HUANG Ying;MA Binyu;WU Yajun;PAN Xiaojie;SHAO Dejun;SHI Mengxuan;ZHANG Mujie;

• 1:Central China Branch of State Grid Corporation of China
• 2:School of Electrical Engineering and Automation
• 3: Wuhan University

Abstract：

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.

Key Words： scenario variation;McDalNet;transient voltage stability;power system

Abstract:

Keywords:

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

Authors: HUANG Ying;MA Binyu;WU Yajun;PAN Xiaojie;SHAO Dejun;SHI Mengxuan;ZHANG Mujie;

DOI: 10.19585/j.zjdl.202603002

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