Data-Driven Wind Turbine Power Generation Performance Monitoring

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

36 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number7128721
Pages (from-to)6627-6635
Journal / PublicationIEEE Transactions on Industrial Electronics
Volume62
Issue number10
Online published9 Jun 2015
Publication statusPublished - Oct 2015

Abstract

This paper investigates the wind turbine power generation performance monitoring based on supervisory control and data acquisition (SCADA) data. The proposed approach identifies turbines with weakened power generation performance through assessing the wind power curve profiles. Profiles that statistically summarize the curvatures and shapes of a wind power curve over consecutive time intervals are constructed by fitting power curve models into SCADA data sets with a least square method. To monitor the variations of wind power curve profiles over time, multivariate and residual approaches are introduced and applied. Two blind industrial studies are conducted to validate the effectiveness of the proposed monitoring approach, and the results demonstrate high accuracy in detecting the abnormal power curve profiles of wind turbines and their associated time intervals.

Research Area(s)

  • multivariate approach, Performance monitoring, power curve, residual analysis, wind energy