GPU-accelerated Power System Sensitivity Analysis

Sensitivity analysis is an important method for power system stability analysis to obtain weak points of the power grid fault. However, the speed of the traditional sensitivity calculation method cannot realize the function of rapid analysis of the power grid. In this paper, the batch sensitivity calculation is accelerated in parallel by the Graphics Processing Unit (GPU). Firstly, the overall parallelization scheme of batch sensitivity calculation on CPU+GPU heterogeneous platform is studied. Then, the GPU kernel functions such as batch Jacobi matrix generation and batch sparse linear systems (SLS) up-looking LU decomposition are designed in detail. Finally, the performance of the above GPU-accelerated sensitivity analysis algorithm was tested on five power system examples. The results show that the proposed algorithm achieves a speedup of 149 times on a 9241-bus system.