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http://hdl.handle.net/1942/37485
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DC Field | Value | Language |
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dc.contributor.author | Jastrzebska, Agnieszka | - |
dc.contributor.author | MORALES HERNANDEZ, Alejandro | - |
dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | Salgueiro, Yamisleydi | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.date.accessioned | 2022-06-09T13:30:26Z | - |
dc.date.available | 2022-06-09T13:30:26Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-05-31T11:05:52Z | - |
dc.identifier.citation | RENEWABLE ENERGY, 190 , p. 730 -740 | - |
dc.identifier.uri | http://hdl.handle.net/1942/37485 | - |
dc.description.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. | - |
dc.description.sponsorship | The project was partially funded by POB Research Center for Artificial Intelligence and Robotics of Warsaw University of Technology within the Excellence Initiative Program - Research University (ID-UB). | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.rights | 2022 Published by Elsevier Ltd. | - |
dc.subject.other | Time series | - |
dc.subject.other | Concept-based model | - |
dc.subject.other | Regression | - |
dc.subject.other | Wind turbine | - |
dc.subject.other | Health index | - |
dc.title | Measuring wind turbine health using fuzzy-concept-based drifting models | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 740 | - |
dc.identifier.spage | 730 | - |
dc.identifier.volume | 190 | - |
local.format.pages | 11 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Jastrzebska, A (corresponding author), Warsaw Univ Technol, Fac Math & Informat Sci, Warsaw, Poland. | - |
dc.description.notes | A.Jastrzebska@mini.pw.edu.pl | - |
local.publisher.place | THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1016/j.renene.2022.03.116 | - |
dc.identifier.isi | WOS:000793520500013 | - |
dc.contributor.orcid | Salgueiro Sicilia, Yamisleydi/0000-0002-1946-0053 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Jastrzebska, Agnieszka] Warsaw Univ Technol, Fac Math & Informat Sci, Warsaw, Poland. | - |
local.description.affiliation | [Hernandez, Alejandro Morales; Vanhoof, Koen] Hasselt Univ, Fac Business Econ, Hasselt, Belgium. | - |
local.description.affiliation | [Napoles, Gonzalo] Tilburg Univ, Dept Cognit Sci & Artificial Intelligence, Tilburg, Netherlands. | - |
local.description.affiliation | [Salgueiro, Yamisleydi] Univ Talca, Fac Engn, Dept Comp Sci, Campus Curico, Curico, Chile. | - |
local.uhasselt.international | yes | - |
item.validation | ecoom 2023 | - |
item.contributor | Jastrzebska, Agnieszka | - |
item.contributor | MORALES HERNANDEZ, Alejandro | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | Salgueiro, Yamisleydi | - |
item.contributor | VANHOOF, Koen | - |
item.accessRights | Open Access | - |
item.fullcitation | Jastrzebska, Agnieszka; MORALES HERNANDEZ, Alejandro; NAPOLES RUIZ, Gonzalo; Salgueiro, Yamisleydi & VANHOOF, Koen (2022) Measuring wind turbine health using fuzzy-concept-based drifting models. In: RENEWABLE ENERGY, 190 , p. 730 -740. | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 0960-1481 | - |
crisitem.journal.eissn | 1879-0682 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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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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