Smart Solar Water Pumping: ANN-Enhanced MPPT System for Optimized Performance

In photovoltaic (PV) water pump systems, Maximum Power Point Tracking (MPPT) controllers play a critical role due to the nonlinear IV characteristics of PV systems and the variability of their maximum power point with solar irradiation. These controllers ensure that the PV array operates at its highest power point under varying lighting conditions, thereby maximizing solar energy conversion. This study proposes an MPPT technique based on artificial neural networks (ANN) for brushless direct current (BLDC) motors powered by photovoltaics. The system comprises a solar array, a buck-boost converter, a three-phase voltage source inverter, and a BLDC motor. The proposed ANN controller generates a pulse width modulated (PWM) control signal to adjust the operational duty cycle of the buck-boost converter to the load, utilizing the output power of the PV array and the rotational speed of the DC motor as input signals. The ANN controller dynamically adjusts the internal and PV generator impedance to increase the engine speed, ensuring efficient operation across various solar irradiance levels. A comprehensive simulation of the system was conducted using MATLAB (R2022B)/SIMULINK to demonstrate the effectiveness of the ANN control strategy under different solar conditions.