论文标题
在兴奋不足的电网中可进行的学习
Tractable learning in under-excited power grids
论文作者
论文摘要
估计物理流网络（例如电网）的结构对于确保能源输送至关重要。本文讨论了“不足”制度中功率电网中的统计结构估计，其中一部分内部节点没有外部注射。基于淋巴电位或电压的事先估计算法在不足的状态下失败。我们提出了一种基于物理知识保护法的学习拓扑算法的新拓扑学习算法，用于学习未经充实的一般（非radial）网络。我们证明了我们的算法对于非吸引人的内部节点的网格的渐近正确性。更重要的是，我们从理论上分析了在噪声测量下的算法的功效，并确定保证渐近正确恢复的最大噪声的界限。我们的方法通过在带有实际注入数据的测试网格上生成的非线性电压样品的模拟来验证
Estimating the structure of physical flow networks such as power grids is critical to secure delivery of energy. This paper discusses statistical structure estimation in power grids in the "under-excited" regime, where a subset of internal nodes do not have external injection. Prior estimation algorithms based on nodal potentials or voltages fail in the under-excited regime. We propose a novel topology learning algorithm for learning underexcited general (non-radial) networks based on physics-informed conservation laws. We prove the asymptotic correctness of our algorithm for grids with non-adjacent under-excited internal nodes. More importantly, we theoretically analyze our algorithm's efficacy under noisy measurements, and determine bounds on maximum noise under which asymptotically correct recovery is guaranteed. Our approach is validated through simulations with non-linear voltage samples generated on test grids with real injection data
