Monitoring Wind Farms With Performance Curves

Andrew Kusiak; Anoop Verma

doi:10.1109/TSTE.2012.2212470

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Monitoring Wind Farms With Performance Curves

Journal article

Peer reviewed

Monitoring Wind Farms With Performance Curves

Andrew Kusiak and Anoop Verma

IEEE transactions on sustainable energy, Vol.4(1), pp.192-199

01/2013

DOI: 10.1109/TSTE.2012.2212470

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Abstract

Three different operational curves-the power curve, rotor curve, and blade pitch curve-are presented for monitoring a wind farm's performance. A five-year historical data set has been assembled for constructing the reference curves of wind power, rotor speed, and blade pitch angle, with wind speed as an input variable. A multivariate outlier detection approach based on k -means clustering and Mahalanobis distance is applied to this data to produce a data set for modeling turbines. Kurtosis and skewness of bivariate data are used as metrics to assess the performance of the wind turbines. Performance monitoring of wind turbines is accomplished with the Hotelling T 2 control chart.

turbine performance curves

Control chart

Wind speed

Blades

performance monitoring

k -means clustering

Rotors

Wind farms

Wind turbines

Data mining

Monitoring

Mahalanobis distance

Details

Title: Subtitle: Monitoring Wind Farms With Performance Curves
Creators: Andrew Kusiak - Intelligent Systems Laboratory, The University of Iowa, Iowa City, IA, USA
Anoop Verma - Intelligent Systems Laboratory, The University of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: IEEE transactions on sustainable energy, Vol.4(1), pp.192-199
Publisher: IEEE
DOI: 10.1109/TSTE.2012.2212470
ISSN: 1949-3029
eISSN: 1949-3037
Language: English
Date published: 01/2013
Academic Unit: Industrial and Systems Engineering; Nursing
Record Identifier: 9984064586202771

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