Optimization Design of Multivariable Control in Multimachine Power Systems

Abstract

A new algorithm and model for the optimization design of multivariable control in multimachine power systems are presented in this paper. This method exploits the modified random search approach to directly optimize the output feedback gains, without solving Riccati equation. Using this approach, we can design the optimal control with all state feedback of the entire system, or the suboptimal control with partial state feedback of the entire system, or the optimal decentralized control with any state feedback of the local individual machine in the system. It has been employed for the parameters design of an optimal excitation control-ler (OEC) installed at a power station in N.W. power system of China. Re-sults show that this method can effectively search out the optimal para-meters of the controller, hence much improve dynamic performance and highly enhance the steady stability limit of the system. A comparative study of the OEC (from solving Riccati equation) and the decentralized local excitation control (DLEC) has been completed; and it is shown that both dynamic performances are very close. This means that the DLEC is a practical, valuable and perspective way for the power system control.