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Weather-related power disruptions present significant challenges to public infrastructure, societal well-being, and the distribution grid. Predicting outage durations in distribution grids is another challenge compared to transmission line outage durations due to distribution networks’ complexity and finer granularity. While forecasting forced power outages is crucial, accurately estimating their duration is essential for timely response and mitigation measures. This study introduces the Spatiotemporal Multiplex Network (SMN-WVF), a methodology designed to predict power outage durations across varying lead times, tackling the difficulties posed by small, high-complexity spaces within distribution grids. SMN-WVF employs multiplex networks that incorporate multi-modal data across both time and space, including layers such as power outages, weather conditions, weather forecasts, vegetation, and distances between substations. We demonstrate the importance of incorporating additional layers of data sources as they are shown to help the model’s predictions through gradual improvement in the macro F1 score performance. 

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. 

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.