中国电机工程学报2025，Vol.45Issue(17)：6683-6695,中插8,14.DOI:10.13334/j.0258-8013.pcsee.240396
机理与生成对抗网络融合的谐波状态估计方法
Harmonic State Estimation Method Combining Mechanism and Generative Adversarial Networks
摘要
Abstract
When the power grid presents an unobservable state due to insufficient measurement configuration,traditional state estimation methods fail to accurately detect the power grid's harmonic distribution.Therefore,this paper proposes a harmonic state estimation method,which combines the mechanism of harmonic propagation and generative adversarial networks(GAN)to estimate the harmonic state of unobservable nodes.Firstly,the unobservable region is simplified using the network topology equivalent method,and the harmonic transfer equations between the state variables of unobservable nodes and the virtual state variables of boundary nodes are derived,which is used as the basis for the integration of the mechanism and GAN.Secondly,a GAN-based harmonic state estimation model is constructed,which formulates a loss function based on harmonic state equations and transfer equations.The loss function incorporates measurement residuals and the mean square error of virtual state quantities at boundary nodes as penalization terms,thereby refining the training process of the model via harmonic equations.Furthermore,a residual model incorporating attention mechanisms is used to improve the structure of generator,and the convolutional neural networks are employed to improve the the structure of discriminator.Besides,the patch GAN is utilized for local data discrimination,so the feature mining capabilities of the model can be enhanced.Finally,the effectiveness of the proposed method is validated through simulation tests on the IEEE 33-node system.关键词
谐波状态估计/机理-数据融合/网络拓扑等效/生成对抗网络/可观性分析Key words
harmonic state estimation/mechanism-data integration/network topology equivalence/generative adversarial networks/observability analysis分类
信息技术与安全科学引用本文复制引用
邵振国,谢雨寒,林俊杰,陈飞雄..机理与生成对抗网络融合的谐波状态估计方法[J].中国电机工程学报,2025,45(17):6683-6695,中插8,14.基金项目
国家自然科学基金项目(52377087).Project Supported by National Natural Science Foundation of China(52377087). （52377087）
