Optimal Dynamic Neurocontrol of a Gate-Controlled Series Capacitor in a Multi-Machine Power System
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This paper presents the design of an optimal dynamic neurocontroller for a new type of FACTS device - the gate controlled series capacitor (GCSC) incorporated in a multi-machine power system. The optimal neurocontroller is developed based on the heuristic dynamic programming (HDP) approach. In addition, a dynamic identifier/model and controller structure using the recurrent neural network trained with backpropagation through time (BPTT) is employed. Simulation results are presented to show the effectiveness of the dynamic neurocontroller and its performance is compared with that of the conventional PI controller under small and large disturbances.