Calibration of reduced dynamic models of power systems using phasor measurement unit (PMU) Data

Accuracy of a power system dynamic model is essential to the secure and efficient operation of the system. Lower confidence on model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, identification algorithms have been developed to calibrate parameters of individual components using measurement data from staged tests. To facilitate online dynamic studies for large power system interconnections, this paper proposes a model reduction and calibration approach using phasor measurement unit (PMU) data. First, a model reduction method is used to reduce the number of dynamic components. Then, a calibration algorithm is developed to estimate parameters of the reduced model. This approach will help to maintain an accurate dynamic model suitable for online dynamic studies. The performance of the proposed method is verified through simulation studies.

[1]  Joe H. Chow,et al.  A toolbox for power system dynamics and control engineering education and research , 1992 .

[2]  L. N. Hannett,et al.  Testing and model validation for combined-cycle power plants , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[3]  Joe H. Chow,et al.  A measurement-based framework for dynamic equivalencing of large power systems using WAMS , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[4]  T. W. Cease,et al.  Generator and exciter parameter estimation of Fort Patrick Henry Hydro Unit 1 , 1999 .

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

[6]  Pouyan Pourbeik.  Automated parameter derivation for power plant models based on staged tests , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[7]  O. Ruhle,et al.  Industrial approaches for dynamic equivalents of large power systems , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[8]  P. Pourbeik,et al.  Model Development and Field Testing of a Heavy-Duty Gas-Turbine Generator , 2008, IEEE Transactions on Power Systems.

[9]  K. R. Padiyar,et al.  ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY , 1990 .

[10]  Siddhartha Kumar Khaitan,et al.  Fast parallelized algorithms for on-line extended-term dynamic cascading analysis , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[11]  Zhenyu Huang,et al.  Application of extended Kalman filter techniques for dynamic model parameter calibration , 2009, 2009 IEEE Power & Energy Society General Meeting.

[12]  J. Chow,et al.  A trajectory sensitivity method for the identification of nonlinear excitation system models , 1993 .

[13]  Joe H. Chow,et al.  Inertial and slow coherency aggregation algorithms for power system dynamic model reduction , 1995 .

[14]  C.C. Lee,et al.  A weighted-least-squares parameter estimator for synchronous machines , 1977, IEEE Transactions on Power Apparatus and Systems.

[15]  Shuai Lu,et al.  A review of dynamic generator reduction methods for transient stability studies , 2011, 2011 IEEE Power and Energy Society General Meeting.

[16]  Petre Stoica,et al.  Introduction to spectral analysis , 1997 .

[17]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .