Optimal maximum power point tracking strategy based on greater cane rat algorithm for wind energy conversion system.

With the rapidly increasing usage of renewable sources, especially wind power, maximizing the power produced from wind energy conversion system (WECS) has become a major concern. Various methods are utilized in the domain of wind turbine performance enhancement for tracking the maximum power point (MPP). Among them, the perturb and observe (P&O) approach is widely applied because of its straightforward implementation. Nevertheless, the primary drawback of this approach is the imprecision caused by variations at the peak power point. Consequently, due to wind's arbitrary and complicated characteristics, using an intelligent optimization technique is compulsory as it can give effective tracking performance. In this study, a recently developed nature-inspired metaheuristic, termed the Greater Cane Rat Algorithm (GCRA), which emulates the cognitive foraging behavior of greater cane rats during and after the breeding season. The GCRA approach seeks to regulate the boost converter by computing the duty cycle value using the voltage and current variables. The Wind Energy Conversion System (WECS) incorporates a wind turbine, a Permanent Magnet Synchronous Generator (PMSG), a rectifier, and a DC/DC boost converter that is linked to a load. The wind system can track the maximum power via a mechanical sensorless tracker system without the need to connect an additional mechanical sensor. The suggested strategy is compared to various tracking methodologies, including the classical Perturb & Observe (P&O), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO). The obtained results, which have been executed in the environment of MATLAB/SIMULINK R2022b, illustrate that the proposed approach improves the performance of the tracking system under different wind profiles step, realistic, and ramp variation of the wind velocity. The proposed strategy outperforms a tracking efficiency that exceeds 99%, surpassing other considered tracking approaches, which are at 95.5%, 94.7%, and 91.4% with the least error ratio and the best tracking for the power coefficient ratio.