Photovoltaic system fault detection techniques: a review

El-Banby, Ghada M.; Moawad, Nada M.; Abouzalm, Belal A.; Abouzaid, Wessam F.; Ramadan, E. A.

doi:10.1007/s00521-023-09041-7

Cited by 16 publications

(7 citation statements)

References 49 publications

(56 reference statements)

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“…In addition, in recent years, different artificial intelligence techniques have been accepted as the basic methods for fault detection [30]. Studies in this area mostly include Neural Networks [31], Convolutional Neural Networks [32], Support Vector Machine [33], k-Nearest Neighbor, Decision Tree, and Fuzzy Logic.…”

Section: Methods For Fault Detection and Classificationmentioning

confidence: 99%

Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems

Baltacı,

Kıral,

Dalkılınç

et al. 2024

Applied Sciences

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The world’s energy demand is on the rise, leading to an increased focus on renewable energy options due to global warming and rising emissions from fossil fuels. To effectively monitor and maintain these renewable energy systems connected to electrical grids, efficient methods are needed. Early detection of PV faults is vital for enhancing the efficiency, reliability, and safety of PV systems. Thermal imaging emerges as an efficient and effective technique for inspection. On the other hand, evidence indicates that monitoring inverters within a solar energy farm reduces maintenance expenses and boosts production. Optimizing the efficiency of solar energy farms necessitates comprehensive analytics and data on every inverter, encompassing voltage, current, temperature, and power. In this study, our objective was to perform two distinct fault analyses utilizing image processing techniques with thermal images and machine learning techniques using inverter and other physical data. The results show that hotspot and bypass failures on the panels can be detected successfully using these methods.

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Section: Methods For Fault Detection and Classificationmentioning

confidence: 99%

Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems

Baltacı,

Kıral,

Dalkılınç

et al. 2024

Applied Sciences

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“…While the results derived from the use of these machine learning approaches are promising, they also exhibit drawbacks, particularly with respect to extensive databases leading to overfitting. Furthermore, machine learning methods present limitations in representing features of complex high-dimensional data [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

Amiri,

Kichou,

Oudira

et al. 2024

Sustainability

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The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.

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“…Therefore, the timely removal of the overlays and maintaining the cleanliness of PV panels are essential to ensure the normal operation of the PV system and prevent these failures. It is also imperative to conduct PV panel fault detection along with PV panel overlay detection [96,97].…”

Section: Arduino-based Dust Removal Systemmentioning

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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Photovoltaic system fault detection techniques: a review

Cited by 16 publications

References 49 publications

Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems

Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

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