Optimized Control of Single-Stage Grid-Connected PV Inverters with Embedded ANN based MPPT

This paper presents a control strategy for single-stage grid-connected photovoltaic (PV) inverters. The objective of this strategy is to address the two primary challenges of this topology: maximizing power extraction through a novel maximum power point tracking (MPPT) method and regulating both active and reactive power injection. The proposed strategy includes two main components. First, an innovative MPPT algorithm based on adaptive linear neurons (ADALINE) is used to maximize the extracted power. This method is built on the fact that at the maximum power point, the derivative of the PV output power with respect to the panel voltage equals zero. ADALINE is chosen for its simplicity, quick convergence, and online learning capability. Second, a simplified control method is developed to manage grid connection and regulate active and reactive power by adjusting the inverter’s voltage amplitude and phase. This approach is significantly simpler than existing methods, which involve more complex calculations. Simulation tests demonstrate the strategy’s effectiveness, showing the MPPT’s ability to adapt under varying conditions and ensuring optimal power extraction. Additionally, the obtained results highlight the effective regulation of both active and reactive power. The injected current demonstrates high quality with low total harmonic distortion. This ensures compliance with grid standards while significantly improving system stability.