The use of machine learning and performance concept to monitor and predict wind power output

Sathler, Kelvin Palhares Bastos and Kolios, Athanasios; (2022) The use of machine learning and performance concept to monitor and predict wind power output. In: International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 . IEEE, CZE, pp. 1-8. ISBN 9781665470872 (https://doi.org/10.1109/icecet55527.2022.9873076)

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Abstract

Monitoring and predicting wind power output more precisely can be very beneficial for an increasingly competitive Wind Power industry. Although many advances have been made throughout the last decades, the production forecast is still based mainly on the manufacturing power curve and wind speed. Even though this approach is very useful, especially during the design phase, it does not consider other factors that affect production, such as topography, weather conditions, and wind features. A more precise prediction model that is able to recognize production fluctuation and is tailored using current operational data is proposed in this paper. The model analyzes the performance through Meteorological Mast Data (Met Mast Data) and then uses it as an input to monitor and predict power output. As a result, the model proposed achieves high accuracy and can be key to understanding the wind turbine asset's behavior throughout its lifespan, assisting operators in decision making to increase overall power production.

  • Item type:Book Section
    ID code:82708
    Dates:
    Date
    Event
    9 September 2022
    Published
    22 July 2022
    Published Online
    15 June 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Hydraulic engineering. Ocean engineering
    Department:Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering
    Depositing user: Pure Administrator
    Date deposited:12 Oct 2022 13:22
    Last modified:14 Apr 2024 00:15
    URI:https://strathprints.strath.ac.uk/id/eprint/82708
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