Simulation of wind turbine faulty production profiles and performance assessment of fault monitoring methods



Published Jun 30, 2018
Usama Aziz Sylvie Charbonnier Christophe Bérenguer Alexis Lebranchu Frederic Prevost


Wind turbines being one of the fastest growing sources of renewable energy have garnered significant scientific interest for the monitoring and fault analysis using SCADA data. Various monitoring approaches using power curves, i.e. industry wide characteristic curves expressing produced power as a function of wind speed, have been proposed in the literature. However, an objective comparison of the performance of these methods is difficult. The difficulty comes from (i) the variability in operational and environmental conditions taken into account; (ii) the nature, size and type of data-sets used and (iii) the type and signatures of faults considered for validation.  To solve this problem, an approach with a twofold contribution is proposed in this work: 1) an original procedure to generate realistic and controlled simulations of 10 minutes SCADA data, simulating situations when the wind turbine is operating in normal or faulty conditions, is presented; 2) a framework for objective performance assessment of the fault detection methods, based on the proposed controlled and standardized simulation scheme is presented. Objective performance evaluation metrics, such as detection probability and false alarm rates are computed and represented as characteristic Receiver Operating Curves (ROC). The proposed simulation approach is shown to provide a useful global framework for objective performance analysis. A number of realistically simulated and controlled data streams are used to compare and discuss the performances of two fault detection methods referenced in the literature.

How to Cite

Aziz, U., Charbonnier, S., Bérenguer, C., Lebranchu, A., & Prevost, F. (2018). Simulation of wind turbine faulty production profiles and performance assessment of fault monitoring methods. PHM Society European Conference, 4(1).
Abstract 406 | PDF Downloads 400



Data-driven methods for fault detection, diagnosis, and prognosis, Industrial applications, Modelling and Simulation, Wind Turbine Monitoring

Technical Papers