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Wind Turbine Machine Models01:24

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Related Experiment Video

Updated: Feb 28, 2026

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Explainable Machine Learning for Tower-Radar Monitoring of Wind Turbine Blades: Fine-Grained Blade Recognition Under

Sercan Alipek1, Christian Kexel2, Jochen Moll1

  • 1Department of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076 Siegen, Germany.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

Radar measurements can identify unique structural features on wind turbine blades, enabling classification even with changing environmental conditions. This approach aids in precise rotor blade monitoring.

Keywords:
deep learningexplainable AIradar sensor systemtower-radar monitoringwind energywind turbine blade classification

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Area of Science:

  • Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Operational wind turbine blade classification using tower-radar systems faces challenges in transferability, environmental condition adaptation, and explainability.
  • Deep neural networks are increasingly used for analyzing complex sensor data in industrial applications.

Purpose of the Study:

  • To evaluate a data-driven classification approach for operational wind turbine blades using tower-radar measurements.
  • To address challenges in transferability, evolving environmental and operational conditions (EOCs), and explainability of deep neural decisions in tower-radar monitoring (TRM).

Main Methods:

  • Utilized consecutive tower-radar measurements compressed into a two-dimensional slow-time to range representation (radargram).
  • Employed a frequency-modulated continuous wave (FMCW) radar system operating in the 33.4-36 GHz frequency range.
  • Applied a class-sensitive visualization technique, Guided Gradient-weighted Class Activation Mapping (GuidedGradCAM), to identify relevant radargram features for convolutional neural networks.

Main Results:

  • Individual rotor blades exhibit characteristic structural features detectable by radar sensors, allowing for neural network-based discrimination.
  • These identified features demonstrate resilience and are not rendered ineffective by changing EOCs.
  • Pixel-level distortions highlighted the critical role of low-level information for accurate rotor blade classification.

Conclusions:

  • Tower-radar monitoring (TRM) can effectively classify individual wind turbine blades by leveraging unique structural features identified through radar.
  • The developed machine learning approach shows promise in adapting to varying EOCs, enhancing the robustness of TRM systems.
  • Explainability techniques like GuidedGradCAM are crucial for understanding and validating the decisions made by deep neural networks in TRM applications.