A mobile educational tool designed for teaching and dissemination of grid connected photovoltaic systems

Torres, Pedro Ferreira; Costa, André Felipe Pacheco; Chaar, Vilson Lima; Monteiro, Weslley Leão; Galhardo, Marcos André Barros; Pinho, João Tavares; Macêdo, Wilson Negrão

doi:10.1016/j.compeleceng.2019.03.017

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…The detection accuracy of the algorithm for complete fruits was between 80 and 89%. Torres et al (2019) proposed a green citrus detection method based on two hyperspectral reflectance imaging systems, resulting in a detection accuracy of 96.97%. Although multispectral images and hyperspectral images can provide many clues to green citrus, at the same time, there is still much information redundancy, which may lead to the poor real-time performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network

et al. 2021

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The accurate detection of green citrus in natural environments is a key step in realizing the intelligent harvesting of citrus through robotics. At present, the visual detection algorithms for green citrus in natural environments still have poor accuracy and robustness due to the color similarity between fruits and backgrounds. This study proposed a multi-scale convolutional neural network (CNN) named YOLO BP to detect green citrus in natural environments. Firstly, the backbone network, CSPDarknet53, was trimmed to extract high-quality features and improve the real-time performance of the network. Then, by removing the redundant nodes of the Path Aggregation Network (PANet) and adding additional connections, a bi-directional feature pyramid network (Bi-PANet) was proposed to efficiently fuse the multilayer features. Finally, three groups of green citrus detection experiments were designed to evaluate the network performance. The results showed that the accuracy, recall, mean average precision (mAP), and detection speed of YOLO BP were 86, 91, and 91.55% and 18 frames per second (FPS), respectively, which were 2, 7, and 4.3% and 1 FPS higher than those of YOLO v4. The proposed detection algorithm had strong robustness and high accuracy in the complex orchard environment, which provides technical support for green fruit detection in natural environments.

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Section: Introductionmentioning

confidence: 99%

A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network

et al. 2021

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“…Hardware based remote experiments start to appear in Mexico, where there is a high need for replacing the ordinary sources of energy by renewable one, targeting well educated student in this field. A mobile PV system for educational purposes is implemented [7]. The prototype is consisted of four PV module associated with Data acquisition system.…”

Section: Introductionmentioning

confidence: 99%

Implementing NI Environment for A Remotely Accessed Photovoltaic Module Characterizer.

Mahmoud

Abdel-Salam

Swief

2020

Journal of Petroleum and Mining Engineering

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This paper surveys how the characterization process for a Photovoltaic module (PV) is implemented to serve in introductory undergraduate courses and research purposes. The characterizer has the capability of measuring the I-V and P-V characteristic curve of the PV module attached to the system with indicating the solar irradiance that is sensed during the measurement. Also a control circuit design is implemented for dual tracking the PV module. In addition, the developed program introduces the facility of compering the measured data with a numerical based simulation data results from a PV simulator using MATLAB platform. The use of the National Instrument (NI) Environment facilitates the Internet publication opportunity where users can access the setup remotely. The remote accessibility is considered as a significant credit in integrating the setup as a demonstration tool in teaching modules as well as student accessibility. The results of the NI software are presented and discussed and analysed.

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“…This technique, makes it possible to run after the maximum power that the module is capable of supplying [17]. The generated power depends on weather conditions irradiation and temperature [18][19][20]. Under these latter conditions, the electrical characteristics of the PVM have only one optimal.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study Between Three Methods for Optimizing the Power Produced from Photovoltaic Generator

Ndiaye¹,

Faye²,

Ndiaye³

2020

Adv. sci. technol. eng. syst. j.

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A mobile educational tool designed for teaching and dissemination of grid connected photovoltaic systems

Cited by 12 publications

References 14 publications

A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network

A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network

Implementing NI Environment for A Remotely Accessed Photovoltaic Module Characterizer.

Comparative Study Between Three Methods for Optimizing the Power Produced from Photovoltaic Generator

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