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Vertical Axis Wind Turbine States Classification by an ART-2 Neural Network with a Stereographic Projection as a Signal Normalization

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Advances in Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO 2014)

Part of the book series: Applied Condition Monitoring ((ACM,volume 4))

Abstract

A common technique used to decrease a cost of wind turbine maintenance is a remote monitoring. Apart from the development of several advanced diagnostic methods for wind turbines there is a need to prepare an early warning tool which would work continuously in real-time and focus the attention on potentially dangerous cases. A research using the resonance neural networks made so far by the authors gave positive results. Systems based on the ART-2 networks were able to perform a classification of operational states of a horizontal axis wind turbine. In this paper the innovative idea of using the ART-2 network is applied to data from vertical axis wind turbines. The system, which were composed by ART-2 and new signal normalization procedures based on a stereographic projection, was implemented and tested. Simulations of a system operation showed that it is capable to perform an efficient state classification.

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Acknowledgments

The paper was supported by the National Centre for Research and Development under grant no. WND-DEM-1-153/01

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Authors and Affiliations

  1. Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Cracow, Poland

    Tomasz Barszcz

  2. Faculty of Electrical Engineering, Automation, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Cracow, Poland

    Andrzej Bielecki & Mateusz Wójcik

  3. Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Cracow, Poland

    Marzena Bielecka

  4. Anew Institute sp. z o.o, Cracow, Poland

    Mirosław Włuka

Authors
  1. Tomasz Barszcz
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  2. Andrzej Bielecki
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  3. Marzena Bielecka
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  4. Mateusz Wójcik
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  5. Mirosław Włuka
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Corresponding author

Correspondence to Mateusz Wójcik .

Editor information

Editors and Affiliations

  1. National School of Engineers of Sfa, Sfax, Tunisia

    Fakher Chaari

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

    Radoslaw Zimroz

  3. Diagnostics and Vibro-Acoustics Science Laboratory, Wrocław Univ of Technology Inst of Mining Engineering, Wroclaw, Poland

    Walter Bartelmus

  4. Mechanical Engineering Department, National School of Engineers of Sfa, Sfax, Tunisia

    Mohamed Haddar

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Barszcz, T., Bielecki, A., Bielecka, M., Wójcik, M., Włuka, M. (2016). Vertical Axis Wind Turbine States Classification by an ART-2 Neural Network with a Stereographic Projection as a Signal Normalization. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_20

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  • DOI: https://doi.org/10.1007/978-3-319-20463-5_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20462-8

  • Online ISBN: 978-3-319-20463-5

  • eBook Packages: EngineeringEngineering (R0)

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