Extreme outage prediction in power systems using a new deep generative Informer model
<p>Extreme weather events have made growing concerns over electric power grid infrastructure as well as the residents living in disaster areas. Moreover, the potential damages due to the extreme events can make serious challenges for supply reliability and security, leading to widespread power...
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2025
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| _version_ | 1864513534702911488 |
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| author | Razieh Rastgoo (22457767) |
| author2 | Nima Amjady (8176431) Syed Islam (7331948) Innocent Kamwa (12757145) S.M. Muyeen (15746160) |
| author2_role | author author author author |
| author_facet | Razieh Rastgoo (22457767) Nima Amjady (8176431) Syed Islam (7331948) Innocent Kamwa (12757145) S.M. Muyeen (15746160) |
| author_role | author |
| dc.creator.none.fl_str_mv | Razieh Rastgoo (22457767) Nima Amjady (8176431) Syed Islam (7331948) Innocent Kamwa (12757145) S.M. Muyeen (15746160) |
| dc.date.none.fl_str_mv | 2025-03-23T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.ijepes.2025.110627 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Extreme_outage_prediction_in_power_systems_using_a_new_deep_generative_Informer_model/30393295 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Deep Learning Extreme Events Generative Adversarial Network (GAN) Informer Outage Prediction |
| dc.title.none.fl_str_mv | Extreme outage prediction in power systems using a new deep generative Informer model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Extreme weather events have made growing concerns over electric power grid infrastructure as well as the residents living in disaster areas. Moreover, the potential damages due to the extreme events can make serious challenges for supply reliability and security, leading to widespread power outages in power systems. This paper proposes a deep learning-based framework for power data rebalancing and outage prediction in power systems to cope with the extreme events. To this end, we propose an Adaptive Wasserstein Conditional Generative Adversarial Network for data generation. Also, we propose a new Wasserstein Bidirectional Generative Adversarial Network with the Informer model, embedded in both the Generator and Discriminator Networks, plus an Encoder Network for the outage prediction in power systems. Two-step classification approach has been used in the proposed outage prediction model: classifying the power grid components into impacted and non-impacted categories and classifying the impacted category into in-service and out-of-service categories. In addition, a new classification-specific loss function is proposed for the minimax objective function of the Vanilla Generative Adversarial Network to improve the prediction performance in the latent space. Evaluation results of the proposed model and 15 comparative models in three groups using six evaluation metrics on a real-world test case demonstrate the superiority of the proposed model compared to all comparative models. These results confirm that the proposed outage prediction model can be effectively employed for accurately predicting extreme outages in power systems.</p><h2>Other Information</h2> <p> Published in: International Journal of Electrical Power & Energy Systems<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.ijepes.2025.110627" target="_blank">https://dx.doi.org/10.1016/j.ijepes.2025.110627</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a38d961723abe2cc967f575199a9ea09 |
| identifier_str_mv | 10.1016/j.ijepes.2025.110627 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30393295 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Extreme outage prediction in power systems using a new deep generative Informer modelRazieh Rastgoo (22457767)Nima Amjady (8176431)Syed Islam (7331948)Innocent Kamwa (12757145)S.M. Muyeen (15746160)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceDeep LearningExtreme EventsGenerative Adversarial Network (GAN)InformerOutage Prediction<p>Extreme weather events have made growing concerns over electric power grid infrastructure as well as the residents living in disaster areas. Moreover, the potential damages due to the extreme events can make serious challenges for supply reliability and security, leading to widespread power outages in power systems. This paper proposes a deep learning-based framework for power data rebalancing and outage prediction in power systems to cope with the extreme events. To this end, we propose an Adaptive Wasserstein Conditional Generative Adversarial Network for data generation. Also, we propose a new Wasserstein Bidirectional Generative Adversarial Network with the Informer model, embedded in both the Generator and Discriminator Networks, plus an Encoder Network for the outage prediction in power systems. Two-step classification approach has been used in the proposed outage prediction model: classifying the power grid components into impacted and non-impacted categories and classifying the impacted category into in-service and out-of-service categories. In addition, a new classification-specific loss function is proposed for the minimax objective function of the Vanilla Generative Adversarial Network to improve the prediction performance in the latent space. Evaluation results of the proposed model and 15 comparative models in three groups using six evaluation metrics on a real-world test case demonstrate the superiority of the proposed model compared to all comparative models. These results confirm that the proposed outage prediction model can be effectively employed for accurately predicting extreme outages in power systems.</p><h2>Other Information</h2> <p> Published in: International Journal of Electrical Power & Energy Systems<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.ijepes.2025.110627" target="_blank">https://dx.doi.org/10.1016/j.ijepes.2025.110627</a></p>2025-03-23T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.ijepes.2025.110627https://figshare.com/articles/journal_contribution/Extreme_outage_prediction_in_power_systems_using_a_new_deep_generative_Informer_model/30393295CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303932952025-03-23T12:00:00Z |
| spellingShingle | Extreme outage prediction in power systems using a new deep generative Informer model Razieh Rastgoo (22457767) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Deep Learning Extreme Events Generative Adversarial Network (GAN) Informer Outage Prediction |
| status_str | publishedVersion |
| title | Extreme outage prediction in power systems using a new deep generative Informer model |
| title_full | Extreme outage prediction in power systems using a new deep generative Informer model |
| title_fullStr | Extreme outage prediction in power systems using a new deep generative Informer model |
| title_full_unstemmed | Extreme outage prediction in power systems using a new deep generative Informer model |
| title_short | Extreme outage prediction in power systems using a new deep generative Informer model |
| title_sort | Extreme outage prediction in power systems using a new deep generative Informer model |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Deep Learning Extreme Events Generative Adversarial Network (GAN) Informer Outage Prediction |