Load frequency control of a three-area power system based on the optimal tuning of a fractional-order proportional–integral–derivative controller with multi-objective gray wolf optimization

Abstract

In an interconnected electrical power system, load frequency control is the most important ancillary service that is essential for maintaining electrical system reliability at an adequate level. Herein, a multi-objective gray wolf optimization (MOGWO) algorithm was introduced to maintain balance between the exploitation and exploration stages and provide the best fitness value. During a disturbance in the output of the system, optimization was implemented using the firefly algorithm (FFA) and MOGWO, which were carefully tuned, and the parameters were compared with those of the three-area three-source model of the power system. A fractional-order proportional–integral–derivative (FOPID) controller is a PID controller whose derivative and integral orders are fractions rather than integers. The FOPID controller supports the improved stability of the designed model with controlled deviations in the frequency and grid tie-line power. In this study, the optimal tuning of the parameters of controllers, such as PI, PID, and FOPID, in the power system was done to regulate the frequency in the multi-area multi-source model designed with hydro-thermal-gas generation units. The output performance of the proposed model was estimated based on the simulation results using the MATLAB-SIMULINK tool. The dynamic performance of the system was examined by introducing step-load perturbations of 1% or 2% in one area. Sensitivity analysis revealed that the FFA and MOGWO optimized the FOPID controller parameters obtained under nominal conditions of loading, size, and position of disturbance. The system parameters remained robust and did not require to be reset even with significant changes in the system. The simulation results demonstrated the effectiveness of the FOPID controller in the presence and absence of the FF and MOGWO algorithms. Additionally, the MOGWO algorithm was executed appropriately and led to improved performance in terms of metrics, such as overshoot, undershoot, and settling time.