BMS for Wind-Battery Powered Standalone Microgrid by LSTM-ANN Controllers

This study explores the integration of battery management systems (BMS) in standalone wind-battery-powered microgrids using LSTM-ANN controllers. Wind energy, in-herently variable due to weather dependence, requires robust energy management to ensure power stability and reliability. The research focuses on implementing a system to effectively manage energy distribution among generation, storage, and load components. LSTM-ANN controllers are employed for precise and adaptive control, ensuring stable operation despite rapid fluctuations in power supply and demand. The controllers enhance the efficiency of maximum power point tracking (MPPT) and bidirectional DC-DC converters, minimizing energy losses and improving overall system performance. Hardware-in-the-loop (HIL) simulations conducted on the OPAL-RT platform validate the proposed system, demonstrating reliable voltage regulation at the DC link and seamless handling of dynamic conditions. These results emphasize the system's ability to optimize energy use and ensure uninterrupted power supply, even under challenging circumstances. The research contributes to advancing intelligent energy management in microgrids, offering scalable solutions for reliable and sustainable renewable energy integration.