Generalized predictive power system stabilizer with fuzzy supervision from a local model network

This work presents a digital adaptive power system stabilizer (PSS) which operates in a self-tuning structure. The parametric models are identified off line for several differently selected operating points in a P/spl times/Q plane (active and reactive powers) to form a local model network (LMN), and saved off-line under the form of a look-up table in the microcomputer that implements the control action. During working, a fuzzy logic based supervisor identifies the current operating point and determines the plant model from the LMN. Then, using this plant model, the PSS control law is designed based on the generalized predictive control (GPC) method. The tests presented show good performance for this PSS, when compared to a conventional (nonadaptive) one.