Application of ensemble Kalman filter in power system state tracking and sensitivity analysis

An ensemble Kalman filter (EnKF) method is proposed to track dynamic states of generators. The algorithm of the EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies that even with some errors in the parameters, the developed EnKF method can still effectively track generator dynamic states.

[1]  G. Evensen.  Using the Extended Kalman Filter with a Multilayer Quasi-Geostrophic Ocean Model , 1992 .

[2]  J. Mandel.  A Brief Tutorial on the Ensemble Kalman Filter , 2009, 0901.3725.

[3]  D. Novosel,et al.  Dawn of the grid synchronization , 2008, IEEE Power and Energy Magazine.

[4]  Jan Mandel,et al.  Efficient Implementation of the Ensemble Kalman Filter Efficient Implementation of the Ensemble Kalman Filter , 2022 .

[5]  I. M. Moroz,et al.  Ensemble Kalman filter with the unscented transform , 2009, 0901.0461.

[6]  M. Glavic,et al.  Wide-Area Detection of Voltage Instability From Synchronized Phasor Measurements. Part II: Simulation Results , 2009, IEEE Transactions on Power Systems.

[7]  S. Corsi,et al.  A Real-Time Voltage Instability Identification Algorithm Based on Local Phasor Measurements , 2008, IEEE Transactions on Power Systems.

[8]  C. W. Taylor,et al.  Model validation for the August 10, 1996 WSCC system outage , 1999 .

[9]  K. Schneider,et al.  Feasibility studies of applying Kalman Filter techniques to power system dynamic state estimation , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[10]  M. H. Haque.  Determination of Steady-State Voltage Stability Limit Using P-Q Curve , 2002 .

[11]  Christian Rehtanz,et al.  Real-Time Voltage Stability Assessment of Transmission Corridors , 2003 .

[12]  K. Vu,et al.  Use of local measurements to estimate voltage-stability margin , 1997 .

[13]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[14]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[15]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[16]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[17]  A.G. Phadke,et al.  Exploring the IEEE Standard C37.118–2005 Synchrophasors for Power Systems , 2008, IEEE Transactions on Power Delivery.

[18]  P. Kundur,et al.  Power system stability and control , 1994 .

[19]  Dmitry Kosterev,et al.  New thermal governor model selection and validation in the WECC , 2004 .

[20]  Thierry Van Cutsem,et al.  A short survey of methods for voltage instability detection , 2011, 2011 IEEE Power and Energy Society General Meeting.