Power grid operation continuously undergoes state transitions caused by internal and external uncertainties, e.g., equipment failures and weather-driven faults, among others. This prompts an observation of different types of waveforms at the measurement points (substations) in power systems. Modern power systems utilize phasor measurement units (PMUs) and intelligent electronic devices embedded with PMU functionality to capture the corresponding peculiarities through synchrophasor measurements. However, existing PMU devices are equipped with only one synchrophasor estimation algorithm (SEA) and are, thus, not always robust to handle different types of signals across the network. This article proposes a PMU-embedded framework that ensures real-time grid surveillance and potentially enables adaptive selection of preinstalled SEAs in the PMU. Therefore, it ensures high-fidelity measurements at all times and irrespective of the input signals. Our proposed framework consists of: 1) a pseudocontinuous quadrature wavelet transform which generates the featured scalograms and 2) a convolutional neural network for event classification based on the extracted features in the scalograms. Our experiments demonstrate that the proposed framework achieves high classification accuracy on multiple types of prevailing events in power grids, through which an enhanced grid-scale situational awareness in real time can be realized.

中文翻译：

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通过 PMU 嵌入式卷积神经网络进行电网在线监控

电网运行不断经历由内部和外部不确定性引起的状态转换，例如设备故障和天气驱动的故障等。这促使在电力系统中的测量点（变电站）观察不同类型的波形。现代电力系统利用相量测量单元 (PMU) 和嵌入 PMU 功能的智能电子设备，通过同步相量测量来捕捉相应的特性。然而，现有的 PMU 设备仅配备一种同步相量估计算法 (SEA)，因此在处理网络上不同类型的信号时并不总是稳健的。本文提出了一种 PMU 嵌入式框架，该框架可确保实时电网监控，并可能实现对 PMU 中预装 SEA 的自适应选择。因此，无论输入信号如何，它都能始终确保高保真测量。我们提出的框架包括：1) 一个伪连续正交小波变换，它生成特征标度图和 2) 一个卷积神经网络，用于基于标度图中提取的特征进行事件分类。我们的实验表明，所提出的框架对电网中多种类型的主要事件实现了高分类精度，通过它可以实现实时增强的电网规模态势感知。1) 伪连续正交小波变换，它生成特征标度图和 2) 基于标度图中提取的特征进行事件分类的卷积神经网络。我们的实验表明，所提出的框架对电网中多种类型的主要事件实现了高分类精度，通过它可以实现实时增强的电网规模态势感知。1) 伪连续正交小波变换，它生成特征标度图和 2) 基于标度图中提取的特征进行事件分类的卷积神经网络。我们的实验表明，所提出的框架对电网中多种类型的主要事件实现了高分类精度，通过它可以实现实时增强的电网规模态势感知。