AI 中文总结
针对并网电网交互逆变器暂态稳定分析的保守性问题，该文提出概率主动学习框架，结合神经李雅普诺夫函数与高斯过程代理，将认证稳定域体积最高扩大20倍，且仿真查询量远少于穷举评估。
AI 中文摘要
基于逆变器的资源持续接入，结合大规模逆变器部署产生的复杂动态，暂态稳定分析对电力系统现代化愈发重要。然而，解析暂态稳定分析方法始终给出过于保守的稳定边界估计，制约电网调度与运行灵活性。尽管近期神经李雅普诺夫方法试图扩大这些稳定边界以获得保守性更低的估计，但由于分布外问题，它们仍局限于受限域内。为突破这一瓶颈，本文提出一种概率主动学习框架：从神经李雅普诺夫函数认证的确定性内部稳定域出发，该框架构建高斯过程代理并部署不确定性引导的边界搜索，通过智能耦合电磁暂态仿真与主动边界探索，系统地将估计的稳定边界向外扩展。针对多机电网支撑基准的综合评估表明，所提框架大幅降低了估计保守性；在最多含4台互联电网支撑逆变器的测试系统中，该方法较经典基准实现了认证稳定域体积最高达20倍的扩大，同时每个系统最多仅需220次时域仿真查询，远少于最简单的单逆变器基准所需的1600次穷举电磁暂态评估查询。
英文摘要
The continuous integration of inverter-based resources makes transient stability analysis increasingly important for power system modernization, in light of the intricate dynamics arising from large-scale inverter deployment. However, analytical transient stability analysis methods consistently yield overly conservative stability boundary estimates, which constrain grid dispatch and operational flexibility. Although recent neural Lyapunov methods attempt to enlarge these stability boundaries to obtain less conservative estimates, they remain trapped within restricted domains due to the out-of-distribution problem. To break this bottleneck, this paper proposes a probabilistic active learning framework. Starting from a deterministic inner stability region certified by neural Lyapunov functions, the framework constructs a Gaussian process surrogate and deploys an uncertainty-guided frontier search. By intelligently coupling electromagnetic transient simulations with active boundary exploration, the algorithm systematically drives the estimated stability boundary outward. Comprehensive evaluations across multi-machine grid-forming benchmarks demonstrate that the proposed framework substantially reduces estimation conservatism. Across test systems ranging up to four interconnected grid-forming inverters, the methodology achieves up to a $20$-fold volumetric enlargement of the certified stability region over classical baselines, while requiring at most 220 time-domain simulation queries per system, far fewer than the 1,600 queries that exhaustive EMT evaluation demands even for the simplest single-inverter benchmark.
