传统基于人工经验制定的典型方式包含的运行场景多样性有限，难以全面表征高比例新能源电力系统复杂的运行边界，依此进行方式安排可能使系统存在安全风险盲区。对此提出了基于卷积神经网络和自注意力机制的典型运行方式提取方法。首先利用卷积神经网络构建自动编码器模型，智能提取高比例新能源电网运行变量间复杂的空间耦合关系。其次基于提取的电网运行特征引入特征聚类层，将其与自动编码器模型联合优化实现聚类。然后通过提出的新能源-负荷-传统能源组合模式指标及聚类效果评价指标表征运行方式聚类结果。最后对以类中心为代表的方式样本集进行拓展并开展静态安全评估。算例结果表明，该方法能够有效挖掘电网高维运行变量的空间相关性和复杂组合模式，在此基础上开展的安全校核有助于区别化表征不同方式间高比例新能源电力系统的安全风险，为新型电力系统典型运行方式的制定提供有力支持。

The traditional approach based on human experience has limited diversity in operating scenarios, making it difficult to fully characterize the complex operating boundaries of high proportion new energy power systems. Operation model arranging based on this approach may result in safety risk blind spots in the system. This article proposes a typical operation mode extraction method based on convolutional neural networks and self-attention mechanisms. Firstly, an autoencoder model is constructed using convolutional neural networks to intelligently extract complex spatial coupling relationships between variables in the operation of high proportion new energy grids. Secondly, based on the extracted operational characteristics of the power grid, a feature clustering layer is introduced, which is jointly optimized with an autoencoder model to achieve clustering. Then, the clustering results of the operation mode are characterized by the proposed indicators of the new energy-load-traditional energy combination mode and the clustering effect evaluation indicators. Finally, the sample set represented is expanded by class centers and static security assessments are conducted. The calculation results show that this method can effectively explore the spatial correlation and complex combination patterns of high-dimensional operating variables in the power grid. The safety verification carried out on this basis helps to differentiate and characterize the safety risks of high proportion new energy power systems between different modes, providing strong support for the formulation of typical operating modes of new power systems.