Assessment of Line Outage Prediction Using Ensemble Learning and Gaussian Processes During Extreme Meteorological Events
Climate change is increasing the occurrence of extreme weather events, such as intense windstorms, with a trend expected to worsen due to global warming. The growing intensity and frequency of these events are causing a significant number of failures in power distribution grids. However, understandi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Wind |
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| Online Access: | https://www.mdpi.com/2674-032X/4/4/17 |
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| author | Altan Unlu Malaquias Peña |
| author_facet | Altan Unlu Malaquias Peña |
| author_sort | Altan Unlu |
| collection | DOAJ |
| description | Climate change is increasing the occurrence of extreme weather events, such as intense windstorms, with a trend expected to worsen due to global warming. The growing intensity and frequency of these events are causing a significant number of failures in power distribution grids. However, understanding the nature of extreme wind events and predicting their impact on distribution grids can help and prevent these issues, potentially mitigating their adverse effects. This study analyzes a structured method to predict distribution grid disruptions caused by extreme wind events. The method utilizes Machine Learning (ML) models, including K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Decision Trees (DTs), Gradient Boosting Machine (GBM), Gaussian Process (GP), Deep Neural Network (DNN), and Ensemble Learning which combines RF, SVM and GP to analyze synthetic failure data and predict power grid outages. The study utilized meteorological information, physical fragility curves, and scenario generation for distribution systems. The approach is validated by using five-fold cross-validation on the dataset, demonstrating its effectiveness in enhancing predictive capabilities against extreme wind events. Experimental results showed that the Ensemble Learning, GP, and SVM models outperformed other predictive models in the binary classification task of identifying failures or non-failures, achieving the highest performance metrics. |
| format | Article |
| id | doaj-art-71cf829a6a39496286e9faf36ef8500c |
| institution | Kabale University |
| issn | 2674-032X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Wind |
| spelling | doaj-art-71cf829a6a39496286e9faf36ef8500c2024-12-27T14:59:41ZengMDPI AGWind2674-032X2024-11-014434236210.3390/wind4040017Assessment of Line Outage Prediction Using Ensemble Learning and Gaussian Processes During Extreme Meteorological EventsAltan Unlu0Malaquias Peña1Department of Electrical & Computer Engineering, University of Connecticut, Storrs, CT 06268, USADepartment of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06268, USAClimate change is increasing the occurrence of extreme weather events, such as intense windstorms, with a trend expected to worsen due to global warming. The growing intensity and frequency of these events are causing a significant number of failures in power distribution grids. However, understanding the nature of extreme wind events and predicting their impact on distribution grids can help and prevent these issues, potentially mitigating their adverse effects. This study analyzes a structured method to predict distribution grid disruptions caused by extreme wind events. The method utilizes Machine Learning (ML) models, including K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Decision Trees (DTs), Gradient Boosting Machine (GBM), Gaussian Process (GP), Deep Neural Network (DNN), and Ensemble Learning which combines RF, SVM and GP to analyze synthetic failure data and predict power grid outages. The study utilized meteorological information, physical fragility curves, and scenario generation for distribution systems. The approach is validated by using five-fold cross-validation on the dataset, demonstrating its effectiveness in enhancing predictive capabilities against extreme wind events. Experimental results showed that the Ensemble Learning, GP, and SVM models outperformed other predictive models in the binary classification task of identifying failures or non-failures, achieving the highest performance metrics.https://www.mdpi.com/2674-032X/4/4/17extreme weather eventspower distribution gridsmachine learningline outage predictiongrid resilienceensemble learning |
| spellingShingle | Altan Unlu Malaquias Peña Assessment of Line Outage Prediction Using Ensemble Learning and Gaussian Processes During Extreme Meteorological Events Wind extreme weather events power distribution grids machine learning line outage prediction grid resilience ensemble learning |
| title | Assessment of Line Outage Prediction Using Ensemble Learning and Gaussian Processes During Extreme Meteorological Events |
| title_full | Assessment of Line Outage Prediction Using Ensemble Learning and Gaussian Processes During Extreme Meteorological Events |
| title_fullStr | Assessment of Line Outage Prediction Using Ensemble Learning and Gaussian Processes During Extreme Meteorological Events |
| title_full_unstemmed | Assessment of Line Outage Prediction Using Ensemble Learning and Gaussian Processes During Extreme Meteorological Events |
| title_short | Assessment of Line Outage Prediction Using Ensemble Learning and Gaussian Processes During Extreme Meteorological Events |
| title_sort | assessment of line outage prediction using ensemble learning and gaussian processes during extreme meteorological events |
| topic | extreme weather events power distribution grids machine learning line outage prediction grid resilience ensemble learning |
| url | https://www.mdpi.com/2674-032X/4/4/17 |
| work_keys_str_mv | AT altanunlu assessmentoflineoutagepredictionusingensemblelearningandgaussianprocessesduringextrememeteorologicalevents AT malaquiaspena assessmentoflineoutagepredictionusingensemblelearningandgaussianprocessesduringextrememeteorologicalevents |