Research On Parallel Computing Of Electric Power System Based On GPU

Power flow is the basic and important analysis of electric power systems, the goal of this thesis is to increase the computing speed of power How solver so that the real-time flow analysis can become possible. Parallel computing is a good solution to increasing the computation speed. Because of the high computing efficiency and low prices, GPUs have been developed rapidly in many fields.This thesis transforms sequential power flow problem to a parallel problem and solves it on GPU. The thesis compares four parallel programming model of GPU (CUDA, OpenACC, C++AMP. and Jacket), and analyzes theoretical parallel performance of three power flow algorithms (Gauss-Seidel algorithm. Newton-Raphson algorithm, and P-Q decoupled algorithm). Then, this thesis implements parallel Gauss-Seidel solver. Newton-Raphson solver, and P-Q decoupled solver using CUDA (Compute Unified Device Architecture) on GPU. with the aim of investigating the performance of three different parallel power flow solvers, and uses four IEEE standard power systems and one actual running power system as the test cases when comparing the speedups that a GPU system can provide. The results show that Newton-Raphson solver has the best speedup when it is operated on GPU, Gauss-Seidel solver performs the worst, and P-Q decoupled solver is in the middle. The test results also indicate that when the size of the system is small, GPU does not seem to have advantages over CPU from computation time perspective. However, as the size of the system increases, the advantages of GPU become more clear. Then, the thesis optimizes the parallel Newton-Raphson solver with sparse matrix technology to improve parallel performance. This thesis also implements parallel Gauss-Seidel solver, Newton-Raphson solver, and P-Q decoupled solver using OpenACC, and compares OpenACC’s parallel performance with CUDA’s using the same test cases. The results show that OpenACC and CUDA have the similar performance.