A NEURO-FUZZY HYBRID POWER-SYSTEM STABILIZER

The paper presents a novel neuro-fuzzy hybrid power system stabilizer (PSS) design for damping electromechanical modes of oscillation and enhancing power system synchronous stability. The hydrid PSS comprises a front-end conventional analog PSS design, an artificial neural network (ANN) based stabilizer, and a fuzzy logic post-processor gain scheduler. The stabilizing action is controlled by the post-processor gain scheduler based on an optimized fuzzy logic excursion based criterion J(0). The two PSS stabilizers, conventional and neural network, have their damping action scaled online by the magnitude of J(0) and its rate of change dJ(0). The ANN feedforward two-layer based PSS design is the curve fitted nonlinear mapping between the damping vector signals and the desired optimized PSS output and is trained using the benchmark analog PSS conventional design. The fuzzy logic se excursions, fault conditions, and load rejections. The parallel operation of a conventional PSS and a neural network PSS provides optimal sharing of the damping action under small as well as large-scale generation-load mismatch or variations in external network topology due to fault or switching conditions.