Big data techniques for wind turbine condition monitoring

David Ferguson, Victoria Catterson

Research output: Contribution to conferencePaper

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Abstract

The continual development of sensor and storage technology has led to a dramatic increase in volumes of data being captured for condition monitoring and machine health assessment. Beyond wind energy, many sectors are dealing with the same issue, and these large, complex data sets have been termed ‘Big Data’. Big Data may be defined as having three dimensions: volume, velocity, and variety. This paper discusses the application of Big Data practices for use in wind turbine condition monitoring, with reference to a deployed system capturing 2 TB of data per month.
Original languageEnglish
Publication statusAccepted/In press - Mar 2014
EventEuropean Wind Energy Association Annual Event (EWEA 2014) - Barcelona, Spain
Duration: 10 Mar 201413 Mar 2014

Conference

ConferenceEuropean Wind Energy Association Annual Event (EWEA 2014)
CountrySpain
CityBarcelona
Period10/03/1413/03/14

Fingerprint

Condition monitoring
Wind turbines
Wind power
Health
Sensors
Big data

Keywords

  • wind energy
  • wind turbine
  • turbine condition monitoring
  • big data

Cite this

Ferguson, D., & Catterson, V. (Accepted/In press). Big data techniques for wind turbine condition monitoring. Paper presented at European Wind Energy Association Annual Event (EWEA 2014), Barcelona, Spain.
Ferguson, David ; Catterson, Victoria. / Big data techniques for wind turbine condition monitoring. Paper presented at European Wind Energy Association Annual Event (EWEA 2014), Barcelona, Spain.
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Ferguson, D & Catterson, V 2014, 'Big data techniques for wind turbine condition monitoring' Paper presented at European Wind Energy Association Annual Event (EWEA 2014), Barcelona, Spain, 10/03/14 - 13/03/14, .

Big data techniques for wind turbine condition monitoring. / Ferguson, David; Catterson, Victoria.

2014. Paper presented at European Wind Energy Association Annual Event (EWEA 2014), Barcelona, Spain.

Research output: Contribution to conferencePaper

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AU - Catterson, Victoria

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KW - wind turbine

KW - turbine condition monitoring

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M3 - Paper

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Ferguson D, Catterson V. Big data techniques for wind turbine condition monitoring. 2014. Paper presented at European Wind Energy Association Annual Event (EWEA 2014), Barcelona, Spain.