Multi-frequency sweeping method for periodic steady-state computations on the graphics processor unit

Abstract

This paper presents the parallelization of the multi-frequency hybrid backward/forward sweeping (BFS) technique on a graphics processor unit (GPU). Primarily, the intrinsic layer structure of a radial network, typical topology of distribution systems, and its multi-frequency behavior are exploited for parallelization of the hybrid BFS method on the GPU. The less computational demanding tasks, e.g., error computation and simple vectorized operations, are assigned to the CPU. The network solution is performed in the Matlab® environment using compute unified device architecture (CUDA). The computational time required by the GPU/CPU BFS implementation is compared with a CPU-only program by solving four networks of different sizes. Validation of the multi-frequency BFS results is made through a CPU implementation of a Newton-type solution scheme. The significant reduction in the computational time of the parallelized GPU implementation of the hybrid BFS method combined with its ability to include a wide range of frequencies and to handle nonlinear components makes it suitable for real-time online applications.