Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system

TLDR: It is observed that the optimum gains of the proposed fuzzy PID controller need not be reset even if the system is subjected to variation in loading condition and system parameters, and the superiority of hybrid DEPSO algorithm over differential evolution and particle swarm optimisation algorithm has been demonstrated.

Abstract: A novel fuzzy proportional–integral derivative (PID) controller is proposed in this study for automatic generation control (AGC) of interconnected power systems. The optimum gains of the proposed fuzzy PID controller are optimised employing a hybrid differential evolution particle swarm optimisation (DEPSO) technique using an integral of time multiplied by absolute value of error criterion. The superiority of hybrid DEPSO algorithm over differential evolution and particle swarm optimisation (PSO) algorithm has also been demonstrated. The results are also compared with some recently published approaches such as artificial bee colony and PSO based proportional–integral/PID controllers for the same interconnected power systems. Furthermore, performance of the proposed system is analysed by varying the different parameters such as loading condition, system parameters and objective functions. It is observed that the optimum gains of the proposed fuzzy PID controller need not be reset even if the system is subjected to variation in loading condition and system parameters. Finally, the study is extended to a three area system considering generation rate constraint to demonstrate the ability of the proposed approach to cope with multiple interconnected systems. Comparison with previous AGC methods reported in the literature validates the significance of the proposed approach.