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

Usama Aziz (1, 2) , Sylvie Charbonnier (2) , Christophe Bérenguer (2) , Alexis Lebranchu (1) , Frédéric Prevost (1)

1 Valemo S.A.S
2 GIPSA-SAIGA - GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique

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 (supervisory control and data acquisition) 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.

Mots clés: Data-driven methods for fault detection; Diagnosis; Industrial applications; Modelling and Simulation; Wind Turbine Monitoring

Domaines: Automatique / Robotique; Applications [stat.AP]

Usama Aziz, Sylvie Charbonnier, Christophe Bérenguer, Alexis Lebranchu, Frédéric Prevost. Simulation of wind turbine faulty production profiles and performance assessment of fault monitoring methods. PHME 2018 - 4th European Conference of the Prognostics and Health Management Society, PHM Society, Jul 2018, Utrecht, Netherlands. Paper 378, 8pp. ⟨hal-01887418⟩