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

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)


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 eval-uates 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 simu-lation results have shown that turbines with IDs T07 and T06 degraded the most. Moreover, the dete-rioration was clearer when we analyzed data concerning relatively low atmospheric temperature and relatively high wind speed. (c) 2022 Published by Elsevier Ltd.

Original languageEnglish
Pages (from-to)730-740
Number of pages11
JournalRenewable Energy
Publication statusPublished - May 2022


  • Concept-based model
  • Diagnosis
  • Health index
  • Prediction
  • Regression
  • Time series
  • Wind turbine


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