Published Paper Details:

The output power of a photovoltaic (PV) array, which is highly dependent on both solar radiation and cell temperature, is continuously maximized using Maximum Power Point Tracking (MPPT) techniques. The complexity of non-traditional approaches and the PV power fluctuations around the maximum power point (MPP) caused by conventional methods are compelling arguments for the need for new MPPT techniques. In order to increase the convergence, speed, and accuracy of the PV system, this study discusses numerous MPPT algorithms, including hill climbing methods like the Perturbation and Observe method (P&O), Incremental Conductance (INC), Genetic Algorithm (GA), Evolutionary Algorithms like Cuckoo Search Algorithm (CSA) and Grey Wolf Algorithm (GWO). In the typical test conditions, the suggested technique may also effectively track the global MPP. The optimum performance of the PV panel during MPPT was achieved by employing GWO more frequently to get the quick reaction of the power transient's reduction. This was done to address the oscillations and spikes problem in the output power. To extract the most power possible from the PV resource, the suggested controllers used a DC/DC boost converter. The software MATLAB/SIMULINK is used for system programming and modeling. Comparisons are made between the acquired results and those from other MPPT algorithms. To identify the global maximum power point (MPP) for PV arrays, numerous evolutionary algorithms (EA) have also been taken into consideration. The following EA algorithms are used: grey wolf algorithm, teaching-learning-based technique, mine blast optimization, genetic algorithm, particle swarm optimization (PSO), ant colony, and PSO algorithm. Even though the aforementioned EAs in terms of MPPT function satisfactorily, there is still a need to advance contemporary EAs. The aforementioned GWO results are contrasted with current optimization methods like CSA, P&O, INC, and GA.