Next Article in Journal
Key Schedule against Template Attack-Based Simple Power Analysis on a Single Target
Next Article in Special Issue
Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image
Previous Article in Journal
Phenological Model Intercomparison for Estimating Grapevine Budbreak Date (Vitis vinifera L.) in Europe
Previous Article in Special Issue
Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
Open AccessArticle

Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera

1
Pattern Recognition and Machine Learning Laboratory, Gachon University, Seongnam 13120, Korea
2
Department of Fire Service Administration, Chodang University, Mu-An 58530, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(11), 3802; https://doi.org/10.3390/app10113802
Received: 28 April 2020 / Revised: 20 May 2020 / Accepted: 26 May 2020 / Published: 29 May 2020
(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)
In the last few decades, photovoltaic (PV) power station installations have surged across the globe. The output efficiency of these stations deteriorates with the passage of time due to multiple factors such as hotspots, shaded cell or module, short-circuited bypass diodes, etc. Traditionally, technicians inspect each solar panel in a PV power station using infrared thermography to ensure consistent output efficiency. With the advancement of drone technology, researchers have proposed to use drones equipped with thermal cameras for PV power station monitoring. However, most of these drone-based approaches require technicians to manually control the drone which in itself is a cumbersome task in the case of large PV power stations. To tackle this issue, this study presents an autonomous drone-based solution. The drone is mounted with both RGB (Red, Green, Blue) and thermal cameras. The proposed system can automatically detect and estimate the exact location of faulty PV modules among hundreds or thousands of PV modules in the power station. In addition, we propose an automatic drone flight path planning algorithm which eliminates the requirement of manual drone control. The system also utilizes an image processing algorithm to process RGB and thermal images for fault detection. The system was evaluated on a 1-MW solar power plant located in Suncheon, South Korea. The experimental results demonstrate the effectiveness of our solution. View Full-Text
Keywords: photovoltaic power station; fault detection; autonomous drone; thermal image analysis photovoltaic power station; fault detection; autonomous drone; thermal image analysis
Show Figures

Figure 1

MDPI and ACS Style

Henry, C.; Poudel, S.; Lee, S.-W.; Jeong, H. Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera. Appl. Sci. 2020, 10, 3802.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop