Mycelium：一种面向配电系统的可泛化跨网格多任务模型
Mycelium: A Generalizable Cross-Grid Multi-Task Model for Electrical Distribution Systems
AI总结：
针对配电网络异构数据推理难题，提出统一网格本体与物理模拟流水线，构建异构图变换器Mycelium，在未见网络上多数指标超越任务特定基线，实现跨任务泛化。
AI中文摘要：
配电网格的运营需要从稀疏、嘈杂且不完整的时间序列测量中，对异构网络进行推理。在这项工作中，我们识别了挑战，并探索了构建一个统一模型的解决方案，该模型能够执行基于电网物理原理的多样化任务，并泛化到未见过的配电网络。我们定义了一个统一的网格本体，将可变规模的配电网络表示为异构图，同时保留跨网络的原始拓扑、资产类型和电气关系。我们开发了一个基于物理的数据模拟流水线，结合参考网络和程序生成的配电网络，包含网络重构、故障场景和可配置的传感条件。我们提出了Mycelium，一种异构图变换器，具有结构感知的通信边和编码网络位置及标称相位方向的电气参考特征，以及生成每任务输出的任务特定时间读出。我们在参考网格和合成网格上训练Mycelium，并研究其在完全排除于训练和验证之外的基准网络上的泛化能力。观察到Mycelium在大多数报告的基准指标上优于任务特定的神经基线。架构消融和上述研究揭示了Mycelium学习底层物理表示的能力，这有助于增强跨任务性能，从而解决了统一网格模型中的一个重大挑战。
英文摘要：
Electrical distribution grid operations require inference across heterogeneous networks from sparse, noisy, and incomplete time series measurements. In this work, we identify challenges and explore solutions towards a unified model that can perform diverse tasks grounded in the physics of the electric grid and generalize to unseen distribution networks. We define a unified grid ontology that represents variable sized distribution networks as heterogeneous graphs while preserving native topology, asset types, and electrical relationships across networks. We develop a physics based data simulation pipeline that combines reference and procedurally generated distribution networks with network reconfigurations, fault scenarios, and configurable sensing conditions. We present Mycelium, a heterogeneous graph transformer with structure aware communication edges and electrical reference features that encode network position and nominal phase orientation, together with task specific temporal readouts which generate per task outputs. We train Mycelium on reference as well as synthetic grids, and study its generalization on benchmark networks completely excluded from training and validation. Mycelium is observed to outperform task specific neural baselines on most reported benchmark metrics. Architectural ablations and the aforementioned studies reveal Mycelium's capability to learn representations of the underlying physics which serves to enhance cross-task performance, thereby addressing a significant challenge in unified grid models.
