A Review of Photovoltaic Module Failure and Degradation Mechanisms: Causes and Detection Techniques

Solar, Год журнала: 2024, Номер 4(1), С. 43 - 82

Опубликована: Янв. 9, 2024

With the global increase in deployment of photovoltaic (PV) modules recent years, need to explore and understand their reported failure mechanisms has become crucial. Despite PV being considered reliable devices, failures extreme degradations often occur. Some within normal range may be minor not cause significant harm. Others initially mild but can rapidly deteriorate, leading catastrophic accidents, particularly harsh environments. This paper conducts a state-of-the-art literature review examine failures, types, root causes based on components (from protective glass junction box). It outlines hazardous consequences arising from module describes potential damage they bring system. The reveals that each component is susceptible specific types failure, with some deteriorating own others impacting additional components, more severe failures. Finally, this briefly summarises detection techniques, emphasising significance electrical characterisation techniques underlining importance considering parameters. Most importantly, identifies most prevalent degradation processes, laying foundation for further investigation by research community through modelling experimental studies. allows early comparing performance when or occur prevent serious progression. worth noting studies included primarily focus detailing observed operations, which attributed various factors, including manufacturing process other external influences. Hence, provide explanations these do extensively corrective actions propose solutions either laboratory experiments real-world experience. Although, field study, there are corresponding have designed suggest preventive measures solutions, an in-depth those beyond scope paper. However, paper, turn, serves as valuable resource scholars confining critically evaluate available preventative actions.

Язык: Английский

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Model-based fault detection in photovoltaic systems: A comprehensive review and avenues for enhancement

Results in Engineering, Год журнала: 2024, Номер 21, С. 101835 - 101835

Опубликована: Янв. 30, 2024

Solar photovoltaic (PV) systems have become a vital renewable energy source, witnessing rapid global demand. Nevertheless, these are susceptible to faults and anomalies that can deteriorate performance yield significant consequences. Hence, this paper is dedicated reviewing recent advancements in monitoring, modeling, fault detection methods for PV systems. It encompasses diverse system types, including grid-connected, stand-alone, hybrid configurations, delves into the latest data acquisition monitoring techniques. The review also discusses various modeling approaches, empirical, analytical, numerical models, highlighting significance of model validation calibration. Furthermore, it provides comprehensive analysis model-based Overall, underscores pivotal role offers thorough comprehension available techniques enhancing management maintenance.

Язык: Английский

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A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Applied Sciences, Год журнала: 2024, Номер 14(5), С. 2072 - 2072

Опубликована: Март 1, 2024

Photovoltaic systems are prone to breaking down due harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing type learning used. In this article, we propose a supervised algorithm that can detect and classify PV system defects. We delve into world learning-based machine its application in detecting classifying defects photovoltaic (PV) systems. explore various types faults occur provide concise overview most commonly used techniques diagnosing such Additionally, introduce novel classifier known as Extra Trees or Extremely Randomized speedy approach for Although has not yet explored realm fault detection classification installations, it is highly recommended remarkable precision, minimal variance, efficient processing. The purpose article assist technicians, engineers, researchers identifying typical responsible failures, well creating effective control supervision minimize breakdowns ensure longevity installed

Язык: Английский

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An Enhanced Buck-Boost Converter for Photovoltaic Diagnosis application: Accurate MPP Tracker and I-V Tracer

Scientific African, Год журнала: 2025, Номер unknown, С. e02561 - e02561

Опубликована: Янв. 1, 2025

Язык: Английский

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Towards a Holistic Approach for UAV-Based Large-Scale Photovoltaic Inspection: A Review on Deep Learning and Image Processing Techniques

Technologies, Год журнала: 2025, Номер 13(3), С. 117 - 117

Опубликована: Март 14, 2025

This paper provides an in-depth literature review on image processing techniques, focusing deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation geolocation, classification, optimizations model generalization. Furthermore, challenges related to domain adaptation, dataset limitations, multimodal fusion RGB thermal are also discussed. Finally, research gaps opportunities analyzed create a holistic, scalable, real-time inspection workflow large-scale installation. serves as reference researchers industry professionals advance inspection.

Язык: Английский

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Evaluating tracking bifacial solar PV based agrivoltaics system across the UK

Solar Energy, Год журнала: 2024, Номер 284, С. 113102 - 113102

Опубликована: Ноя. 16, 2024

Язык: Английский

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Deep regression analysis for enhanced thermal control in photovoltaic energy systems

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 23, 2024

Abstract Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture spatial variability in thermal patterns across panels, impeding accurate assessment system performance. Existing methods quantifying lack precision, hindering optimization PV maintenance and renewable energy output. This research introduces a novel approach utilizing deep learning techniques address these limitations. A U-Net architecture is employed segment panels from background elements imaging videos, facilitating comprehensive analysis efficiency. Two predictive models—a 3-layer Feedforward Neural Network (FNN) proposed Convolutional (CNN)—are developed compared estimating percentages individual images. The study aims enhance precision reliability heat mapping capabilities non-invasive, vision-based monitoring photovoltaic dynamics. By leveraging regression techniques, CNN model demonstrates superior capability traditional methods, enabling estimation efficiencies diverse scenarios. Experimental evaluation illustrates supremacy capability, yielding mean square error (MSE) just 0.001171821, as opposed FNN’s MSE 0.016. Furthermore, remarkable improvements absolute (MAE) R-square, registering values 1.2% 0.95, respectively, whereas FNN posts comparatively inferior numbers 3.5% 0.85. labeled datasets tailored architectures, accelerating advancements technology solutions. Moreover, provides insights into practical implementation cost-effectiveness system, highlighting hardware requirements, integration with existing infrastructure, sensitivity analysis. economic viability scalability assessed through cost-benefit assessment, demonstrating significant potential cost savings revenue increases large-scale installations. strategies addressing limitations, enhancing accuracy, scaling larger discussed, laying groundwork future industry collaboration field management optimization.

Язык: Английский

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Influence of Environmental Conditions on the Electrical Parameters of Side Connectors in Glass–Glass Photovoltaic Modules

Energies, Год журнала: 2024, Номер 17(3), С. 680 - 680

Опубликована: Янв. 31, 2024

This work focused on the verification of electrical parameters and durability side connectors installed in glass–glass photovoltaic modules. Ensuring safe use modules is achieved, among others, by using connecting PV cell circuit inside laminate with an external electric cable. In most cases for standard modules, connector form a junction box attached from back module. The glued to module surface silicone where busbars were previously brought out through specially prepared holes. An alternative method place edge module, laminating part it. such case, “wings” are tightly permanently connected foil, between two glass panes protecting against breakdown. Additionally, this approach eliminates process preparing holes which especially complicated time-consuming case Moreover, desirable BIPV applications because they allow more flexible design installations façades walls buildings. A series samples G-G connectors, then subjected testing influence environmental conditions. All characterized before after effect conditions according PN-EN-61215-2 standards. Insulation resistance tests performed dry wet conditions, ensuring full contact tested sample water. For all being placed climatic chamber, values far above minimum value required standards, allowing be safely used. tests, range GΩ, while obtained MΩ. further work, influences accordance MQT-11, MQT-12, MQT-13 measurements again. simulation impact changing test showed that insulation reduced order magnitude both tests. one can observe visual changes lamination foil connector. carried show potential their advantage over rear boxes, but also technological challenges need overcome.

Язык: Английский

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Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

International Journal of Science and Research Archive, Год журнала: 2024, Номер 11(2), С. 730 - 739

Опубликована: Март 30, 2024

Power electronics pertains to the conception, regulation, and utilization of electronic power circuits proficiently administer transform electrical energy. play a crucial role in maintaining reliability, efficiency, security complex production systems. Also, increasingly important various applications such as renewable energy systems, electric vehicles, industrial automation. However, modern systems are vulnerable both cyber physical anomalies due integration information communication technologies. So far, different methods have been used detect abnormalities. This survey provides an overview state-of-the-art anomaly detection using machine learning deep methods. It highlights potential these techniques addressing growing complexity vulnerability

Язык: Английский

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A model for energy predictions and diagnostics of large-scale photovoltaic systems based on electric data and thermal imaging of the PV fields

Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 206, С. 114858 - 114858

Опубликована: Авг. 30, 2024

Язык: Английский

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