Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely

Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the...

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Autores principales: Mahdi Bahaghighat, Fereshteh Abedini, Qin Xin, Morteza Mohammadi Zanjireh, Seyedali Mirjalili
Formato: article
Lenguaje:EN
Publicado: Elsevier 2021
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Acceso en línea:https://doaj.org/article/9f139ab62e694f6c88bf638076f23664
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spelling oai:doaj.org-article:9f139ab62e694f6c88bf638076f236642021-11-28T04:33:54ZUsing machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely2352-484710.1016/j.egyr.2021.07.077https://doaj.org/article/9f139ab62e694f6c88bf638076f236642021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721005400https://doaj.org/toc/2352-4847Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms’ performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy.Mahdi BahaghighatFereshteh AbediniQin XinMorteza Mohammadi ZanjirehSeyedali MirjaliliElsevierarticleMachine visionBlade detectionImage classificationSignal processingWind turbineSmart gridsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 8561-8576 (2021)
institution DOAJ
collection DOAJ
language EN
topic Machine vision
Blade detection
Image classification
Signal processing
Wind turbine
Smart grids
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Machine vision
Blade detection
Image classification
Signal processing
Wind turbine
Smart grids
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Mahdi Bahaghighat
Fereshteh Abedini
Qin Xin
Morteza Mohammadi Zanjireh
Seyedali Mirjalili
Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
description Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms’ performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy.
format article
author Mahdi Bahaghighat
Fereshteh Abedini
Qin Xin
Morteza Mohammadi Zanjireh
Seyedali Mirjalili
author_facet Mahdi Bahaghighat
Fereshteh Abedini
Qin Xin
Morteza Mohammadi Zanjireh
Seyedali Mirjalili
author_sort Mahdi Bahaghighat
title Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
title_short Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
title_full Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
title_fullStr Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
title_full_unstemmed Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
title_sort using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
publisher Elsevier
publishDate 2021
url https://doaj.org/article/9f139ab62e694f6c88bf638076f23664
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