PMU-data-driven Event Classification in Power Transmission Grids
PMU-data-driven Event Classification in Power Transmission Grids
复制标题
输电网中 PMU 数据驱动的事件分类
DOI：
10.1109/isgt49243.2021.9372227
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
V. Centeno
中科院分区：
文献类型：
--
作者：
I. Niazazari;Yunchuan Liu;Amir Ghasenikhani;Shuchismita Biswas;H. Livani;Lei Yang;V. Centeno
提出了一种输电网事件分类方法。提出了一种基于卷积神经网络(CNN)的分类器，用于获取来自相量测量单元(PMU)的时间同步数据流的时间相似性。使用贝叶斯优化对所提出的CNN进行训练，以搜索最佳的超参数。通过美国电网的真实数据集验证了所提出的事件分类的有效性。该数据集包括线路停运、变压器停运、频率和振荡事件。验证过程还包括不同的PMU输出，如电压幅度、相角、电流幅度、频率和频率变化率(ROCOF)。结果表明，与其他PMU输出相比，ROCOF具有最好的分类性能。此外，实验还表明，用较大的数据集训练的分类器具有较高的准确率。此外，通过与其他分类方法的比较，验证了该方法的优越性。
This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN 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 from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.