LSTM-NN Based Modelling and Simulation of 8 MW Grid-Connected Photovoltaic (PV) System Using Real-time Data

Abstract

This paper proposes an advanced neural network-based Modelling and control approach for an 8 MW grid-connected photovoltaic (PV) system, leveraging real-world irradiance and temperature data to enhance power extraction efficiency. Traditional maximum power point tracking (MPPT) methods, while effective, exhibit limitations in dynamic response and accuracy under rapidly varying environmental conditions. To address this, a The LSTM-based NN (LMST-NN) is trained on historical solar irradiance and temperature datasets to predict the optimal operating points of the PV array, replacing conventional MPPT algorithms. The system architecture comprises a PV array, a DC-DC boost converter (elevating voltage to 11 kV), and a three-phase inverter synchronized with the utility grid using a phase-locked loop (PLL). The neural network’s predictions dynamically adjust the boost converter’s duty cycle, ensuring maximum power transfer under fluctuating conditions. Simulation in MATLAB/Simulink validates the system’s performance, demonstrating superior prediction accuracy compared to traditional techniques. Additionally, the integration of an LCL filter maintains total harmonic distortion (THD) below 1%, complying with IEEE 519 standards. Key metrics, including grid synchronization stability (frequency deviation < 0.05 Hz), converter efficiency (97.5%), and transient response, are rigorously evaluated. The results highlight the neural network’s robustness in optimizing power generation while ensuring seamless grid integration, offering a promising alternative to conventional MPPT for large-scale PV systems.