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Estimation of Wind Turbine Angular Velocity Remotely Found on Video Mining and Convolutional Neural Network

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Department of Electrical Engineering, Engineering Faculty, Raja University, Qazvin 95834, Iran
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Faculty of Science and Technology, University of the Faroe Islands, FO 100 Tórshavn, Faroe Islands
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Electrical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15875-4413, Iran
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Computer Engineering Department, Imam Khomeini International University (IKIU), Qazvin 21333, Iran
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Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(10), 3544; https://doi.org/10.3390/app10103544
Received: 2 March 2020 / Revised: 6 April 2020 / Accepted: 15 April 2020 / Published: 20 May 2020
Today, energy issues are more important than ever. Because of the importance of environmental concerns, clean and renewable energies such as wind power have been most welcomed globally, especially in developing countries. Worldwide development of these technologies leads to the use of intelligent systems for monitoring and maintenance purposes. Besides, deep learning as a new area of machine learning is sharply developing. Its strong performance in computer vision problems has conducted us to provide a high accuracy intelligent machine vision system based on deep learning to estimate the wind turbine angular velocity, remotely. This velocity along with other information such as pitch angle and yaw angle can be used to estimate the wind farm energy production. For this purpose, we have used SSD (Single Shot Multi-Box Detector) object detection algorithm and some specific classification methods based on DenseNet, SqueezeNet, ResNet50, and InceptionV3 models. The results indicate that the proposed system can estimate rotational speed with about 99.05 % accuracy. View Full-Text
Keywords: machine vision; deep learning; object detection; image classification; remote sensing; wind turbine; WTCM; angular velocity machine vision; deep learning; object detection; image classification; remote sensing; wind turbine; WTCM; angular velocity
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Bahaghighat, M.; Xin, Q.; Motamedi, S.A.; Zanjireh, M.M.; Vacavant, A. Estimation of Wind Turbine Angular Velocity Remotely Found on Video Mining and Convolutional Neural Network. Appl. Sci. 2020, 10, 3544.

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