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Support Vector Machine and K-fold Cross-validation to Detect False Alarms in Wind Turbines

In: Sustainability

Author

Listed:
  • Ana Maria Peco Chacon

    (Universidad Castilla-La Mancha)

  • Fausto Pedro García Márquez

    (Universidad Castilla-La Mancha)

Abstract

Wind energy is a growing market, due to improved monitoring systems, decreased downtime, and optimal predictive maintenance. Classification algorithms based on machine learning efficiently categorize large volumes of complex data. K-Fold cross-validation is a competent tool for measuring the success rate of classification algorithms. The purpose of this research is to compare the performance of various kernel functions for the support vector machine algorithm, as well as to analyze the different values of K-fold cross-validation and holdout validation. The approach is applied to a real dataset of wind turbine, with the aim to detect false alarms. The results indicate an accuracy of 99.2%, and the F1 is 0.996. These values indicate that the methodology proposed is efficient to detect and identify false alarms.

Suggested Citation

  • Ana Maria Peco Chacon & Fausto Pedro García Márquez, 2023. "Support Vector Machine and K-fold Cross-validation to Detect False Alarms in Wind Turbines," International Series in Operations Research & Management Science, in: Fausto Pedro García Márquez & Benjamin Lev (ed.), Sustainability, pages 81-97, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-16620-4_6
    DOI: 10.1007/978-3-031-16620-4_6
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