论文标题
电力系统中的深度神经网络基础大区域事件分类
Deep Neural Network based Wide-Area Event Classification in Power Systems
论文作者
论文摘要
本文在传输电网中介绍了广泛的事件分类。基于深神经网络（DNN）的分类器是根据时间同步的相量测量单元（PMU）的数据的可用性开发的。拟议的DNN经过使用贝叶斯优化培训，以寻找最佳的超参数。提议的事件分类的有效性通过美国传输网格的现实数据集进行了验证。该数据集包括线路中断，变压器中断，频率事件和振荡事件。验证过程还包括不同的PMU输出，例如电压幅度，角度，电流幅度，频率和频率变化速率（ROCOF）。仿真结果表明，ROCOF作为输入功能可提供最佳的分类性能。此外，还表明，经过较高采样率PMU和较大数据集的分类器具有更高的精度。
This paper presents a wide-area event classification in transmission power grids. The deep neural network (DNN) based classifier is developed based on the availability of data from time-synchronized phasor measurement units (PMUs). The proposed DNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset of the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency event, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The simulation results show that ROCOF as input feature gives the best classification performance. In addition, it is shown that the classifier trained with higher sampling rate PMUs and a larger dataset has higher accuracy.
