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 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

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

show abstract

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

show abstract

Cell-Resolved PV Soiling Measurement Using Drone Images

Winkel,

Wilbert,

Röger

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The maintenance of photovoltaic (PV) power plants is of central importance for their yield. To reach higher efficiencies in PV parks, it is helpful to detect soiling such as dust deposition and to apply this information to optimize cleaning strategies. Furthermore, a periodic inspection of the PV modules with infrared (IR) imagery is of advantage to detect and potentially remove faulty PV modules. Soiling can be erroneously interpreted as PV module defects and hence spatially resolved soiling measurements can improve the results of IR-based PV inspection. So far, soiling measurements are mostly performed only locally in PV fields, thus not supporting the above-mentioned IR inspections. This study presents a method for measuring the soiling of PV modules at cell resolution using RGB images taken by aerial drones under sunny conditions. The increase in brightness observed for soiled cells under evaluation, compared to clean cells, is used to calculate the transmission loss of the soiling layer. Photos of a clean PV module and a soiled module for which the soiling loss is measured electrically are used to determine the relation between the brightness increase and the soiling loss. To achieve this, the irradiance at the time of the image acquisitions and the viewing geometry are considered. The measurement method has been validated with electrical measurements of the soiling loss yielding root mean square deviations in the 1% absolute range. The method has the potential to be applied to entire PV parks in the future.

show abstract

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

Cited by 2 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

Cell-Resolved PV Soiling Measurement Using Drone Images

Contact Info

Product

Resources

About