Virtual Wind Speed Sensor for Wind Turbines

A data-driven approach for development of a virtual wind-speed sensor for wind turbines is presented. The virtual wind-speed sensor is built from historical wind-farm data by data-mining algorithms. Four different data-mining algorithms are used to develop models using wind-speed data collected by anemometers of various wind turbines on a wind farm. The computational results produced by different algorithms are discussed. The neural network (NN) with the multilayer perception (MLP) algorithm produced the most accurate wind-speed prediction among all the algorithms tested. Wavelets are employed to denoise the high-frequency wind-speed data measured by anemometers. The models built with data-mining algorithms on the basis of the wavelet-transformed data are to serve as virtual wind-speed sensors for wind turbines. The wind speed generated by a virtual sensor can be used for different purposes, including online monitoring and calibration of the wind-speed sensors, as well as providing reliable wind-speed input to a turbine controller. The approach presented in this paper is applicable to utility-scale wind turbines of any type.

• Record URL:
  • http://ascelibrary.org/eyo/resource/1/jleed9/v137/i2/p59_s1?isAuthorized=no
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/8674500
• Authors:
  • Kusiak, Andrew
  • Zheng, Haiyang
  • Zhang, Zijun
• Publication Date: 2011-6

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 59-69
• Serial:
  • Journal of Energy Engineering
  • Volume: 137
  • Issue Number: 2
  • Publisher: American Society of Civil Engineers
  • ISSN: 0733-9402
  • Serial URL: https://ascelibrary.org/journal/jleed9

Subject/Index Terms

• TRT Terms: Data mining; Energy conservation; Neural networks; Statistical analysis; Turbines; Wavelets; Wind; Wind power generation; Wind pressure
• Uncontrolled Terms: Wind speed
• Subject Areas: Bridges and other structures; Energy; Environment; I15: Environment;

Filing Info

• Accession Number: 01345341
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Jul 21 2011 10:08AM