Visible light imaging offers broad coverage and low cost, enabling extensive inspections. To address the current limitations of low precision and high image data requirements in defect detection algorithms based on visible light imaging, this paper proposes a novel visible light image defect detection algorithm for photovoltaic panels.
infrared imaging for detecting faults in photovoltaic (PV) modules has gained prominence recently. This method utilizes infrared devices to capture thermal images of PV systems. By analyzing the temperatu
Given the characteristics of photovoltaic power plants, deep learning-based defect detection models can be deployed on surveillance systems or drone patrols, enabling automated defect detection and ensuring the efficient operation and maintenance of photovoltaic panels.
On the photovoltaic panel visible light image dataset, the proposed algorithm shows outstanding performance, achieving a mean average precision of 88.98% and outperforming the original model and other state-of-the-art models across four performance metrics.
This paper builds a photovoltaic panel equipment intelligent management system to record photovoltaic equipment information in the power system. The system uses the YOLOv5 target detection model to
In this study, an advanced distributed PV identification model, PV Identifier, is proposed to improve the identification performance of small distributed PVs in complex backgrounds from
Therefore, this paper proposes an intelligent detection method for photovoltaic power panels based on the improved Faster-RCNN target detection algorithm to analyze and identify
This paper presents an Artificial Intelligence solution for fault detection and classification in photovoltaic systems. The proposed tool integrates electrical and visual analysis methods,
The research results have shown that the combined use of a well-trained U-Net neural network and Decision tree can diagnose the PV panel faults with 99.8% accuracy. Therefore, it may
This identification algorithm provides automated inspection and monitoring capabilities for photovoltaic panels under visible light conditions.
manual inspection methods highly inefficient and inadequate for modern photovoltaic power stations. To address this issue, this paper proposes a method and system for hot spot detecti
This assertion is affirmed by testing the model with additional data from separate video-capturing PV panels. The video was recorded using a drone equipped with a thermal camera.
In this paper, we propose an approach that identifies PV panels by means of a deterministic algorithm that carefully and extensively analyses the colours of the pixels forming the
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