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This paper addresses the critical issue of wind turbine failure, focusing on the detection and mitigation of anomalies in gearbox components, specifically oil and bearing temperatures. Historical data analysis reveals the gradual deterioration of main bearings during operation, necessitating proactive maintenance measures. Two predictive models are developed using ensemble algorithms, achieving high accuracy of 94.7154% for gearbox oil temperature and 95.758% for gearbox bearing temperature. Comparative analysis shows slight differences in accuracy and computational time between the models. Practical implications include the identification of values exceeding operational limits, aiding in maintenance scheduling and efficiency improvement. These models offer valuable insights for enhancing wind turbine reliability and reducing energy losses associated with mechanical failures. Further refinement and integration of these models could significantly benefit wind turbine operations.

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Enhancing Fault Detection in Wind Turbines Using Ensemble Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Ricardo Manuel Arias Velásquez,
  • Alvaro Moises Castro Romero,
  • Pedro Rafael Conde Chumpitaz,
  • Jennifer Vanessa Mejía Lara

摘要

This paper addresses the critical issue of wind turbine failure, focusing on the detection and mitigation of anomalies in gearbox components, specifically oil and bearing temperatures. Historical data analysis reveals the gradual deterioration of main bearings during operation, necessitating proactive maintenance measures. Two predictive models are developed using ensemble algorithms, achieving high accuracy of 94.7154% for gearbox oil temperature and 95.758% for gearbox bearing temperature. Comparative analysis shows slight differences in accuracy and computational time between the models. Practical implications include the identification of values exceeding operational limits, aiding in maintenance scheduling and efficiency improvement. These models offer valuable insights for enhancing wind turbine reliability and reducing energy losses associated with mechanical failures. Further refinement and integration of these models could significantly benefit wind turbine operations.