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OA

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.