Extreme outage prediction in power systems using a new deep generative Informer model
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...
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2025
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| Online Access: | http://dx.doi.org/10.1016/j.ijepes.2025.110627 https://www.sciencedirect.com/science/article/pii/S0142061525001784 http://hdl.handle.net/10576/65654 |
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| _version_ | 1857415083681906688 |
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| author | Rastgoo, Razieh |
| author2 | Amjady, Nima Islam, Syed Kamwa, Innocent Muyeen, S.M. |
| author2_role | author author author author |
| author_facet | Rastgoo, Razieh Amjady, Nima Islam, Syed Kamwa, Innocent Muyeen, S.M. |
| author_role | author |
| dc.creator.none.fl_str_mv | Rastgoo, Razieh Amjady, Nima Islam, Syed Kamwa, Innocent Muyeen, S.M. |
| dc.date.none.fl_str_mv | 2025-06-22T07:34:57Z 2025-06-30 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.ijepes.2025.110627 Rastgoo, R., Amjady, N., Islam, S., Kamwa, I., & Muyeen, S. M. (2025). Extreme outage prediction in power systems using a new deep generative Informer model. International Journal of Electrical Power & Energy Systems, 167, 110627. 01420615 https://www.sciencedirect.com/science/article/pii/S0142061525001784 http://hdl.handle.net/10576/65654 167 1879-3517 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_e21865af566852a8486d53c623c360cd |
| identifier_str_mv | Rastgoo, R., Amjady, N., Islam, S., Kamwa, I., & Muyeen, S. M. (2025). Extreme outage prediction in power systems using a new deep generative Informer model. International Journal of Electrical Power & Energy Systems, 167, 110627. 01420615 167 1879-3517 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/65654 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | Extreme outage prediction in power systems using a new deep generative Informer modelRastgoo, RaziehAmjady, NimaIslam, SyedKamwa, InnocentMuyeen, S.M.Deep LearningExtreme EventsGenerative Adversarial Network (GAN)InformerOutage PredictionExtreme 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.This research was made possible by the 1st Cycle of ARG Grant No. ARG01-0504-230073, from the Qatar Research, Development and Innovation (QRDI) Council, Qatar. The findings herein reflect the work, and are solely the responsibility, of the authors. The authors also gratefully acknowledge support from Qatar University. Open Access funding provided by the Qatar National Library.Elsevier2025-06-22T07:34:57Z2025-06-30Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.ijepes.2025.110627Rastgoo, R., Amjady, N., Islam, S., Kamwa, I., & Muyeen, S. M. (2025). Extreme outage prediction in power systems using a new deep generative Informer model. International Journal of Electrical Power & Energy Systems, 167, 110627.01420615https://www.sciencedirect.com/science/article/pii/S0142061525001784http://hdl.handle.net/10576/656541671879-3517enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/656542025-06-22T19:08:04Z |
| spellingShingle | Extreme outage prediction in power systems using a new deep generative Informer model Rastgoo, Razieh 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 | Deep Learning Extreme Events Generative Adversarial Network (GAN) Informer Outage Prediction |
| url | http://dx.doi.org/10.1016/j.ijepes.2025.110627 https://www.sciencedirect.com/science/article/pii/S0142061525001784 http://hdl.handle.net/10576/65654 |