DECENTRALIZED ROBUST AGC BASED ON STRUCTURED SINGULAR VALUES

In this paper, a new approach based on structured singular value (µ-synthesis) is presented for the robust decentralized automatic generation controller design of a deregulated multi area power systems under the possible contracts. In each control area, the connections between this area and the rest of the system and the effects of possible contracts are treated as a set of new disturbance signals to achieve decentralization. It is shown that, subject to a condition based on the structured singular values and H1 norm, each local area automatic generation controller can be designed independently. The stability condition for the overall system can be stated as to achieve a sufficient i nteraction margin and a sufficient gain and phase margin defined in classical feedback theory during each independent design. The proposed method is tested on a four-area power system with the possible contracts and compared with the PI controller for a wide range of operating conditions and load changes. The resulting controllers are shown to minimize the effects of load disturbances and maintain robust performance in the presence of specified uncertainties and system nonlinearities. K e y w o r d s: AGC, decentralized control, multi machine deregulated power system, µ-synthesis, robust control

[1]  H. H. Rosenbrock,et al.  Computer Aided Control System Design , 1974, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  A. Mees.  Achieving diagonal dominance , 1981 .

[3]  N. Munro.  Recent extensions to the inverse Nyquist array design method , 1985, 1985 24th IEEE Conference on Decision and Control.

[4]  Manfred Morari,et al.  Interaction measures for systems under decentralized control , 1986, Autom..

[5]  L. H. Fink,et al.  Understanding automatic generation control , 1992 .

[6]  Gary J. Balas,et al.  μ-analysis and synthesis toolbox: for use with Matlab , 1994 .

[7]  R. D. Christie,et al.  Load frequency control issues in power system operations after deregulation , 1995 .

[8]  Ali Feliachi.  On load frequency control in a deregulated environment , 1996, Proceeding of the 1996 IEEE International Conference on Control Applications IEEE International Conference on Control Applications held together with IEEE International Symposium on Intelligent Contro.

[9]  J. Doyle,et al.  Robust and optimal control , 1995, Proceedings of 35th IEEE Conference on Decision and Control.

[10]  Gerald B. Sheblé,et al.  AGC simulator for price-based operation. I. A model , 1997 .

[11]  B. H. Bakken,et al.  Automatic generation control in a deregulated power system , 1998 .

[12]  A. P. Sakis Meliopoulos,et al.  Load-frequency control service in a deregulated environment , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[13]  M. Khammash,et al.  Sensitivity based structured singular value approach to stability robustness of power systems , 2000 .

[14]  M. A. Pai,et al.  Simulation and Optimization in an AGC System after Deregulation , 2001, IEEE Power Engineering Review.

[15]  Boris Lohmann,et al.  Output feedback decentralized control of large-scale systems using weighted sensitivity functions minimization , 2002, Syst. Control. Lett..

[16]  A. Conejo,et al.  Optimal Response of a Power Generator to Energy, AGC, and Reserve Pool-Based Markets , 2002, IEEE Power Engineering Review.

[17]  Ali Feliachi,et al.  Robust load frequency control using genetic algorithms and linear matrix inequalities , 2003 .

[18]  Shengwei Mei,et al.  Optimal load-frequency control in restructured power systems , 2003 .

[19]  Yasunori Mitani,et al.  Robust decentralized AGC in a restructured power system , 2004 .

[20]  R. Raineri,et al.  Technical and economic aspects of ancillary services markets in the electric power industry: an international comparison , 2006 .