Comment
Author: Admin | 2025-04-28
Author / Affiliation / Email Article Menu Font Type: Arial Georgia Verdana Open AccessArticle by Mahdi Bahaghighat 1,*, Qin Xin 2,*, Seyed Ahmad Motamedi 3, Morteza Mohammadi Zanjireh 4 and Antoine Vacavant 5 1 Department of Electrical Engineering, Engineering Faculty, Raja University, Qazvin 95834, Iran 2 Faculty of Science and Technology, University of the Faroe Islands, FO 100 Tórshavn, Faroe Islands 3 Electrical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15875-4413, Iran 4 Computer Engineering Department, Imam Khomeini International University (IKIU), Qazvin 21333, Iran 5 Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France * Authors to whom correspondence should be addressed. Submission received: 2 March 2020 / Revised: 6 April 2020 / Accepted: 15 April 2020 / Published: 20 May 2020 Abstract: 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. 1. IntroductionEnergy is one of the most basic requirements in human life and has become even more important in our advanced world. In recent years, in terms of technology, economics, and environmental issues, renewable energy sources have been considered more than ever. Endless energy resources such as wind and solar energies are clean and can reduce environmental impacts [1,2]. Wind energy is one of the most important renewable energy sources and many countries are predicted to increase wind energy portion of their whole national energy supply to about twenty percent in the next decade [1,2,3]. Figure 1 shows wind farms development from 2017 to 2022 in the globe [2].Compared to steam, hydro, and gas turbines used in traditional power plants, wind turbines (WTs) are usually operated in harsher environments and, therefore, have relatively higher failure rates. The faults in WTs can be classified into two categories: wear-out failures and temporary random faults. Wear-out failures are long-term and permanent events. Repairing or replacing a failed
Add Comment