Early Alarm: Robust Event Analysis for Power Systems using 1-D Fully Convolutional Network
Early Alarm: Robust Event Analysis for Power Systems using 1-D Fully Convolutional Network
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DOI：
10.1109/smartgridcomm57358.2023.10333935
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Chengcheng Li;Wei Wang;Zhihao Jiang;Lin Zhu;Jinyuan Sun;Yilu Liu;Hairong Qi
中科院分区：
文献类型：
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作者：
Chengcheng Li;Wei Wang;Zhihao Jiang;Lin Zhu;Jinyuan Sun;Yilu Liu;Hairong Qi
这项工作提出了一种新的深度学习模型，用于大规模电力系统中多类型事件的早期，准确和鲁棒的检测，识别和时间定位。所提出的方法开发了一个统一的1-D全卷积网络（FCN）模型，该模型将从电力系统测量的原始频率信号的时间序列作为输入，提取区别特征，并在时间序列中的每个时间点预测事件是否发生以及事件的类型是什么。与现有方法相比，该模型无需手工进行特征提取或复杂的数据预处理，可以灵活处理任意长度的输入信号，并精确推断事件发生时间。最重要的是，该模型是故意训练不完整的模式，使它更强大的部分特征的事件，这是常见的在现实世界中的在线识别，导致早期报警电力系统故障。大量的实验结果表明，所提出的方法实现了上级性能的国家的最先进的，也显示出较强的鲁棒性噪声和系统振荡。
This work presents a novel deep learning model for early, accurate, and robust detection, recognition, and temporal localization of multi-type events in large-scale power systems. The proposed method develops a unified 1-D fully convolutional network (FCN) model that takes time series of raw frequency signals measured from a power system as input, extracts distinguishing features, and predicts at every temporal point in the time series if an event is happening and what the type of the event is. Compared to existing methods, the proposed model eliminates the necessity for hand-crafted feature extraction or complicated data pre-processing, can flexibly handle input signals of arbitrary length, and precisely infer the event occurrence time. Most importantly, the model is intentionally trained with incomplete patterns, such that it is more robust to partial features of an event which is common in real-world online recognition, resulting in early alarm for power system failures. Extensive experimental results demonstrate that the proposed method achieves superior performance to the state-of-the-art, and also shows strong robustness to noise and system oscillations.