Coupled inductor interleaved boost converter with ANN and RNN based MPPT algorithm for PV system - Kavin, K. S.; Karuvelam, P. Subha; Kumar, Naresh; Kar, Siddheswar; Rahiman, Riyaz A.; Patwa, Sharda (International Journal of Applied Power Engineering (IJAPE), 2024)
An efficient machine learning approach for accomplishing the maximum power point tracking (MPPT) system for photovoltaic (PV) systems is proposed in this work. PV system is one of the most suitable renewable energy sources (RES) for electric vehicles (EV) based operations due to its ubiquitous availability and speed of installation. The deployment of PV-powered EVs reduces the quantity of carbon dioxide emitted into the atmosphere substantially. The primary objective of this research is to integrate a PV system with an EV load and to provide a constant power supply to the EV load with no distortions. A coupled inductor interleaved boost converter is used to raise voltage level of the PV because it has a wide conversions range with low leakage reaction times. Furthermore, the converter produces a consistent output with no fluctuations and high voltage gain. With the application of artificial neural network (ANN) based MPPT and recurrent neural network (RNN) based MPPT, the converter operational performance enhanced with steady dc link voltage is obtained. Consequently, it is stated that the employment of ANN and RNN-based MPPT controllers in PV-based systems offers improved DC link voltage regulation and lower power losses, thereby boosting system effectiveness. The MATLAB platform is used to test every component of the system's performance, and the findings show that the proposed system is more efficient than alternative approaches.