Metaheuristic Tuning of Cascade PI Controller for DC Machine Speed Control in Meca-Electrical Wind Pumping Systems using Genetic Algorithm and Hybrid ACO-PSO Optimization

Hybrid water pumping systems have increasingly been recognized as a reliable and sustainable alternative for supplying water in isolated areas. These kinds of systems harness natural energy sources, more significantly wind and solar, to guarantee that water is always there, without depending on traditional power lines. Among the different setups, the Meca-Electrical Wind Pumping System has been the subject of much research because of its capacity to incorporate both mechanical and electrical wind pumping systems' advantages. The functioning of the system is mainly based on the DC machine, which is controlled by a bidirectional DC-DC converter. For keeping the system in a stable operation, usually, a PID controller is employed because of its simple structure and easy design. But the usual PI control does not often do a good job of coping with the nonlinearities and complex dynamics of the system. To tackle these issues, a cascade PI controller is proposed, which brings in a dual-loop control system that boosts the accuracy and speed of responses. The best adjustment of controller settings is done with the help of modern optimization techniques, mainly the Genetic Algorithm (GA) and a hybrid Ant Colony Optimization and Particle Swarm Optimization (ACO-PSO). The simulation results that were obtained in simpowersystem MATLAB/Simulink show that both strategies lead to a notable enhancement in the stability and performance of the system, with ACO&PSO being the one that exhibits the best tuning capability and dynamic response.