Deep Reinforcement Learning Based MPPT Control for Grid Connected PV System

Maximum power point tracking (MPPT) helps in generating maximum power from PV system at a specified irradiance levels irrespective of changes in the sun's position and cloud cover conditions. From previous studies, it is observed that, conventional methods for MPPT suffers from oscillations aro...

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Bibliographic Details
Main Authors: Vora, Kunal, Liu, Shichao, Dhulipati, Himavarsha
Format: Conference Proceeding
Language:English
Subjects:
Deep reinforcement learning
Energy Storage
Heuristic algorithms
Maximum power point trackers
Maximum power point tracking
Perturb and Observe Algorithm
Photovoltaic System
Q-learning
Renewable energy sources
Simulation
Software packages
Sustainable Energy
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Summary:Maximum power point tracking (MPPT) helps in generating maximum power from PV system at a specified irradiance levels irrespective of changes in the sun's position and cloud cover conditions. From previous studies, it is observed that, conventional methods for MPPT suffers from oscillations around maximum power point and does not adapt to changing environmental conditions of irradiance and temperature. Therefore, new techniques like reinforcement learning is implemented in PV system to overcome aforementioned limitations. In this paper, integration of deep learning and reinforcement learning named deep Q-learning (DQN) is implemented in grid connected PV system. DQN solves the problem of varying environmental conditions by discretizing the state spaces. The proposed method is implemented in MATLAB/ SIMULINK environment. Based on the simulation results, it can be proposed that proposed method is efficient in handling ever changing environmental conditions.
ISSN:2769-3899
DOI:10.1109/ICPS59941.2024.10639977