HYBRID PSO-DE TECHNIQUE TO OPTIMIZE ENERGY RESOURCE FOR PV SYSTEM

The output power and the power-voltage (P-V) characteristics of the PV array entirely depend on the solar irradiation. Therefore, if these environmental parameters change rapidly, the PV array's P-V curve's maximum power point (MPP) often varies quite quickly. This rapid MPP fluctuation may be in line with the uniform shading of the PV panel or may be in line with the partial shading due to the clouds, tall buildings, leaves, and raindrops. In both cases, however, MPP tracking (MPPT) is not just a nonlinear problem, it becomes an extremely nonlinear problem, a time-bound solution. Owing to the extremely fluctuating atmospheric conditions, after any short period of time, the P-V characteristic shifts. The paper is about optimizing maximum power point tracking in the PV cell. Nevertheless, one of the major obstacles to this form of energy source's widespread increase is insufficient energy efficiency due to the transformation of solar energy into electricity. Hence the extraction of the maximum potential power of each panel is the biggest technological challenge nowadays. Here we suggest a new MPPT optimization based on particle swarm differential evolution optimization technique. The resultant graphs show that MPPT results are improved after applying the above-mentioned optimization technique.