Abstract
In this paper, a novel Combinatorial Discrete and Continuous Action Reinforcement Learning Automata (CDCARLA) based approach for optimal design of multimachine power system stabilizers (PSSs) is presented. The proposed CDCARLA based design approach is a combined procedure of two optimization stages in discrete and continuous spaces for fast convergence and high optimization efficiency. The potential of the proposed approach in seeking the optimal settings of the widely used conventional lead-lag PSSs’ parameters is investigated and assessed in multimachine power systems. The performance and robustness of the proposed CDCARLA based PSS is evaluated under different power system disturbances. The performance of the proposed stabilizer is also compared with other stabilizers reported in the literature including the multi-band PSSs for a two-area four-machine power system. Simulation results show the effectiveness and robustness of the proposed CDCARLA PSS in damping local and inter area oscillation modes under various disturbances, and confirm its superiority in comparison with other types of PSSs.
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