Das, Milton Kumar2025-02-252025-03-312025-02-252021-09-24https://doi.org/10.1109/gucon50781.2021.9573671https://dspacenew8-imu.refread.com/handle/123456789/2433This paper presents the multi-objective grasshopper optimization algorithm (MOGOA) based reinforced learning neural network controller (RLNN) controllers in the load frequency control (LFC) problems for three area deregulated hybrid distributed generation (HDG) power system. The controller parameters and gains are optimized by MOGOA and its performance is compared with PID controllers. Sensitivity analyses are performed to investigate robustness of the considered MOGOA-RLNN controllers expose to change of inertia constant and loading conditions and also analysis exposes that MOGOA-RLNN controllers is superior performances than PID controllers.enload frequency controlreinforced learning neural networkgrasshopper optimization algorithmrobustnessArticle