用约束信息主动采样，提升电力优化模型的泛化能力
Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies
- 基于优化问题中的约束集主动选择有代表性的训练样本
- 相同训练量下，泛化性能优于现有方法
- 适合电力系统优化与机器学习交叉研究者
本文研究优化代理模型，即通过机器学习（ML）训练以高效预测交流最优潮流（ACOPF）问题最优解的模型。尽管前景广阔，但其性能高度依赖于训练数据质量。为此，本文提出一种新型主动采样框架，用于构建更真实、多样化的训练数据。该框架主动探索反映实际运行条件的灵活问题设定，并利用优化特有的关键量——活动约束集，更好地捕捉影响最优解的核心特征。数值结果表明，在同等训练预算下，该方法显著优于现有采样策略，大幅提升了可信赖的ACOPF优化代理模型的性能。
原文摘要 · Abstract (English)
This paper studies optimization proxies, machine learning (ML) models trained to efficiently predict optimal solutions for AC Optimal Power Flow (ACOPF) problems. While promising, optimization proxy performance heavily depends on training data quality. To address this limitation, this paper introduces a novel active sampling framework for ACOPF optimization proxies designed to generate realistic and diverse training data. The framework actively explores varied, flexible problem specifications reflecting plausible operational realities. More importantly, the approach uses optimization-specific quantities (active constraint sets) that better capture the salient features of an ACOPF that lead to the optimal solution. Numerical results show superior generalization over existing sampling methods with an equivalent training budget, significantly advancing the state-of-practice for trustworthy ACOPF optimization proxies.
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