Artificial Neural Network-Based MPPT Controller for PV System Integrated with Grid and Induction Motor

Priyanka Nikhil Mane, Rushikesh Sunil Suryawanshi, Neel Ajit Mangave, Harshal Anandrao Nalawade, Ayan Firoj Sayyad

2025

Abstract

The integration of a Photovoltaic (PV) system with an induction motor and grid offers a sustainable solution for energy generation and utilization, particularly in industrial and commercial applications. However, to guarantee effective functioning, it is essential to optimize the power extracted from PV system. The PV system's operating point, which fluctuates with external conditions including temperature and sun irradiation, must be dynamically adjusted by a Maximum Power Point Tracking (MPPT) controller in order to capture maximum amount of power. Traditional MPPT algorithms, while effective, may not always provide optimal performance in fluctuating conditions. To address this, an Artificial Neural Network (ANN) based MPPT controller enhance the tracking accuracy and efficiency. By learning from system behavior and adjusting in real-time, the ANN-based controller outperforms conventional methods, offering superior performance and faster convergence to Maximum Power Point (MPP). When integrated with the grid and an induction motor, this intelligent MPPT controller ensures not only optimal energy extraction from the PV system but also stable power delivery. The induction motor, driven by solar energy, operates efficiently with minimal energy losses, while the grid connection facilitates the exchange of power, ensuring system stability. Simulation results are obtained using MATLAB, showing that efficiency of the tracking method is 93.5% and the THD value of 2%.

Paper Citation

in Harvard Style

Mane P., Suryawanshi R., Mangave N., Nalawade H. and Sayyad A. (2025). Artificial Neural Network-Based MPPT Controller for PV System Integrated with Grid and Induction Motor. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 721-732. DOI: 10.5220/0013642900004664

in Bibtex Style

@conference{incoft25,
author={Priyanka Mane and Rushikesh Suryawanshi and Neel Mangave and Harshal Nalawade and Ayan Sayyad},
title={Artificial Neural Network-Based MPPT Controller for PV System Integrated with Grid and Induction Motor},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={721-732},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013642900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Artificial Neural Network-Based MPPT Controller for PV System Integrated with Grid and Induction Motor
SN - 978-989-758-763-4
AU - Mane P.
AU - Suryawanshi R.
AU - Mangave N.
AU - Nalawade H.
AU - Sayyad A.
PY - 2025
SP - 721
EP - 732
DO - 10.5220/0013642900004664
PB - SciTePress