Grid-Connected PV Inverter System using the IPSO-based FLCs and NFCs for Enhancing PV Energy Utilizing Efficiency and Power Quality.

In modern distributed power photovoltaic (PV) grid-connected 1-φ inverter system below 10 kW, the PV mismatch that lowers the PV energy utilization efficiency limits this technology’s potential. An utilized switching in double-stage conversion introduces harmonics and degrades overall efficiency. Nonlinear adaptive controllers are required for strongly nonlinear photovoltaic (PV) converter-inverter-based switching in weather-affected systems to optimize performance. This paper proposes a maximum power point tracking (MPPT) algorithm based on two controllers, including a fuzzy logic controller (FLC) and a neuro-fuzzy controller (NFC), on the converter side and an additional FLC and NFC for DC-bus voltage and grid-current control on the inverter side. The sizing components of the system used in the modeling are comprehensively presented. The topology of FLCs and NFCs are achieved by using the improved particle swarm optimization (IPSO), thereby reducing complexity and achieving optimal control performance. The proposed FLCs and NFCs are implemented and evaluated on a 2.5 kW grid-connected PV system using Matlab/Simulink, while comparing with conventional FLC and FLC-PSO, including simple controllers of P&O and PI controllers. Grid current-voltage synchronization and THD reduction are achieved via a phase locked loop (PLL) circuit and an LCL filter. The simulation results show that the proposed controllers improve MPPT performance, in terms of PV energy utilization efficiency, by 8% to 40% on the converter side and achieve up to 96% inverter efficiency, reducing THD by 75% over the rest. [ABSTRACT FROM AUTHOR]