Load frequency control of three area power system using optimal tuning of fractional order proportional integral derivative controller with multi objective grey wolf optimization

Abstract

In an interconnected electrical power system, load frequency control is a most important ancillary service essential for maintaining the electrical system reliability at an adequate level. A Multi-Objective Grey Wolf Optimization (MOGWO) algorithm is introduced for maintaining a balance among exploitation and exploration stages and provides the best value of fitness. During the occurrence of the disturbance in the output of the system the optimization is implemented with Firefly Algorithm (FFA) and MOGWO which is tuned carefully and also the parameters are compared with three-area three-source model of the power system. A Fractional Order Proportional and Derivative (FOPID) controller is a PID controller whose derivative and integral orders are fractional rather than integer. The FOPID controller supports the better stability of the designed model with controlled deviation in frequency and grid tie line power deviations. In this proposed research work, optimal tuning of parameters of various controllers like PI, PID and FOPID in the power system is to regulate frequency in a multi-area multi-source model which is designed with the hydro-thermal-gas generation units. The output performance of the model designed is estimated from simulation results by means of MATLAB-SIMULINK tool. The dynamic performances of the system are studied with 1% or 2% step load perturbation in one Area. Sensitivity analysis reveals that the FFA and MOGWO optimized FOPID controller parameters obtained at nominal condition of loading, size and position of disturbance and system parameters are robust and need not be reset with wide changes in system.The simulation results show the effectiveness of FOPID controller in the presence and the absence of the FF and MOGWO algorithms considered and in that MOGWO algorithm is executed appropriately and it has improved the performance based on metrics such as overshoot, undershoot, and settling time.