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Data-Driven Wind Turbine Power Generation Performance Assessment Using NI LabVIEW’s Watchdog® Agent Toolkit

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Advances in Condition Monitoring of Machinery in Non-Stationary Operations

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Power generation performance is a fundamental metric that all wind farm operators use to determine whether expected power throughput is actually being met. IEC 61400-12-1 has been drafted as an exhaustive power performance measurement scheme for wind turbines. The primary weakness of such a standard is the required level of depth of the associated performance tests, which is more than sufficient for operators to use to run daily wind farm activities. In addition, since this IEC test is not really meant for frequent evaluation, it also fails to capture any loss in power generation performance over time. This paper addresses the aforementioned weaknesses of the IEC standard by the application of data-driven approach to model a wind turbine’s power curve. A set of measurements during a known good condition is utilized to setup a baseline model. Regular power curve measurements are then compared while taking into account the multi-regime dynamics of the turbine. The approach was implemented using NI LabVIEW’s Watchdog Agent® Toolkit and was successfully validated using actual SCADA data collected from an on-shore wind turbine.

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Author information

Authors and Affiliations

  1. National Instruments, Italy, Centro Dir.Le Milanofiori Nord, Via Del Bosco Rinnovato, 8, 20090, Assago, MI, Italy

    Lodovico Menozzi & Edzel Lapira

  2. Center for Intelligent Maintenance Systems at University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH, 45221-0072, USA

    Wenyu Zhao

Authors
  1. Lodovico Menozzi
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  2. Wenyu Zhao
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  3. Edzel Lapira
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Corresponding author

Correspondence to Lodovico Menozzi .

Editor information

Editors and Affiliations

  1. Engineering Department, University of Ferrara, Ferrara, Italy

    Giorgio Dalpiaz

  2. Department of Science and Engineering Methods, University of Modena and Reggio Emilia, Reggio Emilia, Italy

    Riccardo Rubini

  3. Engineering Department, University of Ferrara, Ferrara, Italy

    Gianluca D'Elia

  4. Department of Science and Engineering Methods, University of Modena and Reggio Emilia, Reggio Emilia, Italy

    Marco Cocconcelli

  5. Mechanical Engineering Department, National School of Engineers of Sfax, Sfax, Tunisia

    Fakher Chaari

  6. Institute of Mining Engineering, Diagnostics and Vibro-Acoustics Science Laboratory, Wrocław University of Technology, Wrocław, Poland

    Radoslaw Zimroz

  7. Institute of Mining Engineering, Diagnostics and Vibro-Acoustics Science Laboratory, Wrocław University of Technology, Wrocław, Poland

    Walter Bartelmus

  8. Mechanical Engineering Department, National School of Engineers of Sfax, Sfax, Tunisia

    Mohamed Haddar

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© 2014 Springer-Verlag Berlin Heidelberg

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Menozzi, L., Zhao, W., Lapira, E. (2014). Data-Driven Wind Turbine Power Generation Performance Assessment Using NI LabVIEW’s Watchdog® Agent Toolkit. In: Dalpiaz, G., et al. Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Lecture Notes in Mechanical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39348-8_57

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  • DOI: https://doi.org/10.1007/978-3-642-39348-8_57

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39347-1

  • Online ISBN: 978-3-642-39348-8

  • eBook Packages: EngineeringEngineering (R0)

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