Wind Turbine Performance Analysis based on Multivariate Higher Order Moments and Bayesian Classifiers

Jürgen Herp, Niels Lovmand Pedersen, Esmaeil S. Nadimi

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

A data-driven model based on Bayesian classifiers and multivariate analysis of the power curve (wind speed vs. power) for monitoring wind farms' performance is presented. A new outlier detection criterion and various control bounds on the skewness and kurtosis of the data for cluster separation and classification of turbines' faulty and normal state of operation are introduced. Further continuous monitoring is addressed with Hotelling's T 2 and Bayesian network approaches, and it is proven that under certain conditions, the outcomes of these two methods are equivalent. The Bayesian approach, however addresses the likelihood of classification, making supervised controls more flexible.

Original languageEnglish
JournalControl Engineering Practice
Volume49
Pages (from-to)204-211
ISSN0967-0661
DOIs
Publication statusPublished - 2016

Keywords

  • Bayesian classification
  • Condition monitoring
  • K-means clustering
  • Multivariate analysis
  • Wind farm

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