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1. Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission

   T. Dokic, M. Pavlovski (January 2019, The Hawaii International Conference on System Sciences – HICSS, Maui, Hawaii, January 2019)

   This paper describes the implementation of a prediction model for real-time assessment of weather related outages in the electric transmission system. The network data and historical outages are correlated with a variety of weather sources in order to construct the knowledge extraction platform for accurate outage probability prediction. An extension of the logistic regression prediction model that embeds the spatial configuration of the network was used for prediction. The results show that the developed model manifests high accuracy and is able to differentiate an outage area from the rest of the network in 1 to 3 hours before the outage. The prediction model is integrated inside a weather testbed for real-time mapping of network outage probabilities based on incoming weather forecast. 

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2. The Most Frequent N-k Line Outages Occur in Motifs That Can Improve Contingency Selection

   https://doi.org/10.1109/TPWRS.2023.3249825  

   Zhou, Kai; Dobson, Ian; Wang, Zhaoyu (January 2024, IEEE Transactions on Power Systems)

   Multiple line outages that occur together show a variety of spatial patterns in the power transmission network. Some of these spatial patterns form network contingency motifs, which we define as the patterns of multiple outages that occur much more frequently than multiple outages chosen randomly from the network. We show that choosing N-k contingencies from these commonly occurring contingency motifs accounts for most of the probability of multiple initiating line outages. This result is demonstrated using historical outage data for two transmission systems. It enables N-k contingency lists that are much more efficient in accounting for the likely multiple initiating outages than exhaustive listing or random selection. The N-k contingency lists constructed from motifs can improve risk estimation in cascading outage simulations and help to confirm utility contingency selection. 

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3. Early Prediction of Power Outage Duration Through Hierarchical Spatiotemporal Multiplex Networks

   https://doi.org/10.1007/978-3-031-82435-7_26  

   Aljurbua, Rafaa; Alshehri, Jumanah; Gupta, Shelly; Alharbi, Abdulrahman; Obradovic, Zoran (January 2025, Springer Nature Switzerland)

   Cherifi, H; Donduran, M; Rocha, LM; Cherifi, C; Varol, O (Ed.)

   Long power outages caused by weather can have a big impact on the economy, infrastructure, and quality of life in affected areas. It’s hard to provide early and accurate warnings for these disruptions because severe weather often leads to missing weather recordings, making it difficult to make learning-based predictions. To address this challenge, we have developed HMN-RTS, a hierarchical multiplex network that classifies disruption severity by temporal learning from integrated weather recordings and social media posts. This new framework’s multiplex network layers gather information about power outages, weather, lighting, land cover, transmission lines, and social media comments. Our study shows that this method effectively improves the accuracy of predicting the duration of weather-related outages. The HMN-RTS model improves 3 h ahead outage severity prediction, resulting in a 0.76 macro F1-score vs 0.51 for the best alternative for a five-class problem formulation. The HMN-RTS model provides useful predictions of outage duration 6 h ahead, enabling grid operators to implement outage mitigation strategies promptly. The results highlight the HMN-RTS’s ability to offer early, reliable, and efficient risk assessment. 

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4. State of Risk Prediction for Management and Mitigation of Vegetation and Weather Caused Outages in Distribution Networks

   https://doi.org/10.1109/ACCESS.2023.3324609  

   Baembitov, Rashid; Kezunovic, Mladen (January 2023, IEEE Access)

   The paper proposes a novel approach for the outage State of Risk (SoR) assessment caused by weather and vegetation in the distribution grid. Machine Learning prediction algorithm is used in conjunction with GIS application for mapping the SoR for the entire network. The proposed optimization approach leads to the specification of the mitigation strategies that utility staff and customers can coordinate to minimize the impact of outages. The resulting SoR assessment enables the implementation of an innovative decision- making solution for utility operators, represented in the form of risk maps. Additionally, utilizing the SoR assessments, a Customer Notification System (CNS) is introduced to enhance customer awareness and facilitate the adoption of mitigation measures. This holistic approach shifts outage management from a reactive process to a proactive initiative, promoting grid resilience and reliability through planned outage mitigation. 

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5. Future crop risk estimation due to drought, extreme temperature, hail, lightning, and tornado at the census tract level in Louisiana

   https://doi.org/10.3389/fenvs.2022.919782  

   Mostafiz, Rubayet Bin; Rohli, Robert V.; Friedland, Carol J.; Gall, Melanie; Bushra, Nazla (August 2022, Frontiers in Environmental Science)

   Louisiana is one of the most hazard-prone states in the U.S., and many of its people are engaged directly or indirectly in agricultural activities that are impacted by an array of weather hazards. However, most hazard impact research on agriculture to date, for Louisiana and elsewhere, has focused on floods and hurricanes. This research develops a method of future crop loss risk assessment due to droughts, extreme low and high temperatures, hail, lightning, and tornadoes, using Louisiana as a case study. This approach improves future crop risk assessment by incorporating historical crop loss, historical and modeled future hazard intensity, cropland extent, population, consumer demand, cropping intensity, and technological development as predictors of future risk. The majority of crop activities occurred and will continue to occur in south-central and northeastern Louisiana along the river basins. Despite the fact that cropland is decreasing across most of the state, weather impacts to cropland are anticipated to increase substantially by 2050. Drought is by far the costliest among the six hazards, accounting for $56.1 million of $59.2 million (∼95%) in 2050-projected crop loss, followed by extreme cold ($1.4 million), extreme heat ($1.0 million), tornadoes ($0.4 million), hail ($0.2 million), and lightning ($0.05 million), respectively. These findings will assist decision-makers to minimize risk and enhance agricultural resilience to future weather hazards, thereby strengthening this economically-important industry in Louisiana and enhancing food security. 

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