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An End-to-End, Real-Time Solution for Condition Monitoring of Wind Turbine Generators

Author

Listed:
  • Adrian Stetco

    (Department of Computer Science, University of Manchester, Manchester M13 9PL, UK)

  • Juan Melecio Ramirez

    (Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 3BB, UK)

  • Anees Mohammed

    (Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 3BB, UK)

  • Siniša Djurović

    (Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 3BB, UK)

  • Goran Nenadic

    (Department of Computer Science, University of Manchester, Manchester M13 9PL, UK)

  • John Keane

    (Department of Computer Science, University of Manchester, Manchester M13 9PL, UK)

Abstract

Data-driven wind generator condition monitoring systems largely rely on multi-stage processing involving feature selection and extraction followed by supervised learning. These stages require expert analysis, are potentially error-prone and do not generalize well between applications. In this paper, we introduce a collection of end-to-end Convolutional Neural Networks for advanced condition monitoring of wind turbine generators. End-to-end models have the benefit of utilizing raw, unstructured signals to make predictions about the parameters of interest. This feature makes it easier to scale an existing collection of models to new predictive tasks (e.g., new failure types) since feature extracting steps are not required. These automated models achieve low Mean Squared Errors in predicting the generator operational state (40.85 for Speed and 0.0018 for Load) and high accuracy in diagnosing rotor demagnetization failures (99.67%) by utilizing only raw current signals. We show how to create, deploy and run the collection of proposed models in a real-time setting using a laptop connected to a test rig via a data acquisition card. Based on a sampling rate of 5 kHz, predictions are stored in an efficient time series database and monitored using a dynamic visualization framework. We further discuss existing options for understanding the decision process behind the predictions made by the models.

Suggested Citation

  • Adrian Stetco & Juan Melecio Ramirez & Anees Mohammed & Siniša Djurović & Goran Nenadic & John Keane, 2020. "An End-to-End, Real-Time Solution for Condition Monitoring of Wind Turbine Generators," Energies, MDPI, vol. 13(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4817-:d:413784
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    References listed on IDEAS

    as
    1. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    2. Kong, Ziqian & Tang, Baoping & Deng, Lei & Liu, Wenyi & Han, Yan, 2020. "Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units," Renewable Energy, Elsevier, vol. 146(C), pages 760-768.
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    Cited by:

    1. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.

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