融合注意力机制和卷积神经网络的电网暂态电压稳定评估及可解释性分析

Transient Voltage Stability Assessment and Interpretability Analysis of Power Grids Based on the Fusion of Attention Mechanism and Convolutional Neural Network

提升复杂多变运行场景下电网稳定评估的时效性和准确性,提出一种融合注意力机制和卷积神经网络(convolutional neural network,CNN)的暂态电压稳定评估及可解释性分析方法。首先,采用卷积块注意力模块(convolutional block attention module,CB AM)提升传统CNN的特征捕获能力,考虑模型特性和网络结构设计CBAMCNN组合模块。其次,建立基于CBAM-CNN的电网暂态电压稳定评估模型,揭示运行工况多变场景下系统关键电气量和稳定状态之间的映射关系。最后,基于沙普利值加性解释(Shapley additive explanations,SHAP)理论提出数据驱动模型评估结果的可解释性分析框架,提炼影响样本稳定状态的主导特征,评估各输入特征量对模型输出结果的贡献程度。在典型受端电网仿真系统中验证了所提稳定评估方法的准确性和可解释性分析方法的有效性。

To improve the timeliness and accuracy of power grid stability assessment in complex and variable operating scenarios,a transient voltage stability assessment and interpretability analysis method integrating Attention and convolutional neural networks(CNN)is proposed.Firstly,a convolutional block attention module(CBAM)is adopted to enhance the feature capture capability of CNN,and a CBAM-CNN module is designed considering the model characteristics and network structure.Secondly,based on CBAM-CNN,the transient voltage stability assessment model is established to reveal the mapping relationship between key electrical variables and stable states of the power system under various operating conditions.Finally,based on the Shapley additive interpretations(SHAP)theory,an interpretability analysis framework for data-driven model evaluation results is established.The dominant features affecting the stable state of samples are extracted,and the influence of each input feature on the model's predictions is assessed.The accuracy of the proposed transient voltage stability assessment method and the effectiveness of the interpretability analysis method were verified in typical receiving-end power grids with voltage instability issues.