Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data
Weather-induced outages pose a significant threat to power grid reliability, with transmission systems particularly vulnerable to environmental stressors. Despite numerous tools developed to address this issue, the persistent challenge of weather-related interruptions highlights the need for an accu...
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Elsevier
2025-05-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000436 |
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| author | Mazin Al-Mahrouqi Abdollah Shafieezadeh Jieun Hur Jae-Wook Jung Jeong-Gon Ha Daegi Hahm |
| author_facet | Mazin Al-Mahrouqi Abdollah Shafieezadeh Jieun Hur Jae-Wook Jung Jeong-Gon Ha Daegi Hahm |
| author_sort | Mazin Al-Mahrouqi |
| collection | DOAJ |
| description | Weather-induced outages pose a significant threat to power grid reliability, with transmission systems particularly vulnerable to environmental stressors. Despite numerous tools developed to address this issue, the persistent challenge of weather-related interruptions highlights the need for an accurate fragility model for transmission line interruptions. This paper proposes a novel data-driven approach to model wind-induced transmission line fragility, addressing critical gaps in current methodologies. Our model integrates a novel synthetic data generation approach that creates highly informative synthetic data points, enhancing the representation of rare events. Additionally, we develop an advanced active learning framework that efficiently selects the most relevant instances from large, imbalanced datasets for model training. We further enhance model interpretability through comprehensive sensitivity analysis using SHAP (SHapley Additive exPlanations) values. Results on unseen testing data show significant improvement compared to conventional methods, achieving a 5% improvement in accuracy (from 0.89 to 0.94) in predicting wind-induced transmission line interruptions. Notably, it shows a 16% accuracy improvement (from 0.64 to 0.80) when applied to highly uncertain cases, highlighting its capabilities in high-uncertainty situations. Sensitivity analysis reveals wind gust and mean sea level pressure as the most critical factors influencing interruptions, while also uncovering complex temperature effects where, in a subset of situations, temperature has a significant impact on the interruption probability of lines. This advanced fragility model can offer valuable insights for both real-time dispatch decisions and long-term risk-informed planning, contributing to enhanced power grid resilience in the face of increasing weather-related challenges. |
| format | Article |
| id | doaj-art-3260f065d5c448dba89990c233459e39 |
| institution | OA Journals |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-3260f065d5c448dba89990c233459e392025-08-20T02:26:44ZengElsevierEnergy and AI2666-54682025-05-012010051110.1016/j.egyai.2025.100511Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced dataMazin Al-Mahrouqi0Abdollah Shafieezadeh1Jieun Hur2Jae-Wook Jung3Jeong-Gon Ha4Daegi Hahm5Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 2070 Neil Avenue, Columbus, 43210, OH, USARisk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 2070 Neil Avenue, Columbus, 43210, OH, USA; Corresponding author.Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 2070 Neil Avenue, Columbus, 43210, OH, USAAdvanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, 111 Daedeok-daero 989 beon-gil, Daejeon, 34057, Republic of KoreaAdvanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, 111 Daedeok-daero 989 beon-gil, Daejeon, 34057, Republic of KoreaAdvanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, 111 Daedeok-daero 989 beon-gil, Daejeon, 34057, Republic of KoreaWeather-induced outages pose a significant threat to power grid reliability, with transmission systems particularly vulnerable to environmental stressors. Despite numerous tools developed to address this issue, the persistent challenge of weather-related interruptions highlights the need for an accurate fragility model for transmission line interruptions. This paper proposes a novel data-driven approach to model wind-induced transmission line fragility, addressing critical gaps in current methodologies. Our model integrates a novel synthetic data generation approach that creates highly informative synthetic data points, enhancing the representation of rare events. Additionally, we develop an advanced active learning framework that efficiently selects the most relevant instances from large, imbalanced datasets for model training. We further enhance model interpretability through comprehensive sensitivity analysis using SHAP (SHapley Additive exPlanations) values. Results on unseen testing data show significant improvement compared to conventional methods, achieving a 5% improvement in accuracy (from 0.89 to 0.94) in predicting wind-induced transmission line interruptions. Notably, it shows a 16% accuracy improvement (from 0.64 to 0.80) when applied to highly uncertain cases, highlighting its capabilities in high-uncertainty situations. Sensitivity analysis reveals wind gust and mean sea level pressure as the most critical factors influencing interruptions, while also uncovering complex temperature effects where, in a subset of situations, temperature has a significant impact on the interruption probability of lines. This advanced fragility model can offer valuable insights for both real-time dispatch decisions and long-term risk-informed planning, contributing to enhanced power grid resilience in the face of increasing weather-related challenges.http://www.sciencedirect.com/science/article/pii/S2666546825000436Active learningFragility modelingGenerative Adversarial Networks (GANs)Risk assessmentTransmission line interruptionWind-induced failures |
| spellingShingle | Mazin Al-Mahrouqi Abdollah Shafieezadeh Jieun Hur Jae-Wook Jung Jeong-Gon Ha Daegi Hahm Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data Energy and AI Active learning Fragility modeling Generative Adversarial Networks (GANs) Risk assessment Transmission line interruption Wind-induced failures |
| title | Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data |
| title_full | Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data |
| title_fullStr | Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data |
| title_full_unstemmed | Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data |
| title_short | Wind-induced transmission line interruption fragility models: An adaptive GAN-augmented probabilistic classification approach for extremely unbalanced data |
| title_sort | wind induced transmission line interruption fragility models an adaptive gan augmented probabilistic classification approach for extremely unbalanced data |
| topic | Active learning Fragility modeling Generative Adversarial Networks (GANs) Risk assessment Transmission line interruption Wind-induced failures |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000436 |
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