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

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

Renewable Energy, 2022, vol. 190, issue C, 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 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.

Keywords: Time series; Concept-based model; Regression; Wind turbine; Health index (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122004001
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:190:y:2022:i:c:p:730-740

DOI: 10.1016/j.renene.2022.03.116

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().