Learning Cascading Failure Interactions by Deep Convolutional Generative Adversarial Network
Learning Cascading Failure Interactions by Deep Convolutional Generative Adversarial Network
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DOI：
10.1109/smartgridcomm52983.2022.9961045
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Shuchen Huang;Junjian Qi
中科院分区：
文献类型：
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作者：
Shuchen Huang;Junjian Qi
针对真实的电力系统停电数据，提出了一种连锁故障交互学习方法。为了更好地揭示结构，我们重组的基础上Louvain社区检测的故障交互机制。然后提出了一种基于深度卷积生成对抗网络（DCGAN）的方法来学习交互矩阵中故障传播的隐式特征。一个系统的方法，进一步发展，以评估性能的学习方法上丢失的交互恢复和新的交互发现。在Bonneville电力局14年真实的停电数据上验证了该方法的有效性。
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 ma-trix 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.