DE optimized IPIDF controller for management frequency in a networked power system with SMES and HVDC link - Biswal, Ashutosh; Dwivedi, Prakash; Bose, Sourav (Frontiers in Energy Research, 2022)
A major concern is frequency change with load. So, Load Frequency Control (LFC) of an interconnected power system is proposed in this research using a unique IPIDF. The Differential Evolution (DE) algorithm is used to optimize the IPIDF controller parameters for a two-area power system. By contrasting the results of the proposed method with those of recently published optimization techniques for the same power system, such as the Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), and Differential Evolution (DE) based PID and PIDF controllers, the superiority of the IPIDF approach is made clear. It is possible to determine the system performance index like integral time multiplied the absolute error (ITAE) and the settling time (Ts). The power system with superconducting magnetic energy storage and an HVDC link is also included in the proposed work, and the values of the suggested IPIDF controllers are evaluated using the Differential Evolution method. By comparing the outcomes with the DE tuned PIDF controller for the identical power systems, the suggested controller's superiority is demonstrated. To show the stability of the recommended Differential Evolution algorithm tuned IPIDF controller, the speed governor, turbine, synchronizing coefficient, and frequency bias parameters' time constants and operating load conditions are varied in the range of +25 to -25% from their nominal values, along with the magnitude and location of step load perturbation and pulse load perturbation, to perform sensitivity analysis. According to research, proposed IPIDF controllers offer greater dynamic response by minimizing time required to settle and undershoots than PID controllers and PIDF controllers. MATLAB/Simulink is used to run the simulations.