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10.1016_j.egyr.2023.04.373.pdf (1.62 MB)

A novel hybrid methodology for fault diagnosis of wind energy conversion systems

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journal contribution
submitted on 2024-01-22, 05:05 and posted on 2024-01-22, 09:01 authored by Khaled Dhibi, Majdi Mansouri, Mansour Hajji, Kais Bouzrara, Hazem Nounou, Mohamed Nounou

This paper proposes effective Random Forest (RF)-based fault detection and diagnosis for wind energy conversion (WEC) systems. The proposed technique involved two major steps: feature selection and fault classification. Feature selection pre-processing is an important step to increase the accuracy of the classification algorithm and decrease the dimensionality of a dataset. Therefore, a hybrid feature selection based diagnosis technique, that can preserve the advantages of wrapper and filter algorithms as well as RF model, is proposed. In the first phase, the neighborhood component analysis (NCA) filter algorithm is used to reduce and select only the pertinent features from the original raw data. This phase helps in improving data by removing redundant and unimportant features. In the second step, we applied a wrapper technique called equilibrium optimizer to get optimized features and better classification accuracy. The main idea behind using a hybrid feature selection step is to select a small subset from original data that can achieve maximum classification accuracy and reduce the computational complexity of the RF technique. Then, the sensitive and significant characteristics are transmitted to the RF model for classification purposes. The presented results prove that the proposed methods offer enhanced diagnosis accuracy when applied to WEC systems.

Other Information

Published in: Energy Reports
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.egyr.2023.04.373

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2023

License statement

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

Institution affiliated with

  • Texas A&M University at Qatar