimation , 2009, IEEE Transactions on Power Systems.

[27]  Ami Wiesel,et al.  Linear Regression With Gaussian Model Uncertainty: Algorithms and Bounds , 2008, IEEE Transactions on Signal Processing.

[28]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[29]  Pablo A. Parrilo,et al.  Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..

[30]  C. E. Davila,et al.  Recursive total least squares algorithms for adaptive filtering , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[31]  Shubha Pandit,et al.  Power System State Estimation , 2002 .

[32]  Jing Huang,et al.  State Estimation in Electric Power Grids: Meeting New Challenges Presented by the Requirements of the Future Grid , 2012, IEEE Signal Processing Magazine.

[33]  Evangelos Farantatos,et al.  PMU-based dynamic state estimation for electric power systems , 2009, 2009 IEEE Power & Energy Society General Meeting.

[34]  A. Jain,et al.  Impact of PMU in dynamic state estimation of power systems , 2008, 2008 40th North American Power Symposium.

[35]  G. T. Heydt,et al.  Dynamic state estimation of power system harmonics using Kalman filter methodology , 1991 .