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        Chapter The Use of Electrical Measurements of Wind Turbine Generators for Drive Train Condition Monitoring

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        Author(s)
        Artigao, Estefania
        Gómez-Lázaro, Emilio
        Martín-Martínez, Sergio
        Honrubia-Escribano, Andrés
        Language
        English
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        Abstract
        More modern and larger wind turbine (WT) generators are under continuous development. These exhibit more faults than smaller ones, which becomes critical offshore. Under this framework, operation and maintenance (O&M) is the key to improve reliability and availability of WTs, where condition-based maintenance (CBM) is currently seen as the preferred approach by the early detection and diagnosis of critical faults for WTs. The induction generator is one of the biggest contributors to failure rates and downtime of WTs, together with the gearbox and the drive train. In the present chapter, current signature analysis (CSA) will be introduced as a means for fault detection of WTs. CSA is a cost-effective and nonintrusive technique that can monitor both mechanical and electrical faults within the induction generator, as well as bearing- and gearbox-related faults. Different test cases of in-service wind turbine generators will be used to illustrate its usefulness.
        URI
        https://library.oapen.org/handle/20.500.12657/49346
        Keywords
        condition monitoring, current signature analysis, DFIG, wind turbine, operation and maintenance, predictive maintenance, drive train
        DOI
        10.5772/intechopen.90127
        Publisher
        InTechOpen
        Publisher website
        https://www.intechopen.com/
        Publication date and place
        2020
        Classification
        Engineering: general
        Rights
        https://creativecommons.org/licenses/by/3.0/
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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