Geometric deep learning for online prediction of cascading failures in power grids

[1]  Yongxiang Xia,et al.  Resilience enhancement of renewable cyber-physical power system against malware attacks , 2022, Reliab. Eng. Syst. Saf..

[2]  Jared B. Garrison,et al.  Nexus-e: A platform of interfaced high-resolution models for energy-economic assessments of future electricity systems , 2022, Applied Energy.

[3]  Giovanni Sansavini,et al.  Identifying and assessing power system vulnerabilities to transmission asset outages via cascading failure analysis , 2022, Reliab. Eng. Syst. Saf..

[4]  Suzhen Li,et al.  Data-driven accident consequence assessment on urban gas pipeline network based on machine learning , 2021, Reliab. Eng. Syst. Saf..

[5]  G. Sansavini,et al.  Cascade-risk-informed transmission expansion planning of AC electric power systems , 2021, Electric Power Systems Research.

[6]  Donglei Sun,et al.  Identification method of cascading failure in high-proportion renewable energy systems based on deep learning , 2021, Energy Reports.

[7]  Enrico Zio,et al.  Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice , 2021, Reliab. Eng. Syst. Saf..

[8]  Xiao-Meng Zhang,et al.  Graph Neural Networks and Their Current Applications in Bioinformatics , 2021, Frontiers in Genetics.

[9]  Zhiwei Guo,et al.  A Deep Graph Neural Network-Based Mechanism for Social Recommendations , 2021, IEEE Transactions on Industrial Informatics.

[10]  Andreas Loukas,et al.  Building powerful and equivariant graph neural networks with structural message-passing , 2020, NeurIPS.

[11]  J. Kalita,et al.  A Survey of the Usages of Deep Learning for Natural Language Processing , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Chetan S. Kulkarni,et al.  Fusing Physics-based and Deep Learning Models for Prognostics , 2020, Reliab. Eng. Syst. Saf..

[13]  Xu Sun,et al.  Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View , 2019, AAAI.

[14]  Pierluigi Siano,et al.  A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges , 2019, Energies.

[15]  Giovanni Sansavini,et al.  Measuring Community and Multi-Industry Impacts of Cascading Failures in Power Systems , 2018, IEEE Systems Journal.

[16]  Nikolaos Doulamis,et al.  Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..

[17]  Surya Ganguli,et al.  On the Expressive Power of Deep Neural Networks , 2016, ICML.

[18]  Karl R. Weiss,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[19]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[20]  Faruk Kazi,et al.  Support-Vector-Machine-Based Proactive Cascade Prediction in Smart Grid Using Probabilistic Framework , 2015, IEEE Transactions on Industrial Electronics.

[21]  John Lygeros,et al.  A Probabilistic Framework for Reserve Scheduling and ${\rm N}-1$ Security Assessment of Systems With High Wind Power Penetration , 2013, IEEE Transactions on Power Systems.

[22]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[23]  Daniel S. Kirschen,et al.  Criticality in a cascading failure blackout model , 2006 .

[24]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[25]  I. Kamwa,et al.  Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance , 2005, IEEE Transactions on Power Systems.

[26]  V. E. Lynch,et al.  Critical points and transitions in an electric power transmission model for cascading failure blackouts. , 2002, Chaos.

[27]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[28]  Mohammad Reza Aghamohammadi,et al.  SVM based intelligent predictor for identifying critical lines with potential for cascading failures using pre-outage operating data , 2022, International Journal of Electrical Power & Energy Systems.

[29]  Ruqiang Yan,et al.  Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction , 2021, Reliab. Eng. Syst. Saf..

[30]  Joao P. S. Catalao,et al.  Decision tree analysis to identify harmful contingencies and estimate blackout indices for predicting system vulnerability , 2020 .