This repository contains a study case of the work developed by Phan, B et al. in A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition [1]. Here is shown a Deep Reinforcement Learning approach for MPPT (maximum power point tracking) control.
To use this repository it is essential to use the 2020a version (or upper) of MATLAB.
If you want to use the pretrained model and observe the final result: open the EnvironmentMPPT.mlx MATLAB notebook file, then run Set up the Environment cell, then, run the first cell (RL parameters) of the Create Networks. A Simulink file with the PV architecture will be open, there you can change the parameters lr1, lr2, lr3 and T1 as you wish, then run the simulation and observe its behavior. Now, if you want to re-train the model, run all the cells in EnvornmentMPPT.mlx, which will open a window with the train process.
@article{gaviria_machine_2022,
title = {Machine learning in photovoltaic systems: A review},
issn = {0960-1481},
url = {https://www.sciencedirect.com/science/article/pii/S0960148122009454},
doi = {10.1016/j.renene.2022.06.105},
shorttitle = {Machine learning in photovoltaic systems},
abstract = {This paper presents a review of up-to-date Machine Learning ({ML}) techniques applied to photovoltaic ({PV}) systems, with a special focus on deep learning. It examines the use of {ML} applied to control, islanding detection, management, fault detection and diagnosis, forecasting irradiance and power generation, sizing, and site adaptation in {PV} systems. The contribution of this work is three fold: first, we review more than 100 research articles, most of them from the last five years, that applied state-of-the-art {ML} techniques in {PV} systems; second, we review resources where researchers can find open data-sets, source code, and simulation environments that can be used to test {ML} algorithms; third, we provide a case study for each of one of the topics with open-source code and data to facilitate researchers interested in learning about these topics to introduce themselves to implementations of up-to-date {ML} techniques applied to {PV} systems. Also, we provide some directions, insights, and possibilities for future development.},
journaltitle = {Renewable Energy},
shortjournal = {Renewable Energy},
author = {Gaviria, Jorge Felipe and Narváez, Gabriel and Guillen, Camilo and Giraldo, Luis Felipe and Bressan, Michael},
urldate = {2022-07-03},
date = {2022-07-01},
langid = {english},
keywords = {Deep learning, Machine learning, Neural networks, Photovoltaic systems, Reinforcement learning, Review},
file = {ScienceDirect Snapshot:C\:\\Users\\jfgf1\\Zotero\\storage\\G96H46L2\\S0960148122009454.html:text/html},
},
@article{phan2020deep,
title={A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition},
author={Phan, Bao Chau and Lai, Ying-Chih and Lin, Chin E},
journal={Sensors},
volume={20},
number={11},
pages={3039},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}
}[1] Jorge Felipe Gaviria, Gabriel Narváez, Camilo Guillen, Luis Felipe Giraldo, and Michael Bressan. Machine learning in photovoltaic systems: A review. ISSN 0960-1481. doi: 10.1016/j.renene.2022.06.105. URL https://www.sciencedirect.com/science/article/pii/S0960148122009454?via%3Dihub
[2] Phan, B. C., Lai, Y. C., & Lin, C. E. (2020). A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition. Sensors, 20(11), 3039.
The software is licensed under an MIT License. A copy of the license has been included in the repository and can be found here.