Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals

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Pandit, Ravi Kumar and Infield, David; (2017) Using Gaussian process theory for wind turbine power curve analysis with emphasis on the confidence intervals. In: 2017 6th International Conference on Clean Electrical Power (ICCEP). IEEE, ITA, pp. 744-749. ISBN 978-1-5090-4683-6 (https://doi.org/10.1109/ICCEP.2017.8004774)

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Abstract

High operation and maintenance (O&M) costs may affect the profitability and growth of wind turbine industries in long term, especially where offshore wind farms are concerned. With the increase in age of wind turbines and the expansion of offshore wind, the operation and maintenance (O&M) cost is expected to grow significantly which reinforces the drive towards condition based maintenance. Wind turbine power curves play a central role in the assessment of turbine operational health. Gaussian process theory is finding increasing application in this current emerging research area. This paper investigates the potential of Gaussian process models to improve the representation of wind turbine power curves and in particular the importance of confidence intervals as determined by such modeling.

ORCID iDs

Pandit, Ravi Kumar ORCID logoORCID: https://orcid.org/0000-0001-6850-7922 and Infield, David;
  • Item type:Book Section
    ID code:64535
    Dates:
    Date
    Event
    18 August 2017
    Published
    31 March 2017
    Accepted
    Notes:© 2017 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 > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:20 Jun 2018 08:05
    Last modified:11 Nov 2024 15:14
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/64535
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