Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve the highest efficiency in the energy generation processes. Maximum power point tracking (MPPT) applications using conventional methods may not be able to follow the global MPP (GMPP) of the PV system under changing atmospheric conditions and they could oscillate around the local MPP. In this study, a machine learning and deep learning (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary to the traditional MPPT applications using current and voltage sensors, the output resistance of the PV module estimation was made by using environmental parameters (such as temperature and radiation) and artificial intelligence algorithms in this study.The LSTM model was compared with artificial neural networks (ANN) and regression methods regarding mean square error (MSE), root mean square error(RMSE) and mean absolute error (MAE) parameters. It has been determined that the LSTM model has a better performance and could more successfully follow MPP compared to the other methods. Finally, after the comparison with the ANN method, it is proved that LSTM gives 37%, 21%, and 31% more successful MSE, RMSE, and MAE results, respectively.
Maximum power point tracking, deep learning, long-short term memory, regression, artificial neural network
KARABİNAOĞLU, MURAT SALİM; ÇAKIR, BEKİR; BAŞOĞLU, MUSTAFA ENGİN; KAZDALOĞLU, ABDÜLVEHHAB; and GÜNEROĞLU, AZİZ
"Comparison of deep learning and regression-based MPPT algorithms in PV systems,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 30:
6, Article 21.
Available at: https://journals.tubitak.gov.tr/elektrik/vol30/iss6/21