Optical Stepped Thermography of Defects in Photovoltaic Panels

Guo, Xingming; Cai, Jiahua

doi:10.1109/jsen.2020.3013024

Cited by 16 publications

(7 citation statements)

References 17 publications

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“…Thus, it becomes feasible to concurrently identify multiple faults by evaluating variations in the impedance of PV modules i N = 1, …, at the fault locations. Equations (10) can be reformulated as (11), allowing for the derivation of impedance values R i i N ( ), = 1, …, c , using (12) and (13).…”

Section: Peaks and Valleys Matricesmentioning

confidence: 99%

“…[2][3][4][5][6][7] Despite their proven effectiveness, these methods face limitations, including the requirement for costly equipment, the labor-intensive nature of inspections, and the challenge of managing large data sets in learning-based approaches. [8][9][10] Thermal imaging and I-V curve analysis play pivotal roles in fault detection, [11][12][13] albeit with limitations in the precision of fault localization. Other techniques 14 suggest the use of thermal images and convolutional neural networks (CNNs) for fault diagnosis in solar panels, which represents a novel and effective method for enhancing maintenance and efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Lamdihine,

Ouassaid

2024

Energy Science & Engineering

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This paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed ‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems.

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Section: Peaks and Valleys Matricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Lamdihine,

Ouassaid

2024

Energy Science & Engineering

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“…A transfer learning approach is presented in [11,12] towards the detection of a defect in the solar surface panel, which combines the result of transfer learning with AlexNet CNN. Applying a data processing scheme to thermal images [13,14] for background subtraction, and applying discrete Fourier transform and histogram equalization scheme to enhance the quality of the image. Towards localizing the defect in PV panels and cells, ST has been used in [15].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

Prabhakaran¹,

Uthra²,

Preetharoselyn³

2023

Computer Systems Science and Engineering

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The Problem of Photovoltaic (PV) defects detection and classification has been well studied. Several techniques exist in identifying the defects and localizing them in PV panels that use various features, but suffer to achieve higher performance. An efficient Real-Time Multi Variant Deep learning Model (RMVDM) is presented in this article to handle this issue. The method considers different defects like a spotlight, crack, dust, and micro-cracks to detect the defects as well as localizes the defects. The image data set given has been preprocessed by applying the Region-Based Histogram Approximation (RHA) algorithm. The preprocessed images are applied with Gray Scale Quantization Algorithm (GSQA) to extract the features. Extracted features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of neurons. Each class neuron has been designed to measure Defect Class Support (DCS). At the test phase, the input image has been applied with different operations, and the features extracted passed through the model trained. The output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the image. Further, the method uses the Higher-Order Texture Localization (HOTL) technique in localizing the defect. The proposed model produces efficient results with around 97% in defect detection and localization with higher accuracy and less time complexity.

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“…Tsuzuki et al [3] proposed using the relationship between voltage and current obtained on specific semiconductors to detect their defects. Cai et al [4] proposed a stepwise thermal imaging method for detecting defects in photovoltaic panels. Although the aforementioned methods can identify specific types of defects in solar panels, they are plagued by problems such as low detection accuracy and inefficiency.…”

Section: Introductionmentioning

confidence: 99%

Research on Visual Recognition Method for Defect Detection in Photovoltaic Cells Based on Deep Learning

Wang,

Yin,

Wang

2024

Preprint

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The quality and efficiency of photovoltaic power generation are closely related to the excellent performance of solar panels.Using defective panels may reduce power generation efficiency and product lifespan, and even lead to serious safety accidents .Therefore, strict defect detection is necessary before the photovoltaic panels leave the factory.This paper proposes a detection algorithm based YOLOv5 for Photovoltaic panel inspection.By combining a linear bottleneck inverted residual structure with an efficient pyramid split attention module called EPSA, the computational and parameter requirements are reduced. Additionally, by enhancing the optimization of Ghost convolution and deep convolution, the computational load is further reduced. The balanced cross-entropy loss function and non-maximum suppression method are combined to investigate multiple small target defect detections. Comparison experiment results on the defect dataset of photovoltaic cells demonstrate that the accuracy (P) of the enhanced algorithm has increased by 6.30%, and the average accuracy (mAP) has increased by 9.40%.The proposed model effectively identifies photovoltaic cell defects, fulfilling the criteria for photovoltaic panel inspection, and showing promising application prospects.

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Optical Stepped Thermography of Defects in Photovoltaic Panels

Cited by 16 publications

References 17 publications

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

Research on Visual Recognition Method for Defect Detection in Photovoltaic Cells Based on Deep Learning

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