Big data techniques for wind turbine condition monitoring

David Ferguson; Victoria Catterson

Big data techniques for wind turbine condition monitoring

David Ferguson, Victoria Catterson

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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 language	English
Publication status	Published - 2014
Event	European Wind Energy Association Annual Event (EWEA 2014) - Barcelona, Spain Duration: 10 Mar 2014 → 13 Mar 2014

Conference

Conference	European Wind Energy Association Annual Event (EWEA 2014)
Country/Territory	Spain
City	Barcelona
Period	10/03/14 → 13/03/14

Keywords

wind energy
wind turbine
turbine condition monitoring
big data

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Big Data Techniques for Wind Turbine Condition MonitoringAccepted author manuscript, 381 KB

Cite this

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title = "Big data techniques for wind turbine condition monitoring",

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 {\textquoteleft}Big Data{\textquoteright}. 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.",

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T1 - Big data techniques for wind turbine condition monitoring

AU - Ferguson, David

AU - Catterson, Victoria

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - wind energy

KW - wind turbine

KW - turbine condition monitoring

KW - big data

UR - http://www.ewea.org/annual2014/

M3 - Paper

T2 - European Wind Energy Association Annual Event (EWEA 2014)

Y2 - 10 March 2014 through 13 March 2014

ER -

Big data techniques for wind turbine condition monitoring

Abstract

Conference

Keywords

Access to Document

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