A combined convolutional neural network model and support vector machine technique for fault detection and classification based on electroluminescence images of photovoltaic modules

Et-taleby, Abdelilah; Chaibi, Y.; Allouhi, A.; Boussetta, Mohammed; Benslimane, Mohamed

doi:10.1016/j.segan.2022.100946

Cited by 27 publications

(8 citation statements)

References 42 publications

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“…The basic principle of the intelligent method is to use drones, cameras, and other equipment [104][105][106] to collect visible light and infrared images of photovoltaic panels, and then use artificial intelligence technologies such as neural networks [107][108][109][110][111], fuzzy logic [112][113][114], and expert systems [115][116][117] to analyze and process the images to identify defects and failures in photovoltaic panels, such as hot spots, weed obstruction, stains, bird droppings, breakage, etc.…”

Section: Intelligent Methodsmentioning

confidence: 99%

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Yang,

Sun,

Zou

et al. 2024

Energies

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Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

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Section: Intelligent Methodsmentioning

confidence: 99%

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Yang,

Sun,

Zou

et al. 2024

Energies

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“…Hybridization of Convolution neural and SVM is not limited to aforementioned, it has been explored in the literature in differs ways to different areas of applications; classification tasks (Abdullahi et al, 2017) (Li et al, 2019a) (Manohar et al, 2019;Shrivastava et al, 2019;Hasan et al, 2019;Truong et al, 2021;Latha et al, 2021), medical imaging (Dina A et al, 2019;Oliver et al, 2020;Priya et al, 2021;Cui et al, 2019;Kibriya et al, 2021;Janghel and Rathore, 2021), forecasting (Cao and Wang, 2019;Zhang and Li, 2020) face detection and recognition (Nwosu et al, 2017;Hui and Yu-jie, 2018), Fault detection (Li et al, 2019b;Et-taleby et al, 2022).…”

Section: Hybrid Models and Their Potential In Advancing Deep Learningmentioning

confidence: 99%

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

Ekundayo,

Ezugwu

2024

Preprint

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Deep learning stands at the forefront of contemporary machine learning techniques and is well-known for its outstanding predictive accuracy, adaptability to data variability, and remarkable ability to generalize across diverse domains. These attributes have spurred rapid progress and the emergence of novel iterations within the discipline. Yet, this swift evolution often obscures the foundational breakthroughs, with even trailblazing researchers at risk of fading into obscurity despite their seminal contributions. This study aims to provide a historical narrative of deep learning, tracing its origins from the cybernetic era to its current state-of-the-art status. We critically examine the contributions of individual pioneer scholars who have profoundly influenced the development of deep neural networks under the taxonomy of supervised, unsupervised, and reinforcement learning. Furthermore, the study also discusses the trending deep neural network architectures, explaining their operational principles, confronting associated challenges, exploring real-world applications, and outlining potential future trajectories that could offer a starting point for aspiring researchers in the field.

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“…A method combining Gramian angular summation field (GASF) and squeeze and excitation-deep convolution generative adversarial network (SE-DCGAN) is proposed for series arc fault (SAF) detection in PV arrays by accurately extracting transient current data, converting it into amplified GASF images, augmenting SAF samples, training a CNN for identification, and improving generalization through fusion training, achieving high recognition accuracy without misjudgments for interference events and demonstrating improved universality [39]. Et-taleby et al [40] proposed a new model combining the convolutional neural network (CNN) for feature extraction and support vector machine (SVM) for classification to detect and classify faults in electroluminescence images of PV panels, achieving high classification performance.…”

Section: Introductionmentioning

confidence: 99%

Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology

Song,

Lu,

et al. 2024

Machines

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With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model.

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A combined convolutional neural network model and support vector machine technique for fault detection and classification based on electroluminescence images of photovoltaic modules

Cited by 27 publications

References 42 publications

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Deep Learning: Historical Overview from Inception to Actualization, Models, Applications and Future Trends

Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology

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