Soiling Detection for Photovoltaic Modules Based on an Intelligent Method with Image Processing

Hwang, Po Ching; Ku, Cooper Cheng-Yuan; Chan, James Chi-Chang

doi:10.1109/icce-taiwan49838.2020.9258175

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Other works focused on visible images to detect the cause of hotspot, e.g. soiling [19] or dust [20] [21]. Authors in [19] applied image processing techniques to UAV images to assess the soiling rate, while they postponed to a future work combination of artificial intelligence and statistical algorithms to evaluate the soiling distribution on PV panel surface.…”

Section: State Of the Artmentioning

confidence: 99%

“…soiling [19] or dust [20] [21]. Authors in [19] applied image processing techniques to UAV images to assess the soiling rate, while they postponed to a future work combination of artificial intelligence and statistical algorithms to evaluate the soiling distribution on PV panel surface. [20] and [21] simulated instead different dust coverage and demonstrated that image matching, or a shallow neural network fed with texture features extracted by gray level co-occurrence matrix, are effective solutions for evaluating the degree of pollution.…”

Section: State Of the Artmentioning

confidence: 99%

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A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle

Tommaso¹,

Betti²,

Fontanelli³

et al. 2021

Preprint

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As solar capacity installed worldwide continues to grow, there is an increasing awareness that advanced inspection systems are becoming of utmost importance to schedule smart interventions and minimize downtime likelihood. In this work we propose a novel automatic multi-stage model to detect panel defects on aerial images captured by unmanned aerial vehicle by using the YOLOv3 network and Computer Vision techniques. The model combines detections of panels and defects to refine its accuracy. The main novelties are represented by its versatility to process either thermographic or visible images and detect a large variety of defects and its portability to both rooftop and ground-mounted PV systems and different panel types. The proposed model has been validated on two big PV plants in the south of Italy with an outstanding

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle

Tommaso¹,

Betti²,

Fontanelli³

et al. 2021

Preprint

View full text Add to dashboard Cite

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Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

Aghaei,

Kolahi,

Nedaei

et al. 2024

Progress in Photovoltaics

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This study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

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Electrothermal Modeling of Photovoltaic Modules for the Detection of Hot-Spots Caused by Soiling

Winkel,

Smretschnig,

Wilbert

et al. 2024

Energies

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Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots.

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Soiling Detection for Photovoltaic Modules Based on an Intelligent Method with Image Processing

Cited by 4 publications

References 8 publications

A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle

A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

Electrothermal Modeling of Photovoltaic Modules for the Detection of Hot-Spots Caused by Soiling

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