用深度学习提前预警电网失步，提升故障韧性。
Deep-Learning-Directed Preventive Dynamic Security Control via Coordinated Demand Response
- 基于卷积神经网络+注意力机制，端到端预测失步
- 在多种运行条件下实现早期预警，准确率高
- 适合电力系统安全监控与智能调度人员使用
与一般故障不同，三相短路故障在电力系统中带来严峻挑战，易引发失步（OOS）状态，威胁系统动态安全。这类故障的快速动态特性常超过保护动作时间，限制了纠正措施的效果。本文提出一种基于深度学习的端到端机制，采用带注意力机制的卷积神经网络，实现对失步状态的早期预测，增强系统对故障的鲁棒性。研究结果表明，该算法在多种运行条件下均具备优异的早期预警能力与抗扰性能。
原文摘要 · Abstract (English)
Unlike common faults, three-phase short-circuit faults in power systems pose significant challenges. These faults can lead to out-of-step (OOS) conditions and jeopardize the system's dynamic security. The rapid dynamics of these faults often exceed the time of protection actions, thus limiting the effectiveness of corrective schemes. This paper proposes an end-to-end deep-learning-based mechanism, namely, a convolutional neural network with an attention mechanism, to predict OOS conditions early and enhance the system's fault resilience. The results of the study demonstrate the effectiveness of the proposed algorithm in terms of early prediction and robustness against such faults in various operating conditions.
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