Design and implementation of enhanced Maximum power point tracking for renewable applications using crocodile optimization Algorithm

In modern world,solar photovoltaic (PV) energy systems are gaining a lot of attention due to the need for dependable and efficient renewable energy sources. It is crucial to optimize the output of these systems in various environmental settings in order to enhance their functionality and make them more viable as sustainable energy sources. By continuously modifying the PV array's operating point to maintain maximum power, Maximum Power Point Tracking (MPPT) algorithms are essential to reaching this goal. In this regard, the Crocodile Optimization Algorithm (COA), which takes its cues from crocodile hunting behaviour, has shown promise as a metaheuristic optimization method to enhance solar PV system's MPPT. The COA demonstrates how to strike a balance between exploitation and exploration when looking for prey, imitating a crocodile's hunting style. The objective of the study was to utilize the COA algorithm for solving the MPPT problem in solar cell systems by taking advantage of its capacity to effectively search the search space and arrive at the best solution. In this work, we provide an MPPT algorithm based on COA that has been specifically tailored for solar cell systems. An initial collection of feasible solutions, or candidate voltage or current values for the MPP, is initialized by the algorithm. Then, using an objective function that assesses crocodile performance or fitness, the crocodile optimization algorithm iteratively updates the solution population to mimic the behavior of a crocodile hunter. This function calculates the discrepancy between a PV array's actual power output and the maximum power that can be obtained in a given set of environmental parameters. The stated COA-based MPPT algorithm's capacity to enhance solar PV system performance is corroborated by experimental results, when operating under severe conditions, the COA-based method exhibits greater robustness and efficiency when compared to other MPPT techniques like particle swarm optimization (PSO), Group Teaching Optimization Algorithm (GTOA) and Ant Colony Optimization (ACO) algorithm.