PI-RLNN Controller for LFC of hybrid deregulated power system based on SPOA /

Abstract

This paper presents the student psychology optimization algorithm (SPOA) based Proportional Integral structure incorporated reinforced learning neural network (PI-RLNN) controllers in the load frequency control (LFC) issues for three area hybrid deregulated power system (HDPS) with different generation units like hydro, thermal, diesel engine generation (DEG) and wind turbine generation (WTG). The controller parameters and gains are optimized by SPOA and its performance is compared with both RLNN and PID controllers. Sensitivity analyses are performed to investigate the robustness of the considered SPOA based PI-RLNN controllers representation to different of inertia constant and various loading situations. In addition, the time domain analysis represents that the SPOA based PI-RLNN controllers show superior results than other controllers.