NDLI: Internal leakage detection for wind turbine hydraulic pitching system with computationally efficient adaptive asymmetric SVM

Content Provider	IEEE Xplore Digital Library
Author	Xin Wu Rui Su Congfei Lu Xiaoming Rui
Copyright Year	2015
Description	Author affiliation: Sch. of Energy, Power & Mech. Eng., North China Electr. Power Univ., Beijing, China (Xin Wu; Rui Su; Congfei Lu; Xiaoming Rui)
Abstract	This paper investigate an asymmetric support vector machine approach for wind turbine hydraulic pitch systems. Hydraulic pitching system in the wind turbine is critical for energy capture, load reduction and aerodynamic braking. Its reliability and maintenance is thus of high priority. The fault of cylinder internal leakage is studied in this paper. The fault and not-fault conditions for the internal leakage in the hydraulic system are classified through the self-learning asymmetric support vector machine (ASVM) algorithm, which can maintain the complexity of the fault model. The improved ASVM algorithm can adaptively select the minimal number of support vectors while maintaining the desired classification performance, which makes the practical implementation of the classifier computationally more efficient. The proposed method is verified through the simulation study based on the aerodynamic loading on the pitching axis under smooth and turbulent wind profiles obtained from the simulation of a 1.5 MW variable-speed turbine model on the FAST (Fatigue, Aerodynamics, Structural and Tower) software developed by the National Renewable Energy Laboratory (NREL).
Starting Page	6126
Ending Page	6130
File Size	373186
Page Count	5
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260599
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Training Computational modeling Internal leakage Support vector machine classification Asymmetric support vector machine Hydraulic systems Efficient support vectors classification Wind turbines Wind turbine Load modeling
Content Type	Text
Resource Type	Article

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