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1. Learning Cascading Failure Interactions by Deep Convolutional Generative Adversarial Network

   https://doi.org/10.1109/SmartGridComm52983.2022.9961045  

   Huang, Shuchen; Qi, Junjian (October 2022, 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))

   In this paper, a cascading failure interaction learning method is proposed for real utility outage data. For better revealing the structure, we reorganize the failure interaction matrix based on Louvain community detection. A deep convolutional generative adversarial network (DCGAN) based method is then proposed to learn the implicit features for failure propagation in the interaction matrix. A systematic method is further developed to evaluate the performance of the learning method on missing interaction recovery and new interaction discovery. The effectiveness of the proposed method is validated on the 14-year real utility outage data from Bonneville Power Administration. 

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2. Hazard Resistance-Based Spatiotemporal Risk Analysis for Distribution Network Outages During Hurricanes

   https://doi.org/10.1109/TPWRS.2024.3469168  

   Xu, Luo; Lin, Ning; Xi, Dazhi; Feng, Kairui; Poor, H Vincent (September 2024, IEEE Transactions on Power Systems)

   In recent decades, blackouts have shown an increasing prevalence of power outages due to extreme weather events such as hurricanes. Precisely assessing the spatiotemporal outages in distribution networks, the most vulnerable part of power systems, is critical to enhancing power system resilience. The Sequential Monte Carlo (SMC) simulation method is widely used for spatiotemporal risk analysis of power systems during extreme weather hazards. However, it is found here that the SMC method can lead to large errors as it repeatedly samples the failure probability from the time-invariant fragility functions of system components in time-series analysis, particularly overestimating damages under evolving hazards with high-frequency sampling. To address this issue, a novel hazard resistance-based spatiotemporal risk analysis (HRSRA) method is proposed. This method converts the failure probability of a component into a hazard resistance and uses it as a time-invariant value in time-series analysis. The proposed HRSRA provides an adaptive framework for incorporating high-spatiotemporal-resolution meteorology models into power outage simulations. By leveraging the geographic information system data of the power system and a physics-based hurricane wind field model, the superiority of the proposed method is validated using real-world time-series power outage data from Puerto Rico, including data collected during Hurricane Fiona in 2022. 

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3. Fast Cascading Outage Screening based on Deep Convolutional Neural Network and Depth-First Search

   https://doi.org/10.1109/TPWRS.2020.2969956  

   Du, Yan; Li, Fangxing Fran; Zheng, Tongxin; Li, Jiang (July 2020, IEEE Transactions on Power Systems)

   In this paper, a data-driven method is proposed for fast cascading outage screening in power systems. The proposed method combines a deep convolutional neural network (deep CNN) and a depth-first search (DFS) algorithm. First, a deep CNN is constructed as a security assessment tool to evaluate system security status based on observable information.With its automatic feature extraction ability and the high generalization, a well-trained deep CNN can produce estimated AC optimal power flow (ACOPF) results for various uncertain operation scenarios, i.e., fluctuated load and system topology change, in a nearly computation-free manner. Second, a scenario tree is built to represent the potential operation scenarios and the associated cascading outages. The DFS algorithm is developed as a fast screening tool to calculate the expected security index value for each cascading outage path along the entire tree, which can be a reference for system operators to take predictive measures against system collapse. The simulation results of applying the proposed deep CNN and the DFS algorithm on standard test cases verify their accuracy, and the computational efficiency is thousands of times faster than the model-based traditional approach, which implies the great potential of the proposed algorithm for online applications. 

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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. “Optimized Asset Management in Distribution Systems Based on Predictive Risk Analysis

   T. Dokic, M. Kezunovic (January 2018, Energies)

   The paper introduces an optimal maintenance scheduler based on predictive assessment of risk of outage and equipment failure in distribution networks. The variety of severe weather conditions are observed and their impact on the network components is quantified. The equipment deterioration and failure rates are observed continuously across the space and time using heterogeneous data. The risk of weather-related outages for each component is generated in real-time, and can be extracted at multiple temporal and spatial scales depending on the application of interest. The optimal maintenance scheduling that minimizes the system risk while maintaining the economic investment limits is developed. The benefits of the framework are presented using a distribution network asset management example. 

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