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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
Published in:
Industrial Electronics, IEEE Transactions on
(Volume:54
,
Issue:
1
)
Date of Publication: Feb. 2007
- Page(s):
- 86 - 96
- ISSN :
- 0278-0046
- INSPEC Accession Number:
- 9299034
- Digital Object Identifier :
- 10.1109/TIE.2006.888760
- Date of Current Version :
- 05 February 2007
- Issue Date :
- Feb. 2007
- Sponsored by :
- IEEE Industrial Electronics Society
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