适用于CPU+GPU协同架构的大规模病态潮流求解方法

随着电网规模不断扩大,负荷增长明显,快速、准确地进行大规模病态潮流求解具有重要的实用价值。将连续牛顿法(CNM)应用于大规模电力系统病态潮流求解中,将潮流方程的求解过程等效为常微分方程组积分计算过程。为了加速该计算过程,针对中央处理器(CPU)+图形处理器(GPU)协同计算架构,设计了基于GPU的不平衡功率快速计算方法,进而优化CNM算法并行实现所需软硬件配置,形成高效的大规模病态潮流求解方法。通过多个大规模病态潮流算例验证了所提CPU+GPU协同潮流计算方法的正确性和实用性。

Power Flow Computation Method for Large-scale Ill-conditioned Systems Applied to CPU and GPU Coordination Architecture

With the growing size and increasing load of power systems, it has great significance in practice to solve the large-scale ill-conditioned power flow problems accurately and efficiently. The continuous Newton''s method(CNM)is applied to solve the ill-conditioned cases of large-scale power systems by equalizing the solving process of nonlinear equations to the numerical integration of ordinary differential equations. The coordination architecture of central processing unit(CPU)and graphics processing unit(GPU)is used to accelerate power flow calculations with CNM. Unbalanced power analysis is designed and implemented on GPU. Every part of the whole algorithm is optimized in consideration of characteristics of the coordination architecture to form an efficient solving method. Large-scale ill-conditioned cases are presented to verify the correctness and practicality of the proposed CPU and GPU coordinated power flow computation method.