Transient stability assessment (TSA) based on security region is of great significance to the security of power systems. In this paper, we propose a novel methodology for the assessment of online transient stability margin. Combined with a geographic information system (GIS) and transformation rules, the topology information and pre-fault power flow characteristics can be extracted by 2D computer-vision-based power flow images (CVPFIs). Then, a convolutional neural network (CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency set. The network consists of two components: the classification network classifies the input samples into the credibly stable/unstable and uncertain categories, and the prediction network is utilized to further predict the generator stability indices of the categorized samples, which improves the network ability to distinguish between the samples with similar characteristics. The proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system, and the simulation results validate the effectiveness of the method.

中文翻译：

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基于安全域的数据驱动暂态稳定裕量预测方法

基于安全区域的暂态稳定评估对电力系统的安全具有重要意义。在本文中，我们提出了一种用于评估在线暂态稳定裕度的新颖方法。结合地理信息系统（GIS）和转换规则，可以通过基于2D计算机视觉的潮流图像（CVPFI）提取拓扑信息和故障前潮流特征。然后，构建了基于卷积神经网络（CNN）的综合网络，以映射预期偶然性条件下稳态潮流与发电机稳定性指标之间的关系。该网络由两个部分组成：分类网络将输入样本分类为可靠的稳定/不稳定和不确定的类别，利用预测网络进一步预测分类样本的生成器稳定性指标，提高了网络区分具有相似特征的样本的能力。该方法可用于快速，定量地评估电力系统的暂态稳定裕度，仿真结果验证了该方法的有效性。