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Measuring wind turbine health using fuzzy-concept-based drifting models

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  • Jastrzebska, Agnieszka
  • Morales Hernández, Alejandro
  • Nápoles, Gonzalo
  • Salgueiro, Yamisleydi
  • Vanhoof, Koen

Abstract

Time series processing is an essential aspect of wind turbine health monitoring. In this paper, we propose two new approaches for analyzing wind turbine health. Both methods are based on abstract concepts, implemented using fuzzy sets, which allow aggregating and summarizing the underlying raw data in terms of relative low, moderate, and high power production. By observing a change in concepts, we infer the difference in a turbine's health. The first method evaluates the decrease or increase in relatively high and low power production. This task is performed using a regression model. The second method evaluates the overall drift of extracted concepts. A significant drift indicates that the power production process undergoes fluctuations in time. Concepts are labeled using linguistic labels, which makes our model easier to interpret. We applied the proposed approach to publicly available data describing four wind turbines, while exploring different external conditions (wind speed and temperature). The simulation results have shown that turbines with IDs T07 and T06 degraded the most. Moreover, the deterioration was clearer when we analyzed data concerning relatively low atmospheric temperature and relatively high wind speed.

Suggested Citation

  • Jastrzebska, Agnieszka & Morales Hernández, Alejandro & Nápoles, Gonzalo & Salgueiro, Yamisleydi & Vanhoof, Koen, 2022. "Measuring wind turbine health using fuzzy-concept-based drifting models," Renewable Energy, Elsevier, vol. 190(C), pages 730-740.
    • Handle: RePEc:eee:renene:v:190:y:2022:i:c:p:730-740
    DOI: 10.1016/j.renene.2022.03.116
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    References listed on IDEAS

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    1. Lei Zhang & Zongliang Qiao & Bingsen Hei & Youfei Tang & Shasha Liu, 2022. "Optimization of Steam Distribution Mode for Turbine Units Based on Governing Valve Characteristic Modeling," Energies, MDPI, vol. 15(23), pages 1-15, December.
    2. Li, Yanting & Wu, Zhenyu & Su, Yan, 2023. "Adaptive short-term wind power forecasting with concept drifts," Renewable Energy, Elsevier, vol. 217(C).

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