The demand for single-phase grid-connected inverter systems is on the rise, particularly for residential power generation. These systems are becoming the go-to choice for rooftop solar PV solutions, thanks to their minimal infrastructure requirements and lower installation costs. However, solar power generation is inherently variable, influenced by factors such as solar irradiation and ambient temperature. To maximize energy extraction from PV modules, effective Maximum Power Point Tracking (MPPT) techniques are crucial. This paper introduces the design and development of a 2.1 kW single-phase grid-connected solar PV inverter system, utilizing an Adaptive Network-Based Fuzzy Inference System (ANFIS) for MPPT. The system's performance is compared against the conventional Perturb and Observe (PO) method within the MATLAB Simulink environment. Results demonstrate that the ANFIS-based MPPT inverter ensures a more stable PV output in terms of voltage, current, and power, reducing energy losses and delivering higher grid-injected power. These findings position the ANFIS technique as a superior alternative for efficient power tracking in microgrid applications.
Ashar et al. (Wed,) studied this question.