Please use this identifier to cite or link to this item: http://cris.utm.md/handle/5014/1994
Title: Intelligent Condition Monitoring of Wind Turbine Blades: A Preliminary Approach
Authors: MUNTEANU, Eugeniu 
ZAPOROJAN, Sergiu 
DULGHERU, Valeriu 
SLAVESCU, Radu Razvan 
LARIN, Vladimir 
RABEI, Ivan 
Keywords: wind turbine blade;condition monitoring;predictive maintenance;contactless strain sensor;machine learning
Issue Date: 2022
Publisher: IEEE
Source: E. Munteanu, S. Zaporojan, V. Dulgheru, R. R. Slavescu, V. Larin and I. Rabei, "Intelligent Condition Monitoring of Wind Turbine Blades: A Preliminary Approach," 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2022, pp. 9-16, doi: 10.1109/ICCP56966.2022.10053939.
Journal: 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP)
Abstract: 
Exploring new areas for wind energy production brings new challenges for improvement of wind farms, making them more reliable and suitable for increasing of the power grids. In this regard, it is important to study and propose reliable solutions for contactless intelligent condition monitoring of the wind turbine blades. The article is intended to contribute to the study of various aspects of this actual multidisciplinary topic problem. In this context, the paper follows a systemic approach based on relevant general-purpose pragmatic quality criteria. It reports on the preliminary results obtained when analyzing the problem of building an embedded intelligent monitoring of the state of wind turbine blades using contactless strain sensors. For this reason, the numerical modeling of the blade deformations was performed in order to get the pattern of the maximum deformations of the blade. At the same time, from the pragmatic quality point of view, the required dataset, parameters of interest and intended data protocols were defined. Finally, a detailed structure of the edge computing module, as well as a preliminary framework for an embedded intelligent monitoring and decisionmaking system for predictive maintenance are presented.
URI: http://cris.utm.md/handle/5014/1994
ISBN: 978-1-6654-6437-6
978-1-6654-6436-9
978-1-6654-6438-3
ISSN: 2766-8495
2065-9946
DOI: 10.1109/ICCP56966.2022.10053939
Appears in Collections:Proceedings Papers

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