A novel hybrid methodology for fault diagnosis of wind energy conversion systems
<p>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 a...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , |
| Published: |
2023
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513529462128640 |
|---|---|
| author | Khaled Dhibi (16891524) |
| author2 | Majdi Mansouri (16869885) Mansour Hajji (16869894) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author2_role | author author author author author |
| author_facet | Khaled Dhibi (16891524) Majdi Mansouri (16869885) Mansour Hajji (16869894) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Khaled Dhibi (16891524) Majdi Mansouri (16869885) Mansour Hajji (16869894) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2023-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.egyr.2023.04.373 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_novel_hybrid_methodology_for_fault_diagnosis_of_wind_energy_conversion_systems/25036670 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning Neighborhood component analysis (NCA) Equilibrium optimizer (EO) Random Forest (RF) Fault diagnosis Fault classification Wind energy conversion (WEC) |
| dc.title.none.fl_str_mv | A novel hybrid methodology for fault diagnosis of wind energy conversion systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2> <p> Published in: Energy Reports<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2023.04.373" target="_blank">https://dx.doi.org/10.1016/j.egyr.2023.04.373</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_cb6e3dc2b32b8a1c23a5ce6039c98f3e |
| identifier_str_mv | 10.1016/j.egyr.2023.04.373 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25036670 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A novel hybrid methodology for fault diagnosis of wind energy conversion systemsKhaled Dhibi (16891524)Majdi Mansouri (16869885)Mansour Hajji (16869894)Kais Bouzrara (16869906)Hazem Nounou (16869900)Mohamed Nounou (3489386)EngineeringElectrical engineeringEnvironmental engineeringInformation and computing sciencesMachine learningNeighborhood component analysis (NCA)Equilibrium optimizer (EO)Random Forest (RF)Fault diagnosisFault classificationWind energy conversion (WEC)<p>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.</p><h2>Other Information</h2> <p> Published in: Energy Reports<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2023.04.373" target="_blank">https://dx.doi.org/10.1016/j.egyr.2023.04.373</a></p>2023-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.egyr.2023.04.373https://figshare.com/articles/journal_contribution/A_novel_hybrid_methodology_for_fault_diagnosis_of_wind_energy_conversion_systems/25036670CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/250366702023-12-01T00:00:00Z |
| spellingShingle | A novel hybrid methodology for fault diagnosis of wind energy conversion systems Khaled Dhibi (16891524) Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning Neighborhood component analysis (NCA) Equilibrium optimizer (EO) Random Forest (RF) Fault diagnosis Fault classification Wind energy conversion (WEC) |
| status_str | publishedVersion |
| title | A novel hybrid methodology for fault diagnosis of wind energy conversion systems |
| title_full | A novel hybrid methodology for fault diagnosis of wind energy conversion systems |
| title_fullStr | A novel hybrid methodology for fault diagnosis of wind energy conversion systems |
| title_full_unstemmed | A novel hybrid methodology for fault diagnosis of wind energy conversion systems |
| title_short | A novel hybrid methodology for fault diagnosis of wind energy conversion systems |
| title_sort | A novel hybrid methodology for fault diagnosis of wind energy conversion systems |
| topic | Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning Neighborhood component analysis (NCA) Equilibrium optimizer (EO) Random Forest (RF) Fault diagnosis Fault classification Wind energy conversion (WEC) |