Browsing by Author "Devenish, A."

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Data Analytics for predictive maintenance of Wind Turbines.
    (Stellenbosch : Stellenbosch University, 2021-12) Devenish, A.; Basson, A. H.; Kruger, K.; Grobler, J.; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.
    ENGLISH ABSTRACT: There is a global drive for using greener energy sources for power generation, for which wind energy is a popular alternative. Significant investment is thus attracted to wind farm development. However, research into wind farm management is required to improve the economic viability of wind turbines, and maintain wind energy as a competitive source for electricity generation. Elements of wind farm management that contribute significantly to the overall costs of operating wind farms, are maintenance and repair operations. Maintenance and repair operations constitute 20-25% of the total levelised cost of wind turbines. Decreasing these costs would thus greatly contribute to improving the operating costs of wind farms. The research in this thesis investigates the detection of wind turbine failures to aid the contribution of preventative measures that can be taken to decrease the total levelised cost of wind turbines. The objective of this thesis is to evaluate the use of data analytics for predictive maintenance of wind turbines. Therefore, the thesis presents a review of predictive maintenance solutions found in literature, as well as a case study which demonstrates the use of various data analytic techniques for predictive maintenance of wind turbines. In the case study, various machine learning algorithms are evaluated for predicting failures in wind turbines by two broad approaches - classification and regression. The classification approaches predict whether a failure will occur in a certain time period, while regression approaches estimate the time until a failure occurs. The best performing algorithm for classification in the case study was the support vector machine (SVM), and for regression the random forest. The classification and regression models were evaluated using recall and precision, and root mean squared error (RMSE), respectively. Binary and multiclass experiments were performed for both classification and regression approaches. The binary predictions provided failure warnings without an indication of which component was going to fail, whereas the multiclass experiments made separate failure predictions for each component category. A conclusion drawn from the multiclass predictions is that the failure categories have different predictabilities, as some showed to be more successfully predicted than others. Knowing which component is going to fail is valuable to wind farm operators because they can immediately focus on implementing preventative measures for that specific problem in the turbine. The time required to locate the cause of the failure warning, inspect and fix what is faulty, and return the turbine to normal operation again, will therefore be reduced. From the outcomes of this thesis, it is concluded that for the dataset used in this research, regression is the most reliable approach for predicting failures in wind turbines. Even though the multiclass cases' data was more severely imbalanced than the binary cases, the multiclass failure predictions were better for some components than the binary case predictions: the binary case's RMSE was 30.9 hr, while the multiclass errors ranged from 10.2 hr for transformer failures to 21.4 hr for hydraulic group failures.