Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoring

Ravi Pandit, David Infield

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

5 Citations (Scopus)
16 Downloads (Pure)

Abstract

Unscheduled maintenance consumes a lot of time and effort and hence reduces the overall cost-effectiveness of wind turbines. Supervisory control and data acquisition (SCADA) based condition monitoring is a cost-effective approach to carry out diagnosis and prognosis of faults and to provide performance assessment of a wind turbine. The rotor speed based power curve, which describes the nonlinear relationship between wind turbine rotor speed and power output, is useful for performance appraisal of a wind turbine though limited work on this area has been undertaken to date. Support Vector Machine (SVM) is a data-driven, nonparametric approach used for both classification and regression problems developed initially from statistical learning theory (SLT) by Vapnik. SVM is useful in forecasting and prediction applications.
This paper deals with the application of support vector regression to estimate the rotor speed based power curve of a wind turbine and its usefulness in identifying potential faults. It is compared with a conventional approach based on a binned rotor speed power curve to identify operational anomalies. The comparative studies summaries the advantages and disadvantages of these techniques. SCADA data obtained from a healthy operational wind turbine is used to train and validate these methods.
Original languageEnglish
Title of host publication2018 53rd International Universities Power Engineering Conference (UPEC)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
ISBN (Electronic)9781538629109
DOIs
Publication statusPublished - 13 Dec 2018
EventThe 53rd International Universities Power Engineering Conference - Glasgow Caledonian University, Glasgow , United Kingdom
Duration: 4 Sep 20187 Sep 2018
Conference number: 53
http://www.upec2018.com/

Conference

ConferenceThe 53rd International Universities Power Engineering Conference
Abbreviated titleUPEC2018
CountryUnited Kingdom
CityGlasgow
Period4/09/187/09/18
Internet address

Keywords

  • wind turbine
  • condition monitoring
  • SCADA analysis
  • predictive maintenance
  • SVM technique
  • rotor speed

Fingerprint Dive into the research topics of 'Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoring'. Together they form a unique fingerprint.

Cite this