PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection

Yin, Wang; Lingxin, Shen; Maohuan, Li; Sun, Qianlai; Li, Xiaosong

doi:10.1109/access.2023.3240894

Cited by 16 publications

(6 citation statements)

References 25 publications

(22 reference statements)

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“…In terms of PV fault detection, different versions of the YOLO series models have been applied and some encouraging research results have been achieved. Huang et al [121] applied the YOLOv5 to detect faults in solar panels and improve the YOLOv5. The results show that the improved method has a detection accuracy of 93.9%, which is 1.5% higher than the unmodified YOLOv5 method.…”

Section: Single Methodsmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Hu,

Lim,

Tian

et al. 2024

CAAI Artificial Intelligence Research

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Integrating artificial intelligence (AI) into photovoltaic (PV) systems has become a revolutionary approach to improving the efficiency, reliability, and predictability of solar power generation. In this paper, we explore the impact of AI technology on PV power generation systems and its applications from a global perspective. Central to the discussion are the pivotal applications of AI in maximum power point tracking (MPPT), power forecasting, and fault detection within the PV system. On the one hand, the integration with AI technology enables the optimization and improvement of the operational efficiency of PV systems. On the other hand, new challenges have been observed, mainly in the areas of data processing and model management. Moreover, advances in AI technology and hardware upgrades will lead to the rapid global popularization of new energy sources such as solar energy, which is expected to replace traditional energy sources. Finally, we describe forward-looking solutions including transfer learning, few-shot learning, and edge computing, as well as the state of the art.

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Section: Single Methodsmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Hu,

Lim,

Tian

et al. 2024

CAAI Artificial Intelligence Research

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“…To further improve detection speed, it used the GhostConv which is a lightweight module to modify the backbone, and used the Gaussian error linear unit activation function to replace the original activation function in YOLOv5s. Besides, to balance the detection accuracy, a bidirectional feature pyramid network and attention mechanism were also introduced into YOLOv5s.To improve the accuracy of photovoltaic module defects detection based on lightweight YOLO, Wang et al 29 proposed PV-LOLO based on YOLOX. Zhang et al 30 also proposed an improved YOLOv5 to improve the accuracy and speed of the defect detection of PV modules.…”

Section: Related Workmentioning

confidence: 99%

Defect detection of photovoltaic modules based on improved VarifocalNet

Jia,

Chen,

Zhao

2024

Sci Rep

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Detecting and replacing defective photovoltaic modules is essential as they directly impact power generation efficiency. Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To address this issue, an improved VarifocalNet has been proposed to enhance both the detection speed and accuracy of defective photovoltaic modules. Firstly, a new bottleneck module is designed to replace the first bottleneck module of the last stage convolution group in the backbone. This new module includes both standard convolution and dilated convolution, enabling an increase in network depth and receptive field without reducing the output feature map size. This improvement can help to enhance the accuracy of defect detection for photovoltaic modules. Secondly, another bottleneck module is also designed and used to replace the original bottleneck module used in the fourth stage convolution group of the backbone. This new module has smaller parameters than the original bottleneck module, which is useful to improve the defect detection speed of the photovoltaic module. Thirdly, a feature interactor is designed in the detection head to enhance feature expression in the classification branch. This helps improve detection accuracy. Besides, an improved intersection over union is proposed and introduced into the loss function to measure the difference between the predicted and ground truth boxes. This is useful for improving defect detection accuracy. Compared to other methods, the proposed method has the highest detection accuracy. Additionally, it also has a faster detection speed than other methods except for the DDH-YOLOv5 method and the improved YOLOv7 method.

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“…Regarding speed and efficiency, the CNN approach in one study was particularly emphasized for its rapid fault identification [41]. Additionally, the automated inspection of PV panels using Unmanned Aerial Vehicles (UAVs) indicates a faster approach in certain studies [44,45]. Another paper highlighted the efficiency of CNNs for automating feature extraction, potentially accelerating the fault detection process [46].…”

Section: Comparison Of Techniquesmentioning

confidence: 99%

“…Moreover, deep learning-based photovoltaic fault detection techniques that particularly employ images are covered in [49]. There is also a focus on capturing images from UAVs to detect PV faults and classify them [45]. The automation of identifying visual faults in PV modules has been effectively addressed [53].…”

Section: Efficiency Of Deep Learning Modelsmentioning

confidence: 99%

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Islam,

Rashel,

Ahmed

et al. 2023

Energies

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Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification.

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PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection

Cited by 16 publications

References 25 publications

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

A Comprehensive Review of Artificial Intelligence Applications in the Photovoltaic Systems

Defect detection of photovoltaic modules based on improved VarifocalNet

Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

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