GPU-based matrix structure driven state estimation for large-scale power systems. (December 2021)

• Record Type: Journal Article
• Authors: Zhou, Gan; Hua, Jimin; Zhao, Jiahao; Feng, Yanjun; Yao, Yao; Fu, Meng
• Is Part Of: International journal of electrical power & energy systems. Volume 133(2021)
• Publication Date: 2021-12
• Subjects: State estimation -- Graphics processing unit -- Matrix structure driven strategy -- Parallel SPMM algorithm; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107298
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A matrix structure driven strategy for sparse matrix problems under the heterogeneous computing framework. A novel parallel algorithm designed for sparse matrix–matrix multiplication problem based on exploit of shared memory. A GPU-accelerated parallel state estimation algorithm for large-scale power system. 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.