NDLI: Monitoring wind turbine condition by the approach of Empirical Mode Decomposition

Content Provider	IEEE Xplore Digital Library
Author	Wenxian Yang Jiesheng Jiang Tavner, P.J. Crabtree, C.J.
Copyright Year	2008
Description	Author affiliation: New & Renewable Energy Group, Durham Univ., Durham (Tavner, P.J.; Crabtree, C.J.) \|\| Instn. of Vibration Eng., Northwestern Polytech. Univ., Xi'an (Wenxian Yang; Jiesheng Jiang)
Abstract	An efficient condition monitoring system is indispensable to a large offshore wind turbine (WT) as it suffers higher reliability risk being exposed to extreme running environment and subject to constantly variable loadings, however difficult to access for fault repair. Today, the majority condition monitoring techniques for WT are borrowed from other industry fields where they achieve success. However, to date these techniques have not proved entirely satisfactory in wind industry. The reasons are various. But one of the main reasons is lack of a proper approach to the accurate analysis of WT signals, which are non-stationary in both time and frequency. The inaccurate analysis of WT signals results in frequent spurious alarms, which cause unnecessary shut down of machines and seriously disturb the normal production of wind farms. Aim at improving this situation, a new technique is developed in this work through analyzing the total power signals measured from the terminals of the WT generator by using the approach of empirical mode decomposition (EMD). In comparison with those conventional Fourier transform-based techniques that are being popularly used today in wind industry, the EMD is more ideal for processing the non-stationary, nonlinear WT signals attribute to its intrinsic locally adaptive property. Additionally, the computational algorithm of the EMD is more efficient than that of previous wavelet analysis, which enables the EMD more suitable for use in online condition monitoring systems. The proposed approach has been experimentally validated on a deliberately designed WT test rig with a 3-phase induction generator. It has been proved that the proposed strategy is valid for detecting both drive train mechanical and generator electrical faults occurring in all types of WTs whether geared or direct-drive.
Starting Page	736
Ending Page	740
File Size	607135
Page Count	5
File Format	PDF
ISBN	9781424438266
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	IEEE Consumer Electronics Society(CES)
Subject Keyword	Condition monitoring Wind turbines Signal analysis Induction generators Frequency Production Wind farms Power measurement Power generation Signal generators
Content Type	Text
Resource Type	Article

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