CAJ | 학술논문

为了提高电力系统小干扰稳定全部特征值分析的计算速度，研究了 QR 算法中上Hessenberg约化算法的并行化方法。以分块的方式将约化算法中的浮点运算整合为高阶的基础线性代数子程序(BLAS)运算，实现了分块约化算法在中央处理器(CPU)/图形处理器(GPU)混合架构下的并行，并应用到大规模电力系统的小干扰稳定全部特征值分析中。仿真结果表明，相比于多核CPU并行，基于GPU的分块上 Hessenberg 约化算法取得了高达5倍的加速效果。包含所提方法的全部特征值分析的整体计算速度获得了显著的提升，提高了 QR 算法对于大规模电力系统仿真分析的适用性。
위료제고전력계통소간우은정전부특정치분석적계산속도，연구료 QR 산법중상Hessenberg약화산법적병행화방법。이분괴적방식장약화산법중적부점운산정합위고계적기출선성대수자정서(BLAS)운산，실현료분괴약화산법재중앙처리기(CPU)/도형처리기(GPU)혼합가구하적병행，병응용도대규모전력계통적소간우은정전부특정치분석중。방진결과표명，상비우다핵CPU병행，기우GPU적분괴상 Hessenberg 약화산법취득료고체5배적가속효과。포함소제방법적전부특정치분석적정체계산속도획득료현저적제승，제고료 QR 산법대우대규모전력계통방진분석적괄용성。
To enhance the computational efficiency of complete eigenvalue analysis in power system small-signal stability analysis,the parallelization of upper Hessenberg reduction algorithm in the QR method is studied.A block reduction algorithm is utilized to integrate the floating-point operations into high-level basic linear algebraic subprograms (BLAS).The block reduction algorithm is parallelized on hybrid CPU/GPU (graphic processing unit) system and applied to the complete eigenvalue analysis of large-scale power system small-signal stability analysis.Simulation results show that,compared with multi-core CPU parallelization,the GPU-based block upper Hessenberg reduction algorithm is able to obtain a speed-up ratio up to 5 times the original.The overall computing speed of the complete eigenvalue analysis,including the method proposed, has achieved remarkable acceleration improvement.The applicability of the QR method to large-scale power system simulation analysis is increased.