🤖 AI Summary
This study addresses the challenges of heterogeneous, sparse data and cross-network generalization in power distribution grids by proposing Mycelium, a heterogeneous graph Transformer. Methodologically, the authors construct a unified grid ontology and a physics-based simulation pipeline to generate high-quality training data. The model introduces structure-aware communication edges and electrical reference feature encoding, integrated with task-specific temporal readout mechanisms, to enable physics-driven, cross-grid universal representation learning. Experimental results demonstrate that Mycelium surpasses specialized baselines on unseen benchmark networks, substantially improving multi-task inference performance and cross-domain generalization capabilities.
📝 Abstract
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.