PV panel angle optimization using landmark-based drone imaging and LiDAR data: Genetic algorithm and real-time MPPT integration
Abstract
This study presents a new integrated analysis methodology that utilises three-dimensional (3D) surface reorientation and real-time maximum power point tracking (MPPT) simulations, supported by drone-based imaging and LiDAR technologies, to increase the angular alignment accuracy of fixed and trackable photovoltaic (PV) systems. In the project conducted in a solar panel field, the tilt and direction angles of the panels were determined using LiDAR point cloud analysis, along with reference data obtained from drone images. The obtained angles were analyzed in terms of their effects on annual energy production, and then the optimal angles were calculated with a genetic algorithm (GA). We then used a genetic algorithm to determine the optimal orientation for each panel, aiming to maximize the annual energy yield. Finally, the P&O-based MPPT algorithm was integrated with the system for real-time production increase. The results indicate that misalignment causes significant annual energy losses ranging from 0.25% to 0.62%, which the proposed optimization strategy can significantly reduce. At the end of the study, annual production losses due to angular deviations were determined to be between 3.4% and 9.2%. The sample area’s annual energy gain after system correction was as high as 6.8%.
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URN: https://sloi.org/urn:sl:tjoee101353
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