Firefly algorithm optimized fuzzy PID controller for AGC of multi-area multi-source power systems with UPFC and SMES

In this paper, a Firefly Algorithm (FA) optimized fuzzy PID controller is proposed for Automatic Generation Control (AGC) of multi-area multi-source power system. Initially, a two area six units power system is used and the gains of the fuzzy PID controller are optimized employing FA optimization technique using an ITAE criterion. The superiority of the proposed FA optimized fuzzy PID controller has been demonstrated by comparing the results with some recently published approaches such as optimal control and Differential Evolution (DE) optimized PID controller for the identical interconnected power system. Then, physical constraints such as Time Delay (TD), reheat turbine and Generation Rate Constraint (GRC) are included in the system model and the superiority of FA is demonstrated by comparing the results over DE, Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA) optimization techniques for the same interconnected power system. Additionally, a Unified Power Flow Controller (UPFC) is placed in the tie-line and Superconducting Magnetic Energy Storage (SMES) units are considered in both areas. Simulation results show that the system performances are improved significantly with the proposed UPFC and SMES units. Sensitivity analysis of the system is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed controller design is verified by considering different types of load patterns.