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.
W. Liu et al., "A Heuristic-Dynamic-programming-Based Power System Stabilizer for a Turbogenerator in a Single-Machine Power System," IEEE Transactions in Industry Applications, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at https://doi.org/10.1109/TIA.2005.853386
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)
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