Power System Stabilizer Based on Model Reference Adaptive Fuzzy Control

A model reference adaptive fuzzy controller (MRAFC) consisting of a reference model and self-learning fuzzy logic controller and its application as a power system stabilizer (PSS) is described in this article. Off-line model identification is used to obtain a dynamic equivalent model for the synchronous machine with respect to the rest of the system. This model is used as the basis for defining a reference model for the generating unit. A fuzzy controller with self-learning capability is then used to adapt the system performance to track the reference model. The self-learning ability of the fuzzy controller is based on the steepest descent algorithm. The effectiveness of the proposed adaptive PSS based on this technique is demonstrated. Results obtained show improvement in the overall system damping characteristics using the proposed adaptive PSS.

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