使用基态机器学习模型加速交流潮流事故分析

中文摘要 AI 辅助

在日益多变的运行条件下，电网规划和运行需要对大量线路停运进行快速且准确的交流潮流（AC-PF）事故分析。虽然机器学习模型可以加速这些计算，但现有方法通常需要针对不同事故训练单独的模型，或使用来自多种拓扑结构的数据训练单一模型。这两种方法都会产生大量的离线数据生成和训练成本。为解决这一问题，本文提出了一种定点框架，该框架复用仅基于基态拓扑数据训练的单一机器学习模型，以预测任何非关键单线停运后的事故后交流潮流状态。我们在直流模型近似下分析了所提方法的收敛性，并根据网络参数推导了其收敛速率。作为附带结果，我们证明了非关键线路的功率传输分配因子（PTDF）的幅值小于1。我们进一步刻画了机器学习训练区域，以适应定点迭代中遇到的功率因数规格，并推导了事故后状态估计的机器学习预测误差界限。在6717节点合成德克萨斯系统上的数值测试表明，该方法在所有非关键单线停运情况下均能收敛，且预测误差与基态情况保持接近。所提出的框架在直流求解器的速度和牛顿-拉夫逊法的精度之间提供了有利的权衡。

英文摘要

Grid planning and operation under increasingly variable operating conditions require fast and accurate AC power flow (AC-PF) contingency analysis for numerous line outages. While ML models can accelerate these computations, existing approaches often require either training separate models for different contingencies or a single model using data from multiple topologies. Both approaches incur substantial offline data generation and training costs. To address this gap, this work proposes a fixed-point framework that reuses a single ML model trained exclusively on basecase topology data to predict post-contingency AC-PF states under any non-critical single-line outage. We analyze the proposed method's convergence under the DC model approximation and derive its convergence rate in terms of network parameters. As a side result, we show that power transfer distribution factors (PTDFs) for non-critical lines have magnitudes less than one. We further characterize the ML training region to accommodate PF specifications encountered during fixed-point iterations and derive bounds on ML prediction errors for post-contingency state estimates. Numerical tests on a 6,717-bus synthetic Texas system demonstrate convergence across all non-critical single-line outages, with prediction errors remaining close to those of basecase. The proposed framework offers a favorable tradeoff between the speed of DC solvers and Newton-Raphson accuracy.

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10 page, 7 figures