State estimation with consideration of PMU phase mismatch for smart grids

Phasor measurement units (PMUs) are time synchronized sensors for power system state estimation. Despite their promising advantages, large scale deployment of PMUs is still limited. One of the reasons is the high cost of their exact synchronization. In this paper, we consider the use of cheaper and less accurately synchronized PMUs, but compensate for these imperfections via signal processing methods. We introduce a statistical model for power state estimation using these impaired units. We then derive an alternating minimization technique and a parallel Kalman filter for static and dynamic estimation, respectively. Numerical examples demonstrate the improvement in estimation accuracy of our algorithm compared with traditional algorithms when phase mismatch is present. Our results suggest that the phase mismatches can be largely compensated as long as there is a sufficient number of PMUs and the delays are small.

[1]  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.

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

[3]  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.

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

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

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

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

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

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

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

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

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

[13]  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).

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

[15]  Peng Yang,et al.  Power System State Estimation Using PMUs With Imperfect Synchronization , 2013, IEEE Transactions on Power Systems.

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

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

[18]  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.

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