Enhanced Voltage Regulation in Solar PV Systems using ANN-based MPPT control for DC-DC Converter

Maximizing the power output of a photovoltaic (PV) system is vital, and to accomplish this, maximum power point tracking (MPPT) technologies are applied. Among the various MPPT techniques available, artificial neural network (ANN) stands out due to their decreased oscillations and rapid reaction benefits. However, creating an efficient ANN-MPPT technique requires precise training data, which could be troublesome. This paper provides an ANN-MPPT strategy, using a vast number of experimental training data to minimize training errors. The inputs to the ANN model consist of irradiance and temperature, depicting climatic conditions, while the output is the available electricity at the MPPT point from the PV system. The performance evaluation of the proposed ANN-MPPT technique is carried out using a MATLAB/Simulink model of a PV system. To maintain a constant voltage for the load, the duty cycle of the DC-DC converter in the microcontroller is managed based on the ANN MPPT technique. A comparison is done between the proposed ANN-MPPT methodology and extant MPPT approaches to assess its efficacy.