Power Grid Cascading Failure Prediction Based on Transformer
Power Grid Cascading Failure Prediction Based on Transformer
复制标题
基于变压器的电网连锁故障预测
DOI：
10.1007/978-3-030-91434-9_15
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Lu, Haibing
中科院分区：
文献类型：
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作者：
Zhou, Tianxin;Li, Xiang;Lu, Haibing
智能电网可能容易受到攻击和事故，并且智能电网中的任何初始故障都可能由于连锁故障而发展为大停电。由于智能电网在现代社会中的重要性，保护它们免受连锁故障的影响至关重要。连锁故障仿真可以帮助识别最脆弱的输电线路，并指导保护规划中的优先顺序，因此是保护智能电网免受连锁故障影响的有效途径。然而，由于智能电网初始故障的方式非常多，因此不可能大规模模拟连锁故障，也不可能有效地识别最脆弱的线路。在本文中，我们的目标是：1)开发一种在规模上进行连锁故障模拟的方法；2)建立简化的、基于扩散的连锁故障模型，以支持对最脆弱线路的有效且理论上有界的识别。该方法首先在连锁故障和自然语言之间建立一种新的联系，然后利用自然语言处理中强大的转换器模型来学习连锁故障数据。我们训练的变压器模型在预测级联故障线路总数和识别最脆弱线路方面具有良好的准确性。我们还构建了基于变压器模型关注矩阵的独立级联(IC)扩散模型，以支持具有性能界限的有效脆弱性分析。
Smart grids can be vulnerable to attacks and accidents, and any initial failures in smart grids can grow to a large blackout because of cascading failure. Because of the importance of smart grids in modern society, it is crucial to protect them against cascading failures. Simulation of cascading failures can help identify the most vulnerable transmission lines and guide prioritization in protection planning, hence, it is an effective approach to protect smart grids from cascading failures. However, due to the enormous number of ways that the smart grids may fail initially, it is infeasible to simulate cascading failures at a large scale nor identify the most vulnerable lines efficiently. In this paper, we aim at 1) developing a method to run cascading failure simulations at scale and 2) building simplified, diffusion based cascading failure models to support efficient and theoretically bounded identification of most vulnerable lines. The goals are achieved by first constructing a novel connection between cascading failures and natural languages, and then adapting the powerful transformer model in NLP to learn from cascading failure data. Our trained transformer models have good accuracy in predicting the total number of failed lines in a cascade and identifying the most vulnerable lines. We also constructed independent cascade (IC) diffusion models based on the attention matrices of the transformer models, to support efficient vulnerability analysis with performance bounds.