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Levenberg-Marquardt (LM) algorithm, a powerful off-line batch training method for neural networks, is adapted here for online estimation of power system dynamic behavior. A special form of neural network compatible with the feedback linearization framework is used to enable non-linear self-tuning control. Use of LM is shown to yield better closed-loop performance compared to conventional recursive...
A novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks, is presented in this paper. The action dependent heuristic dynamic programming, a member of the adaptive critic designs family is used for the design of the STATCOM neurocontroller. This neurocontroller provides optimal control based on reinforcement learning and approximate...
Summary form only given. This paper presents a novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks and fuzzy logic. The action dependent heuristic dynamic programming (ADHDP), a member of the adaptive critic designs (ACD) family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller...
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