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Title: | Measuring wind turbine health using fuzzy-concept-based drifting models | Authors: | Jastrzebska, Agnieszka MORALES HERNANDEZ, Alejandro NAPOLES RUIZ, Gonzalo Salgueiro, Yamisleydi VANHOOF, Koen |
Issue Date: | 2022 | Publisher: | PERGAMON-ELSEVIER SCIENCE LTD | Source: | RENEWABLE ENERGY, 190 , p. 730 -740 | 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 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. | Notes: | Jastrzebska, A (corresponding author), Warsaw Univ Technol, Fac Math & Informat Sci, Warsaw, Poland. A.Jastrzebska@mini.pw.edu.pl |
Keywords: | Time series;Concept-based model;Regression;Wind turbine;Health index | Document URI: | http://hdl.handle.net/1942/37485 | ISSN: | 0960-1481 | e-ISSN: | 1879-0682 | DOI: | 10.1016/j.renene.2022.03.116 | ISI #: | WOS:000793520500013 | Rights: | 2022 Published by Elsevier Ltd. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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Measuring wind turbine health using fuzzy-concept-based drifting models.pdf Restricted Access | Published version | 1.74 MB | Adobe PDF | View/Open Request a copy |
FINAL_WTAging_RenewableEnergyJournal.pdf | Peer-reviewed author version | 1.38 MB | Adobe PDF | View/Open |
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