Pitch fault diagnosis of wind turbines in multiple operational states using supervisory control and data acquisition data. Issue 5 (October 2019)

• Record Type: Journal Article
• Authors: Wei, Lu; Qian, Zheng; Yang, Cong; Pei, Yan
• Is Part Of: Wind engineering. Volume 43:Issue 5(2019)
• Page Start: 443
• Page End: 458
• Publication Date: 2019-10
• Subjects: Fault diagnosis -- wind turbine -- pitch system -- multiple operational states -- Gaussian mixture model clustering; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X18791407
• Languages: English
• ISSNs: 0309-524X

Abstract

Supervisory control and data acquisition data including comprehensive signal information have been widely applied to fault diagnosis. However, because of the complex operational condition of wind turbines, supervisory control and data acquisition data become complicated and abstract to study. This article proposes a pitch fault diagnosis method of wind turbines in multiple operational states using supervisory control and data acquisition data. According to the performance of characteristic parameters in nine operational states of wind turbines, Gaussian mixture model clustering and the analysis of normal performance curves are applied to model the relationship of pitch angle, rotor speed, and wind speed. Four cases have been studied to demonstrate the feasibility of the proposed method. The advantages of the proposed approach are as follows: (1) simplifying the analysis of supervisory control and data acquisition data through dividing the data into nine parts; (2) detecting pitch faults earlier than supervisory control and data acquisition monitoring system; (3) visualizing the abnormal behavior of the pitch system; and (4) improving the interpretability of the method with the incorporation of domain knowledge.