C. Tavares-Da-Costa Jr., J.A.L. Barreiros, A.M.D. Ferreira, and W. Barra Jr. (Brazil)
Power System Control, Power System Stabilizers, Neural Networks, Local Model Networks, Excitation Control.
This work proposes the design of a Power System Stabilizer using a Neural Supervisor trained from a Local Model Network. For any given system operation point, represented by the active and reactive power in the synchronous generator output, the neural supervisor can supply a set of parameters that completely define a linear model of the power system. Then, a pole-placement method is used to obtain a discrete-time controller to be used in the actual system operating condition, improving the power system dynamic stability. Simulation results confirm the good performance of this approach when compared with a fixed-parameter PSS and a self-tuning stabilizer.
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