Performance Improvement of AGC Using Novel Controllers in a Multi-Area Solar Thermal System under Deregulated Environment

Abstract

The performance improvement of Automatic Generation Control (AGC) in a Multi Area Solar Thermal Power System (MASTPS) under a deregulated environment is considered as MIPS and their performance is compared using various controllers to improve the performance of AGC in the proposed MIPS. This study takes into account a three-area conventional thermal power system in conjunction with a solar thermal power plant in each area, along with the proper generation rate constraints (GRC). The performance of the different controllers, including the Adaptive Neuro Fuzzy Inference System (ANFIS) controller and the Droop Controller (DC), Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Fractional Order PID (FOPID) controllers, is compared in the proposed AGC deregulated power system. The proposed MIPS is designed and implemented to make the system as realistic as possible with real world scenarios. The recommended MIPS is being simulated in MALTAB Simulink environment and simulated for multiple times to investigate the performance improvement in AGC using the adopted controllers with 0.1 per unit of step load perturbation to verify its dynamic characteristics. According to the investigation, the suggested ANFIS controller improves the AGC performance in proposed MIPS and offers better dynamic performance than other controllers.