Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM
<p dir="ltr">The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirectional long short-term memory (B...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2022
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| _version_ | 1864513548244221952 |
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| author | Zahra Yahyaoui (17821208) |
| author2 | Mansour Hajji (16869894) Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) |
| author2_role | author author author author author |
| author_facet | Zahra Yahyaoui (17821208) Mansour Hajji (16869894) Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zahra Yahyaoui (17821208) Mansour Hajji (16869894) Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) |
| dc.date.none.fl_str_mv | 2022-08-23T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/en15176127 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Effective_Fault_Detection_and_Diagnosis_for_Power_Converters_in_Wind_Turbine_Systems_Using_KPCA-Based_BiLSTM/29069627 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Artificial intelligence Machine learning wind energy conversion (WEC) fault detection and diagnosis (FDD) kernel PCA (KPCA) bidirectional long short term memory (BiLSTM) |
| dc.title.none.fl_str_mv | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirectional long short-term memory (BiLSTM) classifier. In the developed FDD approach, the KPCA model is applied to extract and select the most effective features, while the BiLSTM is utilized for classification purposes. The developed KPCA-based BiLSTM approach involves two main steps: feature extraction and selection, and fault classification. The KPCA model is developed in order to select and extract the most efficient features and the final features are fed to the BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performance of the developed technique when compared to the conventional FDD methods. To evaluate the effectiveness of the proposed KPCA-based BiLSTM approach, we utilize data obtained from a healthy WTC, which are then injected with several fault scenarios: simple fault generator-side, simple fault grid-side, multiple fault generator-side, multiple fault grid-side, and mixed fault on both sides. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. Furthermore, the efficiency of fault diagnosis is shown by the classification accuracy parameter. The experimental results show the efficiency of the developed KPCA-based BiLSTM technique compared to the classical FDD techniques (an accuracy of 97.30%).</p><h2>Other Information</h2><p dir="ltr">Published in: Energies<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en15176127" target="_blank">https://dx.doi.org/10.3390/en15176127</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_136729fbea4f74e242850b46afee5487 |
| identifier_str_mv | 10.3390/en15176127 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29069627 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTMZahra Yahyaoui (17821208)Mansour Hajji (16869894)Majdi Mansouri (16869885)Kamaleldin Abodayeh (16904865)Kais Bouzrara (16869906)Hazem Nounou (16869900)EngineeringControl engineering, mechatronics and roboticsElectrical engineeringInformation and computing sciencesArtificial intelligenceMachine learningwind energy conversion (WEC)fault detection and diagnosis (FDD)kernel PCA (KPCA)bidirectional long short term memory (BiLSTM)<p dir="ltr">The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirectional long short-term memory (BiLSTM) classifier. In the developed FDD approach, the KPCA model is applied to extract and select the most effective features, while the BiLSTM is utilized for classification purposes. The developed KPCA-based BiLSTM approach involves two main steps: feature extraction and selection, and fault classification. The KPCA model is developed in order to select and extract the most efficient features and the final features are fed to the BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performance of the developed technique when compared to the conventional FDD methods. To evaluate the effectiveness of the proposed KPCA-based BiLSTM approach, we utilize data obtained from a healthy WTC, which are then injected with several fault scenarios: simple fault generator-side, simple fault grid-side, multiple fault generator-side, multiple fault grid-side, and mixed fault on both sides. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. Furthermore, the efficiency of fault diagnosis is shown by the classification accuracy parameter. The experimental results show the efficiency of the developed KPCA-based BiLSTM technique compared to the classical FDD techniques (an accuracy of 97.30%).</p><h2>Other Information</h2><p dir="ltr">Published in: Energies<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en15176127" target="_blank">https://dx.doi.org/10.3390/en15176127</a></p>2022-08-23T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/en15176127https://figshare.com/articles/journal_contribution/Effective_Fault_Detection_and_Diagnosis_for_Power_Converters_in_Wind_Turbine_Systems_Using_KPCA-Based_BiLSTM/29069627CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290696272022-08-23T12:00:00Z |
| spellingShingle | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM Zahra Yahyaoui (17821208) Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Artificial intelligence Machine learning wind energy conversion (WEC) fault detection and diagnosis (FDD) kernel PCA (KPCA) bidirectional long short term memory (BiLSTM) |
| status_str | publishedVersion |
| title | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM |
| title_full | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM |
| title_fullStr | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM |
| title_full_unstemmed | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM |
| title_short | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM |
| title_sort | Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM |
| topic | Engineering Control engineering, mechatronics and robotics Electrical engineering Information and computing sciences Artificial intelligence Machine learning wind energy conversion (WEC) fault detection and diagnosis (FDD) kernel PCA (KPCA) bidirectional long short term memory (BiLSTM) |