GPU-based matrix structure driven state estimation for large-scale power systems

Abstract

The rapid development of the power grid brings more computational burden to the online state monitoring of the power system. State estimation (SE), the key fundamental part as well as the cornerstone of other applications, requires urgent improvement in its computing efficiency. Recently, the graphics processing unit (GPU) provides potentials for computationally intensive tasks. This paper proposes a GPU-based matrix structure driven (MSD) strategy for the Weighted Least Squares (WLS) state estimator. In this scheme, structures of all the sparse matrices are determined on CPU in advance and numerical calculations are completed during each iteration, with carefully-tuned kernels on GPU. Besides, a novel parallel algorithm is designed to tackle the sparse matrix–matrix multiplication (SPMM) problem, where shared memory is exploited to a great extent for performance improvement. Case studies verify the superiority of the framework and results show that the proposed MSD-SE solution is 4.97 times faster than the CPU-based SE solution on a 27723-bus system.