Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages
Understanding and quantifying the impact of severe weather events on the electric transmission and distribution system is crucial for ensuring its resilience in the context of the increasing frequency and intensity of extreme weather events caused by climate change. While weather impact models for t...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10816600/ |
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| author | Sita Nyame William O. Taylor William Hughes Mingguo Hong Marika Koukoula Feifei Yang Aaron Spaulding Xiaochuan Luo Slava Maslennikov Diego Cerrai |
| author_facet | Sita Nyame William O. Taylor William Hughes Mingguo Hong Marika Koukoula Feifei Yang Aaron Spaulding Xiaochuan Luo Slava Maslennikov Diego Cerrai |
| author_sort | Sita Nyame |
| collection | DOAJ |
| description | Understanding and quantifying the impact of severe weather events on the electric transmission and distribution system is crucial for ensuring its resilience in the context of the increasing frequency and intensity of extreme weather events caused by climate change. While weather impact models for the distribution system have been widely developed during the past decade, transmission system impact models lagged behind because of the scarcity of data. This study demonstrates a weather impact model for predicting the probability of failure of transmission lines. It builds upon a recently developed model and focuses on reducing model bias, through multi-model integration, feature engineering, and the development of a storm index that leverages distribution system data to aid the prediction of transmission risk. We explored three methods for integrating machine learning with mechanistic models. They consist of: (a) creating a linear combination of the outputs of the two modeling approaches, (b) including fragility curves as additional inputs to machine learning models, and (c) developing a new machine learning model that uses the outputs of the weather-based machine learning model, fragility curve estimates, and wind data to make new predictions. Moreover, due to the limited number of historical failures in transmission networks, a storm index was developed leveraging a dataset of distribution outages to learn about storm behavior to improve model skills. In the current version of the model, we substantially reduced the overestimation in the sum of predicted values of transmission line probability of failure that was present in the previously published model by a factor of 10. This has led to a reduction of model bias from 3352% to 14.46–15.43%. The model with the integrated approach and storm index demonstrates substantial improvements in the estimation of the probability of failure of transmission lines and their ranking by risk level. The improved model is able to capture 60% of the failures within the top 22.5% of the ranked power lines, compared to a value of 34.9% for the previous model. With an estimate of the probability of failure of transmission lines ahead of storms, power system planning and maintenance engineers will have critical information to make informed decisions, to create better mitigation plans and minimize power disruptions. Long term, this model can assist with resilience investments as it highlights areas of the system more susceptible to damage. |
| format | Article |
| id | doaj-art-eea9956cc530491fa87112cb801378f6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eea9956cc530491fa87112cb801378f62025-01-03T00:01:37ZengIEEEIEEE Access2169-35362025-01-0113425510.1109/ACCESS.2024.352341510816600Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution OutagesSita Nyame0https://orcid.org/0000-0001-9818-7659William O. Taylor1https://orcid.org/0000-0001-8713-5650William Hughes2https://orcid.org/0000-0001-9957-137XMingguo Hong3https://orcid.org/0000-0003-2333-4575Marika Koukoula4https://orcid.org/0000-0002-8437-7272Feifei Yang5https://orcid.org/0000-0002-4116-9967Aaron Spaulding6Xiaochuan Luo7https://orcid.org/0000-0001-7437-955XSlava Maslennikov8https://orcid.org/0000-0003-0011-0288Diego Cerrai9https://orcid.org/0000-0001-5918-4885Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USAOperations and Information Management Department, University of Connecticut, Storrs, CT, USADepartment of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USAISO New England Inc., Holyoke, MA, USAInstitute of Earth Surface Dynamics, University of Lausanne, Lausanne, SwitzerlandEversource Energy Center, University of Connecticut, Storrs, CT, USADepartment of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USAISO New England Inc., Holyoke, MA, USAISO New England Inc., Holyoke, MA, USADepartment of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USAUnderstanding and quantifying the impact of severe weather events on the electric transmission and distribution system is crucial for ensuring its resilience in the context of the increasing frequency and intensity of extreme weather events caused by climate change. While weather impact models for the distribution system have been widely developed during the past decade, transmission system impact models lagged behind because of the scarcity of data. This study demonstrates a weather impact model for predicting the probability of failure of transmission lines. It builds upon a recently developed model and focuses on reducing model bias, through multi-model integration, feature engineering, and the development of a storm index that leverages distribution system data to aid the prediction of transmission risk. We explored three methods for integrating machine learning with mechanistic models. They consist of: (a) creating a linear combination of the outputs of the two modeling approaches, (b) including fragility curves as additional inputs to machine learning models, and (c) developing a new machine learning model that uses the outputs of the weather-based machine learning model, fragility curve estimates, and wind data to make new predictions. Moreover, due to the limited number of historical failures in transmission networks, a storm index was developed leveraging a dataset of distribution outages to learn about storm behavior to improve model skills. In the current version of the model, we substantially reduced the overestimation in the sum of predicted values of transmission line probability of failure that was present in the previously published model by a factor of 10. This has led to a reduction of model bias from 3352% to 14.46–15.43%. The model with the integrated approach and storm index demonstrates substantial improvements in the estimation of the probability of failure of transmission lines and their ranking by risk level. The improved model is able to capture 60% of the failures within the top 22.5% of the ranked power lines, compared to a value of 34.9% for the previous model. With an estimate of the probability of failure of transmission lines ahead of storms, power system planning and maintenance engineers will have critical information to make informed decisions, to create better mitigation plans and minimize power disruptions. Long term, this model can assist with resilience investments as it highlights areas of the system more susceptible to damage.https://ieeexplore.ieee.org/document/10816600/Failure predictionmachine learningtransmission systemstructural modeling |
| spellingShingle | Sita Nyame William O. Taylor William Hughes Mingguo Hong Marika Koukoula Feifei Yang Aaron Spaulding Xiaochuan Luo Slava Maslennikov Diego Cerrai Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages IEEE Access Failure prediction machine learning transmission system structural modeling |
| title | Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages |
| title_full | Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages |
| title_fullStr | Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages |
| title_full_unstemmed | Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages |
| title_short | Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages |
| title_sort | transmission failure prediction using ai and structural modeling informed by distribution outages |
| topic | Failure prediction machine learning transmission system structural modeling |
| url | https://ieeexplore.ieee.org/document/10816600/ |
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