Dynamic Controller Design for Maximum Power Point Tracking Control for Solar Energy Systems
M. A. Fkirin, Zeinab M. Gowaly, Emad A. Elsheikh
Abstract
The demand for efficient renewable energy solutions has spurred the development of advanced maximum power point tracking (MPPT) algorithms for photovoltaic (PV) systems, especially under variable atmospheric conditions. This study proposes a dynamic MPPT controller utilizing a combination of Long Short-Term Memory (LSTM)-based Artificial Neural Networks (ANNs) and Fuzzy Logic Control (FLC) to optimize power extraction in solar energy systems across diverse irradiance and temperature conditions. The study focuses on designing and implementing these two dynamic MPPT algorithms, LSTM-ANN and LSTM-FLC, to effectively manage the inherent variability in solar energy generation due to fluctuating atmospheric conditions, ensuring that the PV system consistently operates at its optimal power point. The proposed controllers are evaluated and compared to LSTM–Proportional Integral (PI) and traditional MPPT methods, including ANNs, Fuzzy Logic, and hybrid ANN–Fuzzy. The performance metrics used in the evaluation include tracking efficiency, response time, and system stability. The simulation results with real-time data demonstrate that the LSTM-optimized controllers significantly outperform conventional methods, particularly in adapting to sudden changes in irradiance and temperature.