Potential measurement techniques for photovoltaic module failure diagnosis: A review

Rahman, Md. Momtazur; Khan, Imran; Alameh, Kamal

doi:10.1016/j.rser.2021.111532

Cited by 34 publications

(14 citation statements)

References 70 publications

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“…The objective function is Equation (5). When the continuous function takes an extremum, the partial derivative is zeros:…”

Section: Gamma Distribution Degradation Modelmentioning

confidence: 99%

“…To simplify the operation, PSO is applied to maximize the objective function L in Equation (5). In this model, A and C can be set as particles in PSO, thus B can be calculated by Equation (25).…”

Section: Gamma Distribution Degradation Modelmentioning

confidence: 99%

“…Photovoltaic module degradation modes can be divided into three types 5 : early loss, stable loss, and wear-out loss. Early loss is generally caused by the problem during installation and can be easily detected in the early life.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distribution‐based PV module degradation model

Lai¹,

Wang²,

Wang

et al. 2023

Energy Science & Engineering

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The degradation of photovoltaic modules has an impact on various parameters of photovoltaic modules. Ignoring the degradation of photovoltaic modules or inaccurate estimation of the degradation will lead to wrong power dispatching strategies and lead to economic losses. For PV module life estimation or reliability estimation, it is necessary to first establish an accurate statistical degradation model of PV module. The main goal of this paper is to analyze a selection of explicit PV module degradation model based on distribution. Since the degradation is related to time, the study assumed that those parameters in Gamma or Gaussian distributions are related to time. Five models are calculated based on maximum likelihood estimation and particle swarm optimization. Through verification and comparison on the measured PV module degradation data, the performance of these models in four cases: longterm data fitting, long-term data prediction, single-module short-term data fitting, and multimodule short-term data fitting are evaluated. The results show that the model proposed in this paper has a great improvement over the original model, and the constant-σ Gaussian distribution degradation model achieves the best performance.

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“…The objective function is Equation (5). When the continuous function takes an extremum, the partial derivative is zeros:…”

Section: Gamma Distribution Degradation Modelmentioning

confidence: 99%

Section: Gamma Distribution Degradation Modelmentioning

confidence: 99%

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Distribution‐based PV module degradation model

Lai¹,

Wang²,

Wang

et al. 2023

Energy Science & Engineering

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show abstract

“…The current processing techniques for PV panel images are mainly divided into two categories [28]. The first category is the traditional machine learning methods, which mostly rely on manually designed extractors and require the manual construction of complex recognition relationships [29], and their generalization ability and robustness could be better [7,30,31].…”

Section: Introductionmentioning

confidence: 99%

GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Wang

Zhang

2023

Electronics

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Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects on the PV panel surface and the high similarity between different defects make it challenging to accurately identify and detect such defects. This paper proposes an approach named Ghost convolution with BottleneckCSP and a tiny target prediction head incorporating YOLOv5 (GBH-YOLOv5) for PV panel defect detection. To ensure better accuracy on multiscale targets, the BottleneckCSP module is introduced to add a prediction head for tiny target detection to alleviate tiny defect misses, using Ghost convolution to improve the model inference speed and reduce the number of parameters. First, the original image is compressed and cropped to enlarge the defect size physically. Then, the processed images are input into GBH-YOLOv5, and the depth features are extracted through network processing based on Ghost convolution, the application of the BottleneckCSP module, and the prediction head of tiny targets. Finally, the extracted features are classified by a Feature Pyramid Network (FPN) and a Path Aggregation Network (PAN) structure. Meanwhile, we compare our method with state-of-the-art methods to verify the effectiveness of the proposed method. The proposed PV panel surface-defect detection network improves the mAP performance by at least 27.8%.

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“…In addition to regular solar plant monitoring and inspections, the success of solar energy will also critically hinge on the availability of suitable, cost-effective, and accurate inspection methods to determine the best use of decommissioned PV modules (end-of-life assessment) to enable a thriving circular economy [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Outdoor luminescence imaging of field-deployed PV modules

Kunz¹,

Schlipf²,

Fladung³

et al. 2022

Prog. Energy

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Solar photovoltaic installations have increased exponentially over the last decade and are now at a stage where they provide humanity with the greatest opportunity to mitigate accelerating climate change. For the continued growth and success of photovoltaic energy the reliable inspection of solar power plants is an important requirement. This ensures the installations are of high quality, safe to operate, and produce the maximum possible power for the longest possible plant life. Outdoor luminescence imaging of field-deployed PV modules provides module image data with unparalleled fidelity and is therefore the gold standard for assessing the quality, defect types, and degradation state of field-deployed PV modules. Several luminescence imaging methods have been developed and some of them are already routinely used to inspect solar power plants. The preferred luminescence inspection method to be used depends on the required image resolution, the defect types that need to be identified, cost, inspection throughput, technological readiness, and other factors. Due to the rich and detailed information provided by luminescence imaging measurements and modern image analysis methods, luminescence imaging is becoming an increasingly important tool for PV module quality assurance in PV power plants. Outdoor luminescence imaging can make valuable contributions to the commissioning, operation, and assessment of solar power plants prior to a change of ownership or after severe weather events. Another increasingly important use of these technologies is the cost-effective end-of-life assessment of solar modules to enable a sustainable circular economy.

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Potential measurement techniques for photovoltaic module failure diagnosis: A review

Cited by 34 publications

References 70 publications

Distribution‐based PV module degradation model

Distribution‐based PV module degradation model

GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection

Outdoor luminescence imaging of field-deployed PV modules

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