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 dynamic programming. Using a proportional-integrator approach, the proposed neurocontroller is capable of dealing with actual rather than deviation signals. Simulation results are provided to show that the proposed controller outperforms a conventional PI controller for a STATCOM in a small and large multimachine power system during large-scale faults, as well as small disturbances.
S. Mohagheghi et al., "A Proportional-Integrator Type Adaptive Critic Design-Based Neurocontroller for a Static Compensator in a Multimachine Power System," IEEE Transactions on Industrial Electronics, Institute of Electrical and Electronics Engineers (IEEE), Jan 2007.
The definitive version is available at https://doi.org/10.1109/TIE.2006.888760
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Critic Designs (ACDs); Multimachine Power System; Neurocontroller; Optimal Control; Static Compensator (STATCOM)
International Standard Serial Number (ISSN)
Article - Journal
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