Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared with the CPSS and the indirect-adaptive-neurocontrol-based PSS under small and large disturbances. In addition, the impact of different discount factors in the HDP PSS's performance is presented.


Electrical and Computer Engineering

Keywords and Phrases

Adaptive Critic Design (ACD); Discount Factors; Heuristic Dynamic Programming (HDP); Indirect Adaptive Control; Neural Networks; Neuro-Control; Neuro-Identifier; Online Training; Power System Stabilizer (PSS)

Document Type

Article - Journal

Document Version

Final Version

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© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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