Prediction of wind turbine generator failure using two‐stage cluster‐classification methodology. Issue 11 (7th August 2019)

• Record Type: Journal Article
• Authors: Turnbull, Alan; Carroll, James; McDonald, Alasdair; Koukoura, Sofia
• Is Part Of: Wind energy. Volume 22:Issue 11(2019)
• Page Start: 1593
• Page End: 1602
• Publication Date: 2019-08-07
• Subjects: bearing -- condition monitoring -- failure -- vibration machine learning -- wind turbine; Wind power -- Periodicals; 621.312136
• DOI: 10.1002/we.2391
• Languages: English
• ISSNs: 1095-4244

Abstract

Abstract: Reducing wind turbine downtime through innovations surrounding asset management has the potential to greatly influence the levelised cost of energy (LCoE) for large wind farm developments. Focusing on generator bearing failure and vibration data, this paper presents a two‐stage methodology to predict failure within 1 to 2 months of occurrence. Results are obtained by building up a database of failures and training machine learning algorithms to classify the bearing as healthy or unhealthy. This is achieved by first using clustering techniques to produce subpopulations of data based on operating conditions, which this paper demonstrates can greatly influence the ability to diagnose a fault. Secondly, this work classifies individual clusters as healthy or unhealthy from vibration‐based condition monitoring systems by applying order analysis techniques to extract features. Using the methodology explained in the report, an accuracy of up to 81.6% correct failure prediction was achieved.