Dynamic Controller Design for Maximum Power Point Tracking Control for Solar Energy Systems

Technologies, Год журнала: 2025, Номер 13(2), С. 71 - 71

Опубликована: Фев. 8, 2025

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

Язык: Английский

PV Systems Operating in Dynamic Climatic Circumstances Using a PSO-Based SMC and PID Controller

Опубликована: Май 12, 2024

Язык: Английский