Parallel power flow computing in power grids based on a preconditioned BICGSTAB method
DOI：DOI: 10.19783/j.cnki.pspc.191509
Key Words:power flow  inexact Newton method  Jacobian matrix  BICGSTAB method  preconditioner  CPU-GPU heterogeneous platform
Author NameAffiliation
SONG Xiaozhe 1. State Grid Jilin Electric Power Co., Changchun 130000, China
2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China 
WEI Guo, 1. State Grid Jilin Electric Power Co., Changchun 130000, China
2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China 
LI Xue 1. State Grid Jilin Electric Power Co., Changchun 130000, China
2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China 
WANG Changjiang 1. State Grid Jilin Electric Power Co., Changchun 130000, China
2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China 
SUN Fushou 1. State Grid Jilin Electric Power Co., Changchun 130000, China
2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China 
LI Zhenyuan 1. State Grid Jilin Electric Power Co., Changchun 130000, China
2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China 
Hits: 3109
Download times: 1125
Abstract:In order to solve the large-scale power flow problem accurately and quickly, a parallel algorithm for power flow calculation based on the inexact Newton and CPU-GPU heterogeneous platform is proposed. The efficiency of power flow computing can be effectively enhanced by improving the solving efficiency of the correction equations which are the most time-consuming part of the Newton-Raphson method. For this reason, the Bi-Conjugate Gradient Stabilized (BICGSTAB) method is adopted to solve the correction equations according to the asymmetric and indefinite characteristics of a Jacobian matrix. Then, in order to improve the convergence performance of the BICGSTAB method, a two-step preconditioner for the Jacobian matrix is proposed given the characteristic that the Jacobian matrix is analogous to a sparse diagonally dominant matrix. The convergence performance of the BICGSTAB method can be improved with the two-step preconditioner that consists of the improved Preconditioner with sparsity Pattern of AT (PPAT) preconditioner and the improved Jacobi preconditioner. Next, the above power flow algorithm is transplanted to a CPU-GPU heterogeneous platform to achieve power flow parallel computing. Finally, different test systems are further used to verify and analyze the performance of the proposed method. The results validate that the proposed power flow algorithm can solve the power flow calculation problem accurately and quickly. This work is supported by National Natural Science Foundation of China (No. 51607033 and No. 51677023).
View Full Text  View/Add Comment  Download reader