图像化数据驱动的电力系统暂态稳定性在线评估方法

目前电力系统暂态稳定性评估（TSA）大多采用标准算例生成的数据集，然而实际电网的母线、发电机、线路等电力元件的数量巨大，难以实现评估模型的实时监视和在线更新；而现有降维方法常常遗漏重要信息，导致预测精度下降。提出一种图像化数据驱动的电力系统暂态稳定性在线评估方法，将输入时间序列重新排列成二维图像，利用二维主成分分析法（2D-PCA）对原始图像进行特征降维，并建立卷积神经网络（CNN）模型进行系统稳定性预测。在IEEE-39算例中进行验证，结果表明本文所提基于2D-PCA和CNN的TSA模型在保证预测精度的同时能够大幅提高训练效率，有望推进深度学习在电力系统暂态稳定性在线评估的应用。

Graphical Data-driven Online Assessment of Power System Transient Stability

Power system transient stability assessment （TSA） mostly uses the data set generated by standard examples at present. However， the number of power components such as buses， generators and lines in actual power grid is huge， which makes it difficult to realize real-time monitoring and online updating of the assessment model. Existing dimension reduction methods often omit important information， resulting in the decline of prediction accuracy. An online evaluation method of power system transient stability driven is proposed by image data，which rearranges the input time series into two-dimensional images， reduces the dimension of original images by using two-dimensional principal component analysis， and establishes a convolutional neural network model to predict the system stability. The verification results in IEEE-39 numerical example show that the evaluation model proposed in this paper based on two-dimensional principal component analysis and convolutional neural network can greatly improve the training efficiency while ensuring the prediction accuracy，and is expected to promote the application of in-depth learning in online evaluation of power system transient stability.