Wind turbine operation anomaly detection using copula statistics

Research output: Contribution to conferencePaper

Abstract

The conventional means of assessing the performance of a wind turbine is through consideration of its power curve which provides the relationship between its active power outputs and a measured wind speed. An alternative representation can be obtained via the probability density estimation of the joint probability of wind speed and active power. A probabilistic model has several capabilities that could not be achieved with a simple curve fitting analysis, such as anomaly detection, which in practice may be related to the presence of an incipient problem; and model comparison which could allow the monitoring of the model evolution both over time and with respect to those of other turbines in a fleet. However, the probability density takes on a complex form which most probability distributions are incapable of representing. This paper illustrates the shortcomings of orthodox statistical approaches to probabilistic modelling of the power curve and shows how Copula statistics, traditionally used in finance models, offer a way of describing how to relate variables with a complex dependency structure in wind turbine condition monitoring.
LanguageEnglish
Publication statusPublished - 4 Feb 2013
EventEWEA Annual Wind Energy Event 2013 - Vienna, Austria
Duration: 4 Feb 20137 Feb 2013

Conference

ConferenceEWEA Annual Wind Energy Event 2013
CountryAustria
CityVienna
Period4/02/137/02/13

Fingerprint

Wind turbines
Statistics
Condition monitoring
Curve fitting
Finance
Probability distributions
Turbines
Monitoring
Statistical Models

Keywords

  • copula statistics
  • anomaly detection
  • wind
  • modelling

Cite this

Stephen, B., Gill, S., Galloway, S., Wang, Y., McMillan, D., & Infield, D. (2013). Wind turbine operation anomaly detection using copula statistics. Paper presented at EWEA Annual Wind Energy Event 2013, Vienna, Austria.
Stephen, Bruce ; Gill, Simon ; Galloway, Stuart ; Wang, Yue ; McMillan, David ; Infield, David. / Wind turbine operation anomaly detection using copula statistics. Paper presented at EWEA Annual Wind Energy Event 2013, Vienna, Austria.
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Stephen, B, Gill, S, Galloway, S, Wang, Y, McMillan, D & Infield, D 2013, 'Wind turbine operation anomaly detection using copula statistics' Paper presented at EWEA Annual Wind Energy Event 2013, Vienna, Austria, 4/02/13 - 7/02/13, .

Wind turbine operation anomaly detection using copula statistics. / Stephen, Bruce; Gill, Simon; Galloway, Stuart; Wang, Yue; McMillan, David; Infield, David.

2013. Paper presented at EWEA Annual Wind Energy Event 2013, Vienna, Austria.

Research output: Contribution to conferencePaper

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AU - Galloway, Stuart

AU - Wang, Yue

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AU - Infield, David

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AB - The conventional means of assessing the performance of a wind turbine is through consideration of its power curve which provides the relationship between its active power outputs and a measured wind speed. An alternative representation can be obtained via the probability density estimation of the joint probability of wind speed and active power. A probabilistic model has several capabilities that could not be achieved with a simple curve fitting analysis, such as anomaly detection, which in practice may be related to the presence of an incipient problem; and model comparison which could allow the monitoring of the model evolution both over time and with respect to those of other turbines in a fleet. However, the probability density takes on a complex form which most probability distributions are incapable of representing. This paper illustrates the shortcomings of orthodox statistical approaches to probabilistic modelling of the power curve and shows how Copula statistics, traditionally used in finance models, offer a way of describing how to relate variables with a complex dependency structure in wind turbine condition monitoring.

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Stephen B, Gill S, Galloway S, Wang Y, McMillan D, Infield D. Wind turbine operation anomaly detection using copula statistics. 2013. Paper presented at EWEA Annual Wind Energy Event 2013, Vienna, Austria.