面向暂态扰动分类的深度学习的PMU数据图像嵌入
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification
摘要：
提出了一种基于深度学习的电网扰动分类方法。一个真正的同步相量集组成的三种不同类型的干扰事件的频率监测网络（FNET）。一种称为Gramian角场的图像嵌入技术被应用于将事件数据的每个时间序列转换为用于学习的二维图像。对两种主要的数据挖掘算法CNN（卷积神经网络）和RNN（递归神经网络）与两种广泛使用的数据采掘工具支持向量机和决策树进行了测试和比较。试验结果表明，这两种DL算法在电力系统暂态扰动分类中的应用优于其他方法。
英文摘要：
This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.
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