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26 pages, 5487 KiB

Open AccessArticle

An Extended Methodology for Sizing Solar Unmanned Aerial Vehicles: Theory and Development of a Python Framework for Design Assist

by José Roberto Cândido da Silva and Gefeson Mendes Pacheco

Sensors 2021, 21(22), 7541; https://doi.org/10.3390/s21227541 - 12 Nov 2021

Cited by 1 | Viewed by 2877

Abstract

There is a growing interest in using unmanned aerial vehicles (UAVs) in the most diverse application areas from agriculture to remote sensing, that determine the need to project and define mission profiles of the UAVs. In addition, solar photovoltaic energy increases the flight autonomy of this type of aircraft, forming the term Solar UAV. This study proposes an extended methodology for sizing Solar UAVs that take off from a runway. This methodology considers mission parameters such as operating location, altitude, flight speed, flight endurance, and payload to sizing the aircraft parameters, such as wingspan, area of embedded solar cells panels, runway length required for takeoff and landing, battery weight, and the total weight of the aircraft. Using the Python language, we developed a framework to apply the proposed methodology and assist in designing a Solar UAV. With this framework, it was possible to perform a sensitivity analysis of design parameters and constraints. Finally, we performed a simulation of a mission, checking the output parameters. Full article

(This article belongs to the Special Issue Photovoltaics Generators and Sensors)

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9 pages, 2664 KiB

Open AccessCommunication

Design of Grating Al₂O₃ Passivation Structure Optimized for High-Efficiency Cu(In,Ga)Se₂ Solar Cells

by Chan Hyeon Park, Jun Yong Kim, Shi-Joon Sung, Dae-Hwan Kim and Yun Seon Do

Sensors 2021, 21(14), 4849; https://doi.org/10.3390/s21144849 - 16 Jul 2021

Cited by 5 | Viewed by 2546

Abstract

In this paper, we propose an optimized structure of thin Cu(In,Ga)Se₂ (CIGS) solar cells with a grating aluminum oxide (Al₂O₃) passivation layer (GAPL) providing nano-sized contact openings in order to improve power conversion efficiency using optoelectrical simulations. Al₂O₃ is used as a rear surface passivation material to reduce carrier recombination and improve reflectivity at a rear surface for high efficiency in thin CIGS solar cells. To realize high efficiency for thin CIGS solar cells, the optimized structure was designed by manipulating two structural factors: the contact opening width (COW) and the pitch of the GAPL. Compared with an unpassivated thin CIGS solar cell, the efficiency was improved up to 20.38% when the pitch of the GAPL was 7.5–12.5 μm. Furthermore, the efficiency was improved as the COW of the GAPL was decreased. The maximum efficiency value occurred when the COW was 100 nm because of the effective carrier recombination inhibition and high reflectivity of the Al₂O₃ insulator passivation with local contacts. These results indicate that the designed structure has optimized structural points for high-efficiency thin CIGS solar cells. Therefore, the photovoltaic (PV) generator and sensor designers can achieve the higher performance of photosensitive thin CIGS solar cells by considering these results. Full article

(This article belongs to the Special Issue Photovoltaics Generators and Sensors)

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22 pages, 8996 KiB

Open AccessArticle

Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

by Horng-Horng Lin, Harshad Kumar Dandage, Keh-Moh Lin, You-Teh Lin and Yeou-Jiunn Chen

Sensors 2021, 21(13), 4292; https://doi.org/10.3390/s21134292 - 23 Jun 2021

Cited by 14 | Viewed by 3528

Abstract

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about

2.71

s. The average segmentation errors along the x-direction and y-direction are only

1.6

pixels and

1.4

pixels, respectively. The defect detection approach on segmented cells achieves

99.8 %

accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization. Full article

(This article belongs to the Special Issue Photovoltaics Generators and Sensors)

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