Advanced control strategy for power quality enhancement in grid-connected solar PV system
摘要
The adoption of solar photovoltaic (SPV) on grid systems is progressively displacing conventional power generation methods because of their many benefits. Nonetheless, a challenge linked to SPV systems involves the notable variation in DC voltage, posing a risk to the integrity of grid infrastructure and causing power quality (PQ) concerns, including voltage fluctuations and heightened total harmonic distortion (THD). Hence, to minimize the power loss, a novel method called as Honey Badger-based sequence neural control (HBbSNC) was introduced for maximum power point tracking (MPPT) system to efficiently track as well as capture the highest available PV grid. A novel sequence neural control method is proposed to address harmonic constraints within the grid system, specifically in a multilevel inverter (MLI) context. Extensive simulations were run utilizing MATLAB/Simulink under various conditions to test the accuracy of the suggested technique. Moreover, the results obtained from our proposed approach are contrasted with those of existing methods, demonstrating superior outcomes in terms of reduced THD and optimized solutions.