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Predicting Wind Turbine Blade Fault Condition to Enhance Wind Energy Harvest Through Classification via Regression Classifier

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Advances in Smart Grid Technology

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 687))

Abstract

Wind energy has turned into a huge contender of usual fossil fuel energy. The advancement of substantial wind turbines empowers to obtain energy more proficiently as a result of the growing interest for renewables on the planet. With the expanded zest for the usage of wind turbine power plants in remote ranges, basic condition monitoring will be one of the main factors in the proficient foundation of wind turbines in the energy field. The wind turbine is utilized to change over wind energy into electrical energy. To make wind energy more engaged from various resources of energy, related to execution, convenience, dependability, viability, the life of turbines must be enhanced. Fault recognition on cutting edge at an early stage will avoid the issue, as sharp edge destruction can prompt a disastrous result for the whole wind turbine framework. This paper brings a pattern recognition technology into the wind energy field and endeavours to anticipate a different fault condition which happens in wind turbine sharp edge using vibration signals.

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Authors and Affiliations

  1. Centre for Automation & Robotics (ANRO), Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, 603103, India

    A. Joshuva, M. Arjun, G. S. Sriram Gangadhar & S. S. Dhanush

  2. Information Technology Manager, Global Technology and Business Centre (GTBC), Ford Motor Pvt. Ltd, Chennai, Tamil Nadu, 60011, India

    R. Murugavel

  3. Department of Automobile Engineering, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, 603103, India

    V. A. Shridhar

Authors
  1. A. Joshuva
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  2. M. Arjun
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  3. R. Murugavel
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  4. V. A. Shridhar
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  5. G. S. Sriram Gangadhar
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  6. S. S. Dhanush
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Corresponding author

Correspondence to A. Joshuva .

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Editors and Affiliations

  1. Department of Management and Innovation Systems, University of Salerno, Fisciano, Italy

    Pierluigi Siano

  2. School of Electrical Engineering, Vellore Institute of Technology, Chennai, India

    K. Jamuna

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Joshuva, A., Arjun, M., Murugavel, R., Shridhar, V.A., Sriram Gangadhar, G.S., Dhanush, S.S. (2020). Predicting Wind Turbine Blade Fault Condition to Enhance Wind Energy Harvest Through Classification via Regression Classifier. In: Siano, P., Jamuna, K. (eds) Advances in Smart Grid Technology. Lecture Notes in Electrical Engineering, vol 687. Springer, Singapore. https://doi.org/10.1007/978-981-15-7245-6_2

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  • DOI: https://doi.org/10.1007/978-981-15-7245-6_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7244-9

  • Online ISBN: 978-981-15-7245-6

  • eBook Packages: EnergyEnergy (R0)

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