Operation of Grid-Connected PV System with ANN-Based MPPT and an Optimized LCL Filter Using GRG Algorithm for Enhanced Power Quality

Abstract

The expanding use of photovoltaics (PV) as a green energy resource has been rising in these years, mostly due to the possibility of being incorporated with traditional power systems, to meet the world's energy needs and reduce carbon emissions. However, providing green electricity from this renewable generator is frequently vulnerable to power quality (PQ) disruptions resulting from the PV's intermittent nature and other factors associated with the electric grid, power converters, and linked loads. These disruptions need to be reduced to keep the investigated system's PQ from deteriorating. The investigated system includes PV, DC-DC, and DC-AC converters, filter, power grid, and control schemes. If the DC-DC converter is not managed, a deviation from the maximum power point (MPP) extrapolated from the PV system will take place. In order to maximize the energy harvested from the PV system by managing the DC-DC converter, this research developed two MPP tracking (MPPT) algorithms: artificial neural networks (ANN) and cuckoo search (CS). Additionally, a design and implementation for a shunt active power filter (LCL) using genetic algorithm and GRG is provided to lower the injected total harmonic distortion (THD) and thereby enhance the PQ. To achieve the smallest size of the LCL components, the generalized reduced gradient (GRG) was the best compared to genetic algorithms GA. The results of the simulation showed that ANN performed better at tracking maximum power than CS. With the designed LCL, the THD is reduced by 99.78% compared to without a filter. To verify the simulation's findings, a practical configuration is implemented.