电网技术2025，Vol.49Issue(2)：459-469,中插18-中插23,17.DOI:10.13335/j.1000-3673.pst.2024.0688
基于残差分组卷积神经网络和多级注意力机制的源荷极端场景辨识方法
Source-load Extreme Scenarios Recognition Based on GCNN-resnet Network and Multi-level Attention Mechanism
摘要
Abstract
To address the challenges posed by extreme weather events to the safe and stable operation of new power systems,it is imperative to consider extreme scenarios in the production simulation of the power grid.Traditional scenario generation methods cannot directly produce extreme scenarios due to the scarcity of historical extreme samples,necessitating the recognition of scenarios.To address this,the paper proposes an extreme scenario identification method that incorporates the bilateral aspects of source-load dynamics.Wind,photovoltaic(PV),and load sequences are initially reshaped and concatenated along the channel dimension.Subsequently,the temporal features of the scenario and the coupling features between the source-load scenarios are extracted using grouping convolution and deep residual networks.Furthermore,the model integrates a channel attention mechanism and a multi-head attention mechanism to assign greater significance to critical features and categorize the scenarios.Additionally,the issue of imbalanced datasets in the training samples is addressed by employing an improved loss function.Finally,validation is conducted using historical datasets.The results demonstrate the effectiveness of the proposed method in accurately classifying scenarios,particularly in identifying source-load extreme scenarios with risk of power supply guarantee or renewable energy consumption from the historical dataset.关键词
极端场景辨识/残差神经网络/分组卷积/注意力机制/源荷不确定性Key words
extreme scenario recognition/residual neural network/grouping convolution/attention mechanism/source and load uncertainty分类
信息技术与安全科学引用本文复制引用
郭红霞,李渊,陈凌轩,王建学,马骞..基于残差分组卷积神经网络和多级注意力机制的源荷极端场景辨识方法[J].电网技术,2025,49(2):459-469,中插18-中插23,17.基金项目
国家重点研发计划项目(2022YFB2403500).Project Supported by National Key Research and Development Program of China(2022YFB2403500). （2022YFB2403500）
