用强化学习优化电力系统初始解，显著提升复杂工况下的收敛性。
Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
- 基于扰动预测构建奖励模型，以迭代次数为监督信号进行微调
- 在多个电网规模测试中实现100%收敛且平均迭代次数最少
- 解决传统方法在电压崩溃附近失效的问题，适合电力系统优化场景
神经网络预热解可大幅减少求解交流潮流问题所需的牛顿-拉夫逊迭代次数，但现有监督方法在重载工况、接近电压崩溃时泛化能力差。我们证明了牛顿-拉夫逊迭代次数存在下界，该下界取决于初始误差的方向而非大小，且当潮流雅可比矩阵最小奇异值趋近零时，该下界变得无效，揭示了监督回归在鞍结分岔附近的失效机制。受此分析启发，我们提出Newton's Lantern微调框架，结合组相对策略优化与基于基础模型预测扰动的自学习奖励模型，直接以迭代次数作为监督信号。在IEEE 118节点、GOC 500节点和GOC 2000节点基准测试中，Newton's Lantern是唯一能在所有测试快照上收敛并达到最小均值迭代次数的方法。
原文摘要 · Abstract (English)
Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised approaches generalize poorly on heavily loaded instances near voltage collapse. We prove a lower bound on the Newton-Raphson iteration count that depends on the direction of the warm start error rather than on its magnitude, and show as a corollary that the bound becomes vacuous as the smallest singular value of the power-flow Jacobian shrinks, identifying the failure mode of supervised regression near the saddle-node bifurcation. Motivated by this analysis, we introduce Newton's Lantern, a finetuning pipeline that combines group relative policy optimization with a learned reward model trained on perturbations of the base model's predictions, using the iteration count itself as the supervisory signal. Across IEEE 118-bus, GOC 500-bus, and GOC 2000-bus benchmarks, Newton's Lantern is the only method that converges on every test snapshot while attaining the smallest mean iteration count.
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