Power system stabilizers (PSS) 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 conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed 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. The proposed HDP based PSS is evaluated against the conventional power system stabilizer and indirect adaptive neurocontrol based PSS under small and large disturbances in a single machine infinite bus power system setup. Results are presented to show the effectiveness of this new technique.

Meeting Name

38th IAS Annual Meeting of the Industry Applications Conference, 2003


Electrical and Computer Engineering

Keywords and Phrases

Adaptive Control; Damping; Dynamic Programming; Excitation System; Heuristic Dynamic Programming; Indirect Adaptive Neurocontrol; Infinite Bus Power System; Learning (Artificial Intelligence); Low Frequency Power System Oscillations Damping; Neurocontrollers; Nonlinear Control Systems; Nonlinear Optimal Power System Stabilizer; Optimal Control; Power System Control; Power System Stability; Power System Stabilizer; Reinforcement Learning; Single Machine Power System; Supplementary Control Signals; Turbogenerators

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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