Abstract

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.

Department(s)

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)

0278-0046

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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