Automatic generation control of a two area reheat interconnected power system based on CPS using fuzzy neural network

The AGC of reheat interconnected two area power systems are characterized by non-linearity and uncertainty. A hybrid neural network and fuzzy control is proposed for automatic generation control in power systems. Recurrent neural network is employed to forecast controller and system's future output, based on the current Area Control Error (ACE) and the predicted change-of-ACE. The Control Performance Standard (CPS) criterion is adapted to the fuzzy controller design, thus improves the dynamic quality of system. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 1% step-load disturbance in area 1 were obtained. The comparison of frequency deviations and tie-line power deviations for the two area interconnected thermal power system integral controller with Redox Flow Batteries (RFB) reveals that the system with hybrid fuzzy neural controller enhances a better stability than that of system without hybrid fuzzy neural controller.