Machine learning in wind turbine O&M

Tools

Mckinnon, Conor and Carroll, James and McDonald, Alasdair and Koukoura, Sofia (2019) Machine learning in wind turbine O&M. In: Future Wind and Marine, 2019-03-07, University of Strathclyde.

[thumbnail of McKinnon-etal-FWM-2019-Machine-learning-in-wind-turbine-O-and-M]
Preview
 Text. Filename: McKinnon_etal_FWM_2019_Machine_learning_in_wind_turbine_O_and_M.pdf
Final Published Version
Download (747kB)| Preview

Abstract

This research will investigate the use of Machine Learning techniques in various applications within the field of Wind Energy The general approach to Machine Learning follows the steps shown on the right Model selection is done through literature review, which depends on the data used This data is then processed and cleaned, through clustering and removal of outliers Features are extracted from the data, either from univariate statistics to find the feature with the least variance from the target, or PCA to reduce the number of features to two abstract features with no physical meaning This data is then used to train and test the model(s) The results produced are then analysed, either using existing alarm data, or through k folds cross validation These results can also inform on model selection

ORCID iDs

Mckinnon, Conor, Carroll, James ORCID logoORCID: https://orcid.org/0000-0002-1510-1416, McDonald, Alasdair ORCID logoORCID: https://orcid.org/0000-0002-2238-3589 and Koukoura, Sofia;
CORE (COnnecting REpositories)