黄莹,马彬喻,吴亚骏,潘晓杰,邵德军,石梦璇,张慕婕
HUANG Ying,MA Binyu,WU Yajun,PAN Xiaojie,SHAO Dejun,SHI Mengxuan,ZHANG Mujie

• 1:国家电网有限公司华中分部
• 2:武汉大学电气与自动化学院

摘要(Abstract)：

针对电力系统运行场景频繁切换导致的暂态电压稳定评估模型泛化性下降问题，提出了一种基于改进McDalNet（多分类域对抗学习网络）的电力系统暂态电压稳定自适应评估方法。首先，采用Wasserstein距离构造McDalNet的损失函数，以更好地捕捉场景切换前后的域分布差异，并通过中心损失强化类内特征聚类，从而更好地区分不同类样本。然后，利用稳态、故障发生、故障清除3个采样时刻的特征训练特征提取器及标签分类器，构建适用于原始场景的高精度评估模型。最后，通过辅助分类器及少量目标域样本实现域对齐，从而自适应更新模型，使其适用于新场景的暂态电压稳定评估。算例分析表明，所提方法能够对齐源域与目标域的数据分布，有效提升电力系统运行场景多次切换后暂态电压稳定评估模型的泛化性和可持续学习能力。
To address the degradation in model generalization caused by frequent switching of power system operating scenarios in transient voltage stability assessment(TVSA) models, an adaptive assessment method based on a modified multi-class domain adversarial learning networks(McDalNet) is proposed. First, the modified McDalNet uses the Wasserstein distance to construct the loss function to more effectively capture domain distribution discrepancies before and after scenario switching, while a center loss is introduced to enhance intra-class feature clustering, thereby improving the separability of samples from different classes. Subsequently, the feature extractor and label classifier are trained using features from three sampling moments: steady-state, fault occurrence, and fault clearance, to build a high-precision assessment model for the original scenario. Finally, domain alignment is achieved through an auxiliary classifier and a small number of target-domain samples, enabling adaptive model updating so that it can be applied to TVSA in new scenarios. Case studies demonstrate that the proposed method can align the data distributions of the source and target domains, effectively enhancing the generalization performance and continual learning capability of TVSA models under multiple operating scenario transitions in power systems.

关键词(KeyWords)： 场景变化;多分类域对抗学习网络;暂态电压稳定;电力系统
scenario variation;McDalNet;transient voltage stability;power system

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司科技项目（521400250009）

作者(Author): 黄莹,马彬喻,吴亚骏,潘晓杰,邵德军,石梦璇,张慕婕
HUANG Ying,MA Binyu,WU Yajun,PAN Xiaojie,SHAO Dejun,SHI Mengxuan,ZHANG Mujie

DOI: 10.19585/j.zjdl.202603002

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