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

Meeting Name

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)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link