Adaptive transformer-based multi-task learning framework for synchronous prediction of substation flooding and outage risks
•A novel Transformer-based multi-task prediction model synchronously predicts flooding and outage risks within substations.•Adaptive time coding improves temporal dependency modeling for flooding and outage predictions.•Feature fusion strategy handles multivariate inputs and reduces redundant inform...
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| Vydáno v: | Electric power systems research Ročník 242; s. 111450 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.05.2025
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| ISSN: | 0378-7796 |
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| Abstract | •A novel Transformer-based multi-task prediction model synchronously predicts flooding and outage risks within substations.•Adaptive time coding improves temporal dependency modeling for flooding and outage predictions.•Feature fusion strategy handles multivariate inputs and reduces redundant information.•Cost-sensitive learning loss reduces the impact of imbalance on the data side, while joint-weighted loss reduces the impact of imbalance on the model training side.•A novel data-driven model offers reliable decision support for substation flooding prevention.
Flooding disasters significantly threaten substation security, and forecasting risks of flooding and resulting outages within the substation is crucial for taking preventive measures and enhancing the substation's resilience. Existing models may suffer from low accuracy of risk prediction due to the difficulty of handling nonlinear multi-factors, dynamic temporal dependencies, and unbalanced data. Additionally, they rarely forecast flooding and outages simultaneously, leading to incomplete risk assessments. Therefore, a novel Transformer-based multi-task learning model (MTformer) is proposed to simultaneously predict flooding and outage risk within substations. MTformer is an attention-based shared encoder-decoder architecture that can achieve shared feature extraction and collaborative prediction. This model adopts three improved strategies: adaptive temporal encoding to enhance temporal dependency extraction, feature perception strategy to fuse heterogeneous data inputs, and training balancing strategy to balance multi-task training and reduce the impact of data imbalance. The experiment results show that the MTformer effectively predicts substation flooding and outage risks and outperforms the mainstream predictive model, with a decrease of 47.96 % in RMSE for flooding prediction and an increase of 39.82 % in F1 for outage prediction. Case studies demonstrate the potential of MTformer as a decision-making tool for proactive disaster mitigation and recovery planning. |
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| AbstractList | •A novel Transformer-based multi-task prediction model synchronously predicts flooding and outage risks within substations.•Adaptive time coding improves temporal dependency modeling for flooding and outage predictions.•Feature fusion strategy handles multivariate inputs and reduces redundant information.•Cost-sensitive learning loss reduces the impact of imbalance on the data side, while joint-weighted loss reduces the impact of imbalance on the model training side.•A novel data-driven model offers reliable decision support for substation flooding prevention.
Flooding disasters significantly threaten substation security, and forecasting risks of flooding and resulting outages within the substation is crucial for taking preventive measures and enhancing the substation's resilience. Existing models may suffer from low accuracy of risk prediction due to the difficulty of handling nonlinear multi-factors, dynamic temporal dependencies, and unbalanced data. Additionally, they rarely forecast flooding and outages simultaneously, leading to incomplete risk assessments. Therefore, a novel Transformer-based multi-task learning model (MTformer) is proposed to simultaneously predict flooding and outage risk within substations. MTformer is an attention-based shared encoder-decoder architecture that can achieve shared feature extraction and collaborative prediction. This model adopts three improved strategies: adaptive temporal encoding to enhance temporal dependency extraction, feature perception strategy to fuse heterogeneous data inputs, and training balancing strategy to balance multi-task training and reduce the impact of data imbalance. The experiment results show that the MTformer effectively predicts substation flooding and outage risks and outperforms the mainstream predictive model, with a decrease of 47.96 % in RMSE for flooding prediction and an increase of 39.82 % in F1 for outage prediction. Case studies demonstrate the potential of MTformer as a decision-making tool for proactive disaster mitigation and recovery planning. |
| ArticleNumber | 111450 |
| Author | Shi, Ying Yao, Degui Liang, Yun Shi, Yu Lu, Ming |
| Author_xml | – sequence: 1 givenname: Yu surname: Shi fullname: Shi, Yu organization: School of Automation, Wuhan University of Technology, Wuhan, Hubei 430070, China – sequence: 2 givenname: Ying orcidid: 0000-0003-3519-2308 surname: Shi fullname: Shi, Ying email: a_laly@163.com organization: School of Automation, Wuhan University of Technology, Wuhan, Hubei 430070, China – sequence: 3 givenname: Degui surname: Yao fullname: Yao, Degui organization: State Grid Henan Electric Power Research Institute, Zhengzhou 450000, China – sequence: 4 givenname: Ming surname: Lu fullname: Lu, Ming organization: State Grid Henan Electric Power Research Institute, Zhengzhou 450000, China – sequence: 5 givenname: Yun surname: Liang fullname: Liang, Yun organization: State Grid Henan Electric Power Research Institute, Zhengzhou 450000, China |
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| Cites_doi | 10.1145/2347736.2347755 10.1109/TPAMI.2021.3054719 10.1613/jair.1.11192 10.1109/TMM.2019.2920613 10.1109/TSG.2022.3166600 10.3390/w10111536 10.1109/TPWRD.2016.2598572 10.1186/s40537-016-0043-6 10.1016/j.epsr.2024.110901 10.1109/TPWRS.2015.2429656 10.1016/j.ijforecast.2021.03.012 10.1016/j.epsr.2023.109473 10.1016/j.ress.2024.110169 10.1109/PESGM.2017.8273905 10.1038/nature14539 10.1016/j.epsr.2022.108098 10.1016/j.jhydrol.2020.124631 10.1038/s42256-022-00568-3 10.1016/j.comnet.2020.107744 10.1109/ACCESS.2024.3355484 10.9734/air/2023/v24i5961 10.1007/s00500-020-04954-0 10.1016/j.ijepes.2021.107545 10.1109/TIM.2023.3238059 10.2166/nh.2020.068 10.3390/su12041527 10.1109/TPWRS.2021.3086031 10.3390/en12020205 10.1016/j.ress.2022.108628 10.1109/TPWRS.2022.3146229 |
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| Keywords | Risk prediction and assessment Transformer algorithm Substation flooding Multi-task learning Substation outage |
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