A radically data-driven method for fault detection and diagnosis in wind turbines. (July 2018)

• Record Type: Journal Article
• Authors: Yu, D.; Chen, Z.M.; Xiahou, K.S.; Li, M.S.; Ji, T.Y.; Wu, Q.H.
• Is Part Of: International journal of electrical power & energy systems. Volume 99(2018)
• Page Start: 577
• Page End: 584
• Publication Date: 2018-07
• Subjects: Wind turbine -- Fault detection and diagnosis -- Deep belief network -- Data-driven; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2018.01.009
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Radically data-driven method of fault diagnosis for the wind turbines. Nine types of fault scenarios are employed to test the proposed algorithm. FDI shows strong robustness, wide practicability and high reliability in experiment. Abstract: In order to improve the reliability of wind turbines, avoid serious accidents and reduce operation and maintenance (O&M) costs, it is important to effectively detect faults of wind turbines operating in harsh environment. This paper proposes a radically data-driven fault detection and diagnosis (FDD) method for wind turbines, which implements deep belief network (DBN). The DBN requires no knowledge of physical model, instead, it employs historical data without any pre-selection. The method has been evaluated in a wind turbine benchmark simulink model, in comparison with four model-based algorithms and four data-driven methods, and the results have shown that the proposed method achieves the highest accuracy. Moreover, extensive evaluation has been taken to analyse the robustness of proposed method, and the simulation results indicate the stable performance of proposed method in faults diagnosis of wind turbine.