论文标题
电力系统事件基于深度神经网络的识别，并带有信息加载
Power System Event Identification based on Deep Neural Network with Information Loading
论文作者
论文摘要
在线电源系统事件识别和分类对于增强传输系统的可靠性至关重要。在本文中，我们开发了一种基于深神经网络（DNN）的方法，通过利用数百个相量测量单元（PMU）的现实世界测量以及数千个事件的标签来识别和分类电源系统事件。将两种创新设计嵌入了基线模型中，以卷积神经网络（CNN）提高事件分类的准确性。首先，我们提出了一种基于图信号处理的PMU排序算法，以提高CNN的学习效率。其次，我们部署基于信息加载的正规化，以在DNN的记忆和概括之间取得适当的平衡。基于美国电力传输电网东部互连的现实数据集的数值研究结果表明，基于PMU的分类和基于信息加载的正则化技术的组合有助于拟议的DNN方法获得高度准确的事件识别和分类结果。
Online power system event identification and classification is crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical studies results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.
