Optimal Neuro-Fuzzy External Controller for a STATCOM in the 12-Bus Benchmark Power System
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Abstract
An optimal neuro-fuzzy external controller is designed in this paper for a static compensator (STATCOM) in the 12-bus benchmark power system. The controller provides an auxiliary reference signal for the STATCOM in such a way that it improves the damping of the rotor speed deviations of its neighboring generators. A Mamdani fuzzy rule base constitutes the core of the controller. A heuristic dynamic programming-based approach is used to further train the controller and enable it to provide nonlinear optimal control at different operating conditions of the power system. Simulation results are provided that indicate the proposed neuro-fuzzy external controller is more effective than a linear external controller for damping out the speed deviations of the generators. In addition, the two controllers are compared in terms of the control effort generated by each one during various disturbances and the proposed neuro-fuzzy controller proves to be more effective with smaller control effort.
