Fast Parallel Stochastic Subspace Algorithms for Large-Scale Ambient Oscillation Monitoring
Fast Parallel Stochastic Subspace Algorithms for Large-Scale Ambient Oscillation Monitoring
复制标题
DOI：
10.1109/tsg.2016.2608965
复制
发表时间：
2017-05-01
影响因子：
9.6
通讯作者：
Pothen, Alex
中科院分区：
文献类型：
--
作者：
Wu, Tianying;Venkatasubramanian, Vaithianathan (Mani);Pothen, Alex
随着同步相量在电网中的广泛应用，基于测量的振荡监测算法在识别电力系统中的实时振荡模态特性方面变得越来越有用。随着相量测量单元（PMU）通道数量的增加，基于PMU数据的算法在处理大规模稠密矩阵时的计算量占主导地位。为了克服这一限制，本文提出了新的配方和计算策略，加快环境振荡监测算法，即随机子空间识别（SSI）。基于以前的工作，两个快速奇异值分解（SVD）的方法首先应用到奇异值分解的SSI算法内的评估。其次，利用块结构，使大规模的稠密矩阵计算可以并行处理。这有助于节省内存以及整个计算时间。三组西部互联存档数据的实验结果表明，新方法可以提供显着的加速，同时保持模态估计精度。提出的快速并行算法，实时振荡监测的大规模系统，使用数百个PMU的测量变得可行。
With the installation of synchrophasors widely across the power grid, measurement-based oscillation monitoring algorithms are becoming increasingly useful in identifying the real-time oscillatory modal properties in power systems. When the number of phasor measurement unit (PMU) channels grows, the computational time of many PMU data based algorithms is dominated by the computational burden in processing large-scale dense matrices. In order to overcome this limitation, this paper presents new formulations and computational strategies for speeding up an ambient oscillation monitoring algorithm, namely, stochastic subspace identification (SSI). Based on previous work, two fast singular value decomposition (SVD) approaches are first applied to the SVD evaluation within the SSI algorithm. Next, block structures are exploited so that the large-scale dense matrix computations can be processed in parallel. This helps in memory savings as well as in over-all computational time. Experimental results from three sets of archived data of the western interconnection demonstrate that the new approaches can provide significant speedups while retaining modal estimation accuracy. With proposed fast parallel algorithms, the real-time oscillation monitoring of the large-scale system using hundreds of PMU measurements becomes feasible.