Projects per year
Reducing wind turbine downtime through innovations surrounding asset management has the potential to greatly influence the overall levelised cost of energy of large onshore and offshore developments. This research paper uses multiple examples of the same generator bearing failure to provide insight into how condition monitoring systems can be used in to train machine learning algorithms with the ultimate goal of predicting failure and remaining useful life. Results show that by analysing high frequency vibration data and extracting key features to train support vector machine algorithms, an accuracy of 67% can be achieved in successfully predicting failure 1-2 months before occurrence. This paper reflects on the limitations surrounding a generalised training approach, taking advantage of all available data, showing that if too many different examples are considered of different wind turbines and operating conditions the overall accuracy can be diminished.
|Number of pages||6|
|Publication status||Published - 28 Sep 2018|
|Event||The 7th International Conference on Renewable Power Generation : The 7th International Conference on Renewable Power Generation - DTU, Lyngby, Copenhagen, Denmark|
Duration: 26 Aug 2018 → 27 Sep 2018
Conference number: 7
|Conference||The 7th International Conference on Renewable Power Generation|
|Period||26/08/18 → 27/09/18|
- wind turbine
- condition monitoring
1/10/16 → 1/10/20
Project: Research Studentship - Internally Allocated
Turnbull, A., Carroll, J., Koukoura, S., & McDonald, A. (2018). Prediction of wind turbine generator bearing failure through analysis of high frequency vibration data and the application of support vector machine algorithms. Paper presented at The 7th International Conference on Renewable Power Generation , Copenhagen, Denmark.