Big data techniques for wind turbine condition monitoring

Ferguson, David and Catterson, Victoria (2014) Big data techniques for wind turbine condition monitoring. In: European Wind Energy Association Annual Event (EWEA 2014), 2014-03-10 - 2014-03-13.

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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.

ORCID iDs

Ferguson, David ORCID logoORCID: https://orcid.org/0000-0002-4911-782X and Catterson, Victoria ORCID logoORCID: https://orcid.org/0000-0003-3455-803X;
CORE (COnnecting REpositories)