FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults
<p dir="ltr">Transient stability assessment (TSA) is critical to the reliable operation of a power system against severe fault conditions. In practice, TSA based on deep learning is preferable for its high accuracy but often overlooks challenges in maintaining data privacy while copi...
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2024
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| _version_ | 1864513541464129536 |
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| author | Mohamed Massaoudi (16888710) |
| author2 | Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| author2_role | author author |
| author_facet | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| dc.date.none.fl_str_mv | 2024-07-19T21:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojia.2024.3426281 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/FLACON_A_Deep_Federated_Transfer_Learning-Enabled_Transient_Stability_Assessment_During_Symmetrical_and_Asymmetrical_Grid_Faults/29900330 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Cybersecurity and privacy Machine learning Federated transfer learning (FTL) multihead attention (MHA) mechanisms power grid faults symmetrical and asymmetrical faults transient stability assessment (TSA) Power system stability Stability criteria Transient analysis Transfer learning Generators Phasor measurement units Long short term memory |
| dc.title.none.fl_str_mv | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Transient stability assessment (TSA) is critical to the reliable operation of a power system against severe fault conditions. In practice, TSA based on deep learning is preferable for its high accuracy but often overlooks challenges in maintaining data privacy while coping with network topology changes. This article proposes an innovative focal loss-based multihead attention convolutional network (FLACON) for accurate post-disturbance TSA under both symmetrical and asymmetrical smart grid faults. The proposed approach effectively incorporates cross-domain deep federated transfer learning (FTL) to leverage local operating data for TSA in a decentralized fashion. By introducing convolutional layers alongside multi-head attention mechanisms, the FLACON framework significantly improves learning efficiency across geographically distributed datasets. To address the challenge of class imbalance, the model integrates a balance factor-enhanced focal loss function. The FTL architecture enables decentralized model training across various clients, thus preserving data privacy and reducing the burden of communication overhead. To avoid the constant adjustment of hyperparameters, the FLACON employs an inductive transfer learning approach for hyperparameter tuning of the pre-trained model, markedly decreasing training time. Extensive experiments on datasets from the IEEE 39-bus system and the IEEE 68-bus system demonstrate FLACON's exceptional accuracy of 98.98% compared to some competitive alternatives.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of Industry Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojia.2024.3426281" target="_blank">https://dx.doi.org/10.1109/ojia.2024.3426281</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7c374b44202a166aca093006223812ba |
| identifier_str_mv | 10.1109/ojia.2024.3426281 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29900330 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid FaultsMohamed Massaoudi (16888710)Haitham Abu-Rub (16855500)Ali Ghrayeb (16864266)EngineeringElectrical engineeringInformation and computing sciencesCybersecurity and privacyMachine learningFederated transfer learning (FTL)multihead attention (MHA) mechanismspower grid faultssymmetrical and asymmetrical faultstransient stability assessment (TSA)Power system stabilityStability criteriaTransient analysisTransfer learningGeneratorsPhasor measurement unitsLong short term memory<p dir="ltr">Transient stability assessment (TSA) is critical to the reliable operation of a power system against severe fault conditions. In practice, TSA based on deep learning is preferable for its high accuracy but often overlooks challenges in maintaining data privacy while coping with network topology changes. This article proposes an innovative focal loss-based multihead attention convolutional network (FLACON) for accurate post-disturbance TSA under both symmetrical and asymmetrical smart grid faults. The proposed approach effectively incorporates cross-domain deep federated transfer learning (FTL) to leverage local operating data for TSA in a decentralized fashion. By introducing convolutional layers alongside multi-head attention mechanisms, the FLACON framework significantly improves learning efficiency across geographically distributed datasets. To address the challenge of class imbalance, the model integrates a balance factor-enhanced focal loss function. The FTL architecture enables decentralized model training across various clients, thus preserving data privacy and reducing the burden of communication overhead. To avoid the constant adjustment of hyperparameters, the FLACON employs an inductive transfer learning approach for hyperparameter tuning of the pre-trained model, markedly decreasing training time. Extensive experiments on datasets from the IEEE 39-bus system and the IEEE 68-bus system demonstrate FLACON's exceptional accuracy of 98.98% compared to some competitive alternatives.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of Industry Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojia.2024.3426281" target="_blank">https://dx.doi.org/10.1109/ojia.2024.3426281</a></p>2024-07-19T21:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojia.2024.3426281https://figshare.com/articles/journal_contribution/FLACON_A_Deep_Federated_Transfer_Learning-Enabled_Transient_Stability_Assessment_During_Symmetrical_and_Asymmetrical_Grid_Faults/29900330CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299003302024-07-19T21:00:00Z |
| spellingShingle | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults Mohamed Massaoudi (16888710) Engineering Electrical engineering Information and computing sciences Cybersecurity and privacy Machine learning Federated transfer learning (FTL) multihead attention (MHA) mechanisms power grid faults symmetrical and asymmetrical faults transient stability assessment (TSA) Power system stability Stability criteria Transient analysis Transfer learning Generators Phasor measurement units Long short term memory |
| status_str | publishedVersion |
| title | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults |
| title_full | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults |
| title_fullStr | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults |
| title_full_unstemmed | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults |
| title_short | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults |
| title_sort | FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults |
| topic | Engineering Electrical engineering Information and computing sciences Cybersecurity and privacy Machine learning Federated transfer learning (FTL) multihead attention (MHA) mechanisms power grid faults symmetrical and asymmetrical faults transient stability assessment (TSA) Power system stability Stability criteria Transient analysis Transfer learning Generators Phasor measurement units Long short term memory |