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.
S. Ray et al., "Optimal Dynamic Neurocontrol of a Gate-Controlled Series Capacitor in a Multi-Machine Power System," Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at http://dx.doi.org/10.1109/ISAP.2005.1599267
13th International Conference on, Intelligent Systems Application to Power Systems, 2005
Electrical and Computer Engineering
Keywords and Phrases
FACTS Device; Backpropagation; Backpropagation Through Time; Dynamic Neurocontroller; Flexible AC Transmission Systems; Gate Controlled Series Capacitor; Heuristic Dynamic Programming; Multimachine Power System; Neurocontrollers; Optimal Control; Optimal Dynamic Neurocontrol; Power Capacitors; Power System Control; Recurrent Neural Nets; Recurrent Neural Network
Article - Conference proceedings
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