Optimal Coordinate Design of Multiple HVDC Modulation Controllers based on MIMO System Identification

Low frequency oscillation is becoming a serious bottleneck limiting power system stability enhancement, especially in the conditions of interconnections of regional networks. Because of the global properties, most of the units are involved in this problem. The controllers of these devices must be designed coordinately. In this paper, an optimal controller design approach is proposed for the applications in large scale power system. A special injection signal is used to excite the dynamics in certain frequency range, and then prediction error method (PEM) is employed to identify the reduced order system based on multiple inputs and outputs. Traditional optimal control techniques are improved to include the lead-lag blocks, so the popular supplementary damping controllers are compatible. Finally, this approach is applied in the design of two HVDC modulation controllers in China Southern Grid, and the results demonstrate the advantages.

[1]  M. Szechtman,et al.  Synchronizing and damping torque modulation controllers for multi-infeed HVDC systems , 1995 .

[2]  Yong Min,et al.  On-line dynamic security monitoring of power systems based on SMES , 2002, Proceedings. International Conference on Power System Technology.

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

[4]  E. Lerch,et al.  Optimization and Coordination of Damping Controls for Improving System Dynamic Performance , 2001, IEEE Power Engineering Review.

[5]  Glauco N. Taranto,et al.  Simultaneous tuning of power system damping controllers using genetic algorithms , 2000 .

[6]  I. Erlich,et al.  Simultaneous coordinated tuning of PSS and FACTS damping controllers in large power systems , 2005, IEEE Transactions on Power Systems.

[7]  Yao-nan Yu,et al.  Application of an Optimal Control Theory to a Power System , 1970 .

[8]  Ning Zhou,et al.  Initial results in power system identification from injected probing signals using a subspace method , 2006, IEEE Transactions on Power Systems.

[9]  Chao Lu,et al.  Direct Neural Dynamic Programming Method for Power System Stability Enhancement , 2005, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[10]  Ying-Yi Hong,et al.  A new approach using optimization for tuning parameters of power system stabilizers , 1999 .

[11]  J. W. Pierre,et al.  Use of ARMA Block Processing for Estimating Stationary Low-Frequency Electromechanical Modes of Power Systems , 2002, IEEE Power Engineering Review.

[12]  Daniel J. Trudnowski,et al.  Initial results in electromechanical mode identification from ambient data , 1997 .

[13]  D.J. Trudnowski,et al.  Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[14]  J. F. Hauer,et al.  Dynamic Performance Validation in the Western Power System , 2000 .

[15]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.