A GPU-accelerated Lu factorization method for power flow Jacobian matrices

Abstract

The invention discloses a GPU accelerated power flow jacobian matrix LU decomposition method. The method includes the steps that LU symbol decomposition is carried out on a jacobian matrix J in a CPU to obtain sparse structures of a lower triangle transformation matrix L and an upper triangle matrix U, and the sparse structure of J is equal to L+U after symbol decomposition; according to the sparse structure of the matrix U, all columns of the matrix J are layered in a parallelization mode, and data needed by calculation is transmitted to a GPU; a layered LU decomposition kernel function Sparse LU is calculated in the GPU in the sequence of level progressive increase. The mode of combining program process controlling and basic data processing through the CPU with dense floating-point calculation processing through the GPU is used for improving the efficiency of power flow jacobian matrix LU decomposition, and the problem that flow calculation consumes time greatly in electric system static safety analysis is solved.

Description

technical field [0001] The invention belongs to the application field of high-performance computing in electric power systems, and in particular relates to an LU decomposition method of a GPU-accelerated power flow Jacobian matrix. Background technique [0002] Power flow calculation is the most widely used, basic and important electrical calculation in power system. In the study of power system operation mode and planning scheme, power flow calculation is required to compare the feasibility, reliability and economy of the operation mode or planning power supply scheme. At the same time, in order to monitor the operating status of the power system in real time, a large number of fast power flow calculations are also required. Therefore, when planning and designing the system and arranging the operation mode of the system, the offline power flow calculation is used; in the real-time monitoring of the power system operating status, the online power flow calculation is used. ...

AI Technical Summary

Problems solved by technology

Therefore, through reasonable scheduling between the CPU and the GPU, the coefficient matrix of the equation system can be quickly completed for LU decomposition, and the sparse linear equation system can be solved. Scholars at home and abroad have begun to study the method of accelerating the solution of the sparse linear equation system on the GPU, but there is no In-depth optimization of thread design, purely from the distribution of calculation calculation thread design, without in-depth research on thread calculation methods and data index methods, can not make the program fully utilize the advantages of the GPU

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