Data-Driven Multimode Fault Detection for Wind Energy Conversion Systems. Issue 21 (2015)

• Record Type: Journal Article
• Authors: Haghani, Adel; Krueger, Minjia; Jeinsch, Torsten; Ding, Steven X.; Engel, Peter
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 21(2015)
• Page Start: 633
• Page End: 638
• Publication Date: 2015
• Subjects: Wind energy conversion systems -- Fault detection -- Data-driven -- Multimode approaches; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.09.597
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper a data-driven fault detection scheme for wind energy conversion system is proposed. The method uses the offline measurements of the wind turbine in a wide range of operating points and builds a monitoring system which is able to detect faults with higher detection rate compared to the classical data-driven techniques. The nonlinear characteristics of wind turbine are approximated by multiple piece-wise linear systems and hence the monitoring scheme is robust against model uncertainties which may arise due to nonlinearity of the system. Moreover, the proposed method is able to discriminate the faults which are affecting the power production in the wind energy system. The effectiveness of the monitoring scheme is demonstrated using the data collected from different 2MW wind turbines. The superior performance of the proposed method is further compared with the classical data-driven methods and the results are discussed.