A High-Performance Factorization Method for High-Order Heterogeneous Graph Tensors From Large-Scale Power Grid

The modern power grid has evolved into a large-scale, cyber-physical system with complex interactions among heterogeneous entities. Accurate modeling and analysis are essential, yet grid data often suffer from missing values due to sensor failures and communication issues. Tensor factorization is a powerful tool for capturing high-order dependencies and enabling data imputation, but existing methods face scalability and synchronization bottlenecks on large-scale heterogeneous tensors. To address these challenges, this paper proposes a high-performance tensor factorization framework for large-scale power grid data. The approach represents monitoring signals as structured high-order tensors, adopts a 3D block partitioning strategy for efficient distribution, and designs a lock-free scheduling scheme for stochastic updates to minimize synchronization overhead. Complexity analysis confirms the method's efficiency in both time and memory. Experiments on four real-world power grid datasets demonstrate superior scalability and efficiency performance compared with state-of-the-art methods.