浙江电力2026，Vol.45Issue(3)：17-29,13.DOI:10.19585/j.zjdl.202603002
基于改进McDalNet的电力系统暂态电压稳定自适应评估方法
An adaptive transient voltage stability assessment method for power systems based on a modified McDalNet
摘要
Abstract
To address the degradation in model generalization caused by frequent switching of power system operat-ing 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 discrep-ancies before and after scenario switching,while a center loss is introduced to enhance intra-class feature cluster-ing,thereby improving the separability of samples from different classes.Subsequently,the feature extractor and la-bel 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 up-dating 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
场景变化/多分类域对抗学习网络/暂态电压稳定/电力系统Key words
scenario variation/McDalNet/transient voltage stability/power system引用本文复制引用
黄莹,马彬喻,吴亚骏,潘晓杰,邵德军,石梦璇,张慕婕..基于改进McDalNet的电力系统暂态电压稳定自适应评估方法[J].浙江电力,2026,45(3):17-29,13.基金项目
国家电网有限公司科技项目(521400250009) （521400250009）
