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, a member of the adaptive Critic designs family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system. Two multimachine systems are considered in this study: a 10-bus system and a 45-bus network (a section of the Brazilian power system). Simulation results are provided to show that the proposed controller outperforms a conventional PI controller in large scale faults as well as small disturbances
S. Mohagheghi et al., "Adaptive Critic Design Based Neuro-Fuzzy Controller for a Static Compensator in a Multimachine Power System," Proceedings of the IEEE Power Engineering Society General Meeting, 2007, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at http://dx.doi.org/10.1109/PES.2007.386029
IEEE Power Engineering Society General Meeting, 2007
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Critic Designs; Brazilian Power System; PI Control; STATCOM; Adaptive Critic Design; Artificial Neural Networks; Control System Analysis; Dynamic Programming; Fuzzy Control; Fuzzy Logic; Heuristic Dynamic Programming; Large Scale Faults; Multimachine Power System; Neuro-Fuzzy Controller; Neuro-Fuzzy Systems; Neurocontrollers; Nonlinear Optimal Controller; Optimal Control; Power System Control; Power System Faults; Proportional-Integrator Approach; Reinforcement Learning; Static VAr Compensators; Static Compensator
International Standard Serial Number (ISSN)
Article - Conference proceedings
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