@article{4071,
  author = {Alexander Gozhyj, Vladyslav Nechakhin and Irina Kalinina},
  title = {Maximum Power Point Tracking (MPPT) Controllers for Improving the efficiency of Solar Panels},
  journal = {Transactions on Machine Design},
  year = {2024},
  volume = {12},
  number = {2},
  doi = {https://doi.org/10.6025/tmd/2024/12/2/62-73},
  url = {https://www.dline.info/tmd/fulltext/v12n2/tmdv12n2_2.pdf},
  abstract = {This study explores how Long Short-Term Memory (LSTM) neural networks can be used as Maximum Power Point Tracking (MPPT) controllers for solar panels. It compares existing MPPT algorithms like Perturb and Observe (P&O), Incremental Conductance (IncCond), and Hill Climbing (HC) with LSTM-based methods in terms of their accuracy, efficiency, and ability to adapt. Data on voltage, current, power output, temperature, and solar irradiance from various locations are utilized to train and assess the LSTM model. The findings show that LSTM-based MPPT controllers are more effective than traditional methods, providing better tracking precision and the ability to adjust to changing environmental conditions. The research emphasizes the importance of LSTM-based controllers in improving the efficiency of solar panels and increasing the amount of energy collected. This study aids in the development of renewable energy technologies and highlights the role of artificial intelligence in optimizing solar energy systems.
},
}