电力系统保护与控制2026，Vol.54Issue(10)：71-81,11.DOI:10.19783/j.cnki.pspc.251250
用于电力系统暂态稳定评估的半监督指导的两阶段降维可视化方法
A semi-supervised guided two-stage dimensionality reduction and visualization method for power system transient stability assessment
摘要
Abstract
To address the issues of poor separability and unclear intrinsic correlations of high-dimensional samples in existing data-driven transient stability assessment methods,a semi-supervised guided two-stage dimensionality reduction and visualization method is proposed.First,a pseudo-label generation method based on dual-index confidence and hierarchical clustering guidance is used to screen unlabeled samples with high confidence.Then,by incorporating a semi-supervised modification into the distance metric of modified t-distributed stochastic neighbor embedding(t-SNE),the method effectively separates samples of different categories while preserving the intrinsic structure of the data to the greatest extent.Finally,pairwise controlled manifold approximation projection(PaCMAP)is applied to map high-dimensional samples into a two-dimensional visualization space.The proposed method enables high-dimensional samples representing power system operating states and their stability to be projected into a 2D plane.For unknown operating conditions,their positions and stability status can be quickly determined based on the distribution of known operating point.Verification results on a large-scale system with tens of thousands of nodes and on a real-world power grid demonstrate the effectiveness of the proposed method.关键词
暂态稳定评估/两阶段降维/半监督/t-SNE/PaCMAP/可视化Key words
transient stability assessment/two-stage dimensionality reduction/semi-supervision/t-SNE/PaCMAP/visualization引用本文复制引用
李融熹,于之虹,王梓淦,解梅,韦笑..用于电力系统暂态稳定评估的半监督指导的两阶段降维可视化方法[J].电力系统保护与控制,2026,54(10):71-81,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.U2166601). 国家自然科学基金项目资助(U2166601) （No.U2166601）
