Studies of real-time adaptive optimal excitation controller and adaptive optimal power system stabilizer

The use of an adaptive optimal control algorithm for two real-time control applications, an optimal excitation control of a synchronous generator (OEC) and a power system stabilizer (PSS), is described. Experimental studies on a physical model of a power system showed that the proposed OEC and PSS can track the controlled system by parameter identification at different operating conditions. The proposed algorithm is based on linear optimal control theory. A special fifth-order discrete Riccati equation is solved in each sampling period. The proposed OEC and PSS can always guarantee that in a closed loop the controlled system will be stable based on the identified parameters. As the actual output of the controlled system and control are directly used in the controller, no observer is required. The proposed OEC and PSS can track the controlled system very rapidly. A number of tests have been performed, and all show satisfactory results