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

Mahdi Bahaghighat, Fereshteh Abedini, Qin Xin, Morteza Mohammadi Zanjireh, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
55 Downloads (Pure)


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.
Original languageEnglish
Number of pages16
JournalEnergy Reports
Publication statusPublished - 6 Aug 2021


  • Machine vision
  • Blade detection
  • mage classification
  • Signal processing
  • Wind turbine
  • Smart grids


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