Deep reinforcement learning using deep-Q-network for Global Maximum Power Point tracking: Design and experiments in real photovoltaic systems

Deep reinforcement learning using deep-Q-network for Global Maximum Power Point tracking: Design and experiments in real photovoltaic systems

This paper presents a methodology for integrating Deep Reinforcement Learning (DRL) using a Deep-Q-Network (DQN) agent into real-time experiments to achieve the Global Maximum Power Point (GMPP) of Photovoltaic (PV) systems under various environmental conditions. Conventional methods, such as the Pe...

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Bibliographic Details
Main Authors: Luis Felipe Giraldo, Jorge Felipe Gaviria, María Isabella Torres, Corinne Alonso, Michael Bressan
Format: Article
Language:English
Published: Elsevier 2024-11-01
Series:Heliyon
Subjects:
Photovoltaic systems
Global maximum power point tracking
Neural networks
Reinforcement learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844024140054
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