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10.1016_j.renene.2022.05.082.pdf (1.94 MB)

Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems

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submitted on 2023-12-04, 10:28 and posted on 2023-12-04, 11:52 authored by Khaled Dhibi, Majdi Mansouri, Kais Bouzrara, Hazem Nounou, Mohamed Nounou

Wind energy (WE) is one of the most important technology to produce energy and an efficient source of renewable energy (RE) available in the atmospheric environment due to different air-currents spread over the stratosphere and troposphere. Wind energy conversion (WEC) system has become a focal point in the research of RE in recent years. Moreover, fault detection and diagnosis (FDD) plays an important role in ensuring WEC safety. In the past decades, neural networks (NN) has provided an effective performance in fault diagnosis. On the other hand, ensemble learning (EL) techniques have gained significant attention from the scientific community. EL is a technique that creates and combines multiple machine learning models in order to produce one optimal predictive model which gives improved results. The goal of this paper is to develop and validate effective neural networks based ensemble approach. First, an ensemble classifier based on neural networks techniques and using bagging, boosting, and random subspace combination techniques is proposed. Second, an improved extension of the proposed neural networks-based ensemble technique is presented. Finally, the results obtained from the proposed neural networks-based ensemble techniques are compared with other methods to illustrate and validate the advantages of the proposed techniques.

Other Information

Published in: Renewable Energy
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.renene.2022.05.082

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Texas A&M University at Qatar