浙江电力2026，Vol.45Issue(1)：23-33,11.DOI:10.19585/j.zjdl.202601003
基于多对抗迁移学习的暂态稳定评估模型
A transient stability assessment model based on multi-adversarial transfer learning
摘要
Abstract
Transfer learning has been introduced to power system transient stability assessment(TSA)to expand scenario coverage.However,when transferring the classification boundary knowledge from known faults to potential fault assessments,existing methods often exhibit low accuracy for critical samples in the target domain.To address this,this paper proposes a multi-adversarial transfer learning model with multi-domain discriminators.By incorpo-rating fault severity indices as prior knowledge,fault samples are subdivided into four classes.Multiple domain dis-criminators then align these four sample categories between source and target domains.Through a multi-adversarial adaptation framework,granular alignment of sample distribution is achieved.This approach significantly improves the assessment accuracy for critical samples in the target domain while enhancing the model's positive transfer capa-bility.Simulation results on the IEEE 39-bus system and a regional power grid validate the effectiveness of the pro-posed method.关键词
暂态稳定评估/迁移学习/对抗迁移/多域鉴别器/故障严重程度Key words
TSA/transfer learning/adversarial transfer/multi-domain discriminator/fault severity引用本文复制引用
卢国强,李剑,王亦婷,肖智伟,王怀远..基于多对抗迁移学习的暂态稳定评估模型[J].浙江电力,2026,45(1):23-33,11.基金项目
福建省自然科学基金(2022J01113) （2022J01113）
国网青海省电力有限公司科技项目(522800230001) （522800230001）
