Photovoltaic Image Registration Based on Feature Matching via Guided Spatial Consensus

Song, Ming; Li, Lan; Chen, Shuhan; Dai, Shuguang; Li, Fang

doi:10.1109/jphotov.2021.3084818

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Typically, the UAV-acquired data of PVs will contain a solar panel and where the solar panel is usually in the shape of a square or rectangle and is typic of semiconductor materials (e.g., silicon) that are highly absorbent of solar lig surface is dark in color. The gridlines are the conductive lines located on the sur solar panels; these lines form a grid-like structure [28] that connects the solar p is used to collect and conduct the current generated by the solar panels, and it white or silver. Figure 2 In this stage, two ways are employed to remove the gridlines of the ac module: (1) a threshold-based cutting method and fabrication mask and (2) based cutting.…”

Section: Remove Gridlinesmentioning

confidence: 99%

Stain Detection Based on Unmanned Aerial Vehicle Hyperspectral Photovoltaic Module

Li,

Cui

et al. 2023

Remote Sensing

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Solar power generation has great development potential as an abundant and clean energy source. However, many factors affect the efficiency of the photovoltaic (PV) module; among these factors, outdoor PV modules are inevitably affected by stains, thus reducing the power generation efficiency of the PV panel. This paper proposes a framework for PV module stain detection based on UAV hyperspectral images (HSIs). The framework consists of two stain detection methods: constrained energy minimization (CEM)-based and orthogonal subspace projection (OSP)-based stain detection methods. Firstly, the contaminated PV modules are analyzed and processed to enhance the data’s analytical capability. Secondly, based on the known spectral signature of the PV module, stain detection methods are proposed, including CEM-based stain detection and OSP-based stain detection for PV modules. The experimental results on real data illustrate that, in comparison with contrasting methods, the proposed method achieves stain detection results that closely align with known stain percentages. Additionally, it exhibits a fitting curve similar to the more maturely developed electroluminescence-based methods currently in use.

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Section: Remove Gridlinesmentioning

confidence: 99%