A NBM based on P-N relationship for DFIG wind turbine fault detection

Ran Bi, C. Zhou, D. M. Hepburn, Jin Rong

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

3 Citations (Scopus)

Abstract

Supervisory control and data acquisition (SCADA) data has been widely applied to identify abnormal conditions in wind turbine generators (WTG). One approach was to apply Artificial Intelligence (AI) to SCADA data, comparing the predicted power output of a WTG and its actual output and using the prediction error as an indicator to detect faults. However, complicated training processes limit its application. This paper presents a normal behavior model (NBM), based on power output-generator speed (P-N) curve, to analyze SCADA data from modern pitch regulated WTGs for detecting anomalies. Through analysis of the operational characteristics of the pitch regulated WTG, it is found that inaccuracies in wind speed measurement, the inertia of the rotor, yaw and pitch misalignments, and air density fluctuation may affect the performance of the power curve monitoring algorithms. This paper shows that under normal conditions the P-N curve based NBM performs better when fitting the SCADA data to WTG output under normal conditions than the power curve. Results demonstrate that it can give alarm to forthcoming faults earlier than the existing condition monitoring system (CMS).

Original languageEnglish
Title of host publicationProceedings: 2015 International Conference on Smart Grid and Clean Energy Technologies: ICSGCE 2015
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9781467387347
ISBN (Print)9781467387323
DOIs
Publication statusPublished - 15 Apr 2016
Event2015 International Conference on Smart Grid and Clean Energy Technologies - Offenburg, Germany
Duration: 20 Oct 201523 Oct 2015

Conference

Conference2015 International Conference on Smart Grid and Clean Energy Technologies
Abbreviated title ICSGCE 2015
Country/TerritoryGermany
CityOffenburg
Period20/10/1523/10/15

Keywords

  • doubly fed induction generator (DFIG)
  • fault detection
  • normal behavior model (NBM)
  • wind turbine generator (WTG)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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