ANN-Based MPPT Control of a Standalone PV System with Bi-Directional Battery Management and NPC Inverter for Dynamic 3-Phase AC Loads

This paper presents an intelligent energy management system for a standalone photovoltaic (PV) system integrated with a lithium-ion battery and a three-level neutral-point clamped (NPC) inverter to supply a three-phase AC load. An Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller is employed to enhance the efficiency and dynamic response of the PV system under varying irradiance conditions. The extracted power is regulated through a DC-DC converter, maintaining an optimal DC link voltage.To ensure reliable power delivery and load balancing, a bi-directional DC-DC converter is interfaced between the battery and the DC link. A Proportional-Integral (PI) controller governs the charging and discharging of the battery based on the power difference between the PV generation and the AC load demand. This mechanism allows for smooth energy transition, enabling battery charging during surplus PV generation and discharging during power deficits.The DC link supplies power to a three-level NPC inverter, which is responsible for converting DC power into high-quality AC output for three-phase loads. An inverter control strategy is implemented to maintain voltage stability and minimize total harmonic distortion (THD). The proposed system is simulated in MATLAB/Simulink, and its performance is analyzed under dynamic load and environmental conditions. Results demonstrate improved MPPT accuracy, effective battery management and stable AC output voltage, showcasing the suitability of the system for off-grid applications.