基于边界邻近指标聚类的可扩展电压稳定数据集生成
Scalable Voltage-Stability Dataset Generation Via Boundary-Proximity Indicators Clustering
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中文总结 AI 辅助
本文提出一种基于边界邻近指标聚类的可扩展电压稳定数据集生成框架，结合多种采样与聚类技术，可减少95.45%的连续潮流评估量，且能保持高准确率，适用于电力系统运行与规划优化。
中文摘要 AI 辅助
本文提出一种可扩展的电压稳定数据集生成框架。受电压稳定约束的规划日益依赖机器学习代理模型，但训练这些模型需要由连续潮流（CPF）结果标注的大型数据集，而这在计算上成本高昂。为解决该问题，本文提出的框架采用边界邻近指标的层次聚类，以减少所需的CPF评估次数。该方法结合了四项内容：（i）采用Hit-and-Run马尔可夫链蒙特卡罗对可行运行空间进行均匀采样；（ii）通过极大极小拉丁超立方采样（LHS）构建结构化应力方向；（iii）针对弱节点的灵敏度引导扰动；（iv）基于聚类的代表性CPF标注，即从聚类中心重建未标注运行点的电压稳定裕度。在IEEE 39节点系统上的结果表明，该框架将CPF评估次数显著减少了95.45%，同时在回归和分类任务中均保持了高准确率和边界保真度。缩减后的代理模型与全CPF数据集对应的代理模型在结构上保持一致，证明了该方法在运行与规划优化中的适用性和可扩展性。
英文摘要
This paper proposes a scalable framework for voltage-stability dataset generation. Voltage-stability-constrained planning increasingly relies on machine-learning surrogates but training them requires large datasets labelled by continuation power flow (CPF) results, which is computationally costly. To address this, this paper proposes a framework that uses hierarchical clustering on boundary-proximity indicators to reduce the number of required CPF evaluations. The proposed approach combines (i) uniform sampling of feasible operating space using Hit-and-Run Markov Chain Monte Carlo, (ii) structured stress directions via maximin Latin hypercube sampling (LHS), (iii) sensitivity-guided perturbations to target weak buses, and (iv) clustering-based representative CPF labelling that reconstructs the voltage stability margins of unlabelled operating points from representative cluster medoids. Results on the IEEE 39-bus system show that the proposed framework significantly reduces CPF evaluations by 95.45% while preserving high accuracy and boundary fidelity for both regression and classification tasks. The reduced surrogates remain structurally consistent with their full-CPF dataset counterparts, demonstrating the suitability and scalability of the proposed approach for operation and planning optimization.
