Wind turbine generator condition-monitoring using temperature trend analysis

Peng Guo, David Infield, Xiyun Yang

Research output: Contribution to journalArticle

77 Citations (Scopus)

Abstract

Condition monitoring can greatly reduce the maintenance cost for a wind turbine. In this paper, a new condition-monitoring method based on the nonlinear state estimate technique for a wind turbine generator is proposed. The technique is used to construct the normal behavior model of the electrical generator temperature. A new and improved memory matrix construction method is adopted to achieve better coverage of the generator's normal operational space. Generator incipient failure is indicated when the residuals between model estimates and the measured generator temperature become significant. Moving window averaging is used to detect statistically significant changes of the residual mean value and standard deviation in an effective manner; when these parameters exceed predefined thresholds, an incipient failure is flagged. Examples based on data from the Supervisory Control and Data Acquisition system at a wind farm located at Zhangjiakou in northern China have been used to validate the approach and examine its sensitivity to key factors that influence the performance of the approach. It is demonstrated that the technique can identify dangerous generator over temperature before damage has occurred that results in complete shutdown of the turbine.
LanguageEnglish
Pages124-133
Number of pages10
JournalIEEE Transactions on Sustainable Energy
Volume3
Issue number1
Early online date1 Aug 2011
DOIs
Publication statusPublished - 31 Jan 2012

Fingerprint

Turbogenerators
Condition monitoring
Wind turbines
SCADA systems
Temperature
Farms
Turbines
Data storage equipment
Costs

Keywords

  • condition monitoring
  • cost reduction
  • generator temperature measurement
  • wind power plants
  • wind turbines
  • turbogenerators

Cite this

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title = "Wind turbine generator condition-monitoring using temperature trend analysis",
abstract = "Condition monitoring can greatly reduce the maintenance cost for a wind turbine. In this paper, a new condition-monitoring method based on the nonlinear state estimate technique for a wind turbine generator is proposed. The technique is used to construct the normal behavior model of the electrical generator temperature. A new and improved memory matrix construction method is adopted to achieve better coverage of the generator's normal operational space. Generator incipient failure is indicated when the residuals between model estimates and the measured generator temperature become significant. Moving window averaging is used to detect statistically significant changes of the residual mean value and standard deviation in an effective manner; when these parameters exceed predefined thresholds, an incipient failure is flagged. Examples based on data from the Supervisory Control and Data Acquisition system at a wind farm located at Zhangjiakou in northern China have been used to validate the approach and examine its sensitivity to key factors that influence the performance of the approach. It is demonstrated that the technique can identify dangerous generator over temperature before damage has occurred that results in complete shutdown of the turbine.",
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Wind turbine generator condition-monitoring using temperature trend analysis. / Guo, Peng; Infield, David; Yang, Xiyun.

In: IEEE Transactions on Sustainable Energy, Vol. 3, No. 1, 31.01.2012, p. 124-133.

Research output: Contribution to journalArticle

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