Deep reinforcement learning using deep-Q-network for global maximum power point tracking in photovoltaic systems

This M.Tech Electrical Engineering topic focuses on Deep reinforcement learning using deep-Q-network for global maximum power point tracking in photovoltaic systems. It belongs to the MPPT Techniques area within Renewable Energy Systems.…

Abstract	This M.Tech Electrical Engineering topic focuses on Deep reinforcement learning using deep-Q-network for global maximum power point tracking in photovoltaic systems. It belongs to the MPPT Techniques area within Renewable Energy Systems. The work is suitable for research topic selection, synopsis preparation, literature review, renewable-energy modelling, MATLAB/Simulink or Python-based simulation, controller design, optimization, forecasting, energy-management analysis, and dissertation documentation. Students can use this 2023-onward research reference as the base paper and then customize the implementation using suitable renewable source data, converter topology, MPPT strategy, storage model, grid-integration method, and performance metrics.
Reference Paper	Deep reinforcement learning using deep-Q-network for global maximum power point tracking in photovoltaic systems
Year	2024
Domain	Renewable Energy Systems
Sub-Domain	MPPT Techniques
PDF / Source Link	Open Paper / PDF
Get Help	Get Help on WhatsApp Message: Hi FE, I need help with "Deep reinforcement learning using deep-Q-network for global maximum power point tracking in photovoltaic systems" in "Renewable Energy Systems"

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Deep reinforcement learning using deep-Q-network for global maximum power point tracking in photovoltaic systems

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