On-line transient stability analysis of a power grid is crucial in determining whether the power grid will traverse to a steady state stable operating point after a disturbance. The transient stability analysis involv...On-line transient stability analysis of a power grid is crucial in determining whether the power grid will traverse to a steady state stable operating point after a disturbance. The transient stability analysis involves computing the solutions of the algebraic equations modeling the grid network and the ordinary differential equations modeling the dynamics of the electrical components like synchronous generators, exciters, governors, etc., of the grid in near real-time. In this research, we investigate the use of time-parallel approach in particular the Parareal algorithm implementation on Graphical Processing Unit using Compute Unified Device Architecture to compute solutions of ordinary differential equations. The numerical solution accuracy and computation time of the Parareal algorithm executing on the GPU are demonstrated on the single machine infinite bus test system. Two types of dynamic model of the single synchronous generator namely the classical and detailed models are studied. The numerical solutions of the ordinary differential equations computed by the Parareal algorithm are compared to that computed using the modified Euler’s method demonstrating the accuracy of the Parareal algorithm executing on GPU. Simulations are performed with varying numerical integration time steps, and the suitability of Parareal algorithm in computing near real-time solutions of ordinary different equations is presented. A speedup of 25× and 31× is achieved with the Parareal algorithm for classical and detailed dynamic models of the synchronous generator respectively compared to the sequential modified Euler’s method. The weak scaling efficiency of the Parareal algorithm when required to solve a large number of ordinary differential equations at each time step due to the increase in sequential computations and associated memory transfer latency between the CPU and GPU is discussed.展开更多
A temporal multiscale hybridization method is presented that carefully couples coarse scale gyrokinetic models with exact charged particle solution trajectories (that is, with full phase information) in a magnetic fie...A temporal multiscale hybridization method is presented that carefully couples coarse scale gyrokinetic models with exact charged particle solution trajectories (that is, with full phase information) in a magnetic field. The approach is based on the careful approximation of a sum, generally employed for time-parallel (TP) computing applications. While the hybridization method presented is highly parallelizable, a computational efficiency gain is seen from considering serial computations only. A complete numerical method is only presented for the aforementioned charged particle application, however, the general approach depicted likely has relevance to a wide swath of challenging multiscale/multiphysics problems. Additionally, the approach has obvious relevance to TP computing applications (such as variable selection on which to perform TP calculations and fine scale sampling strategies).展开更多
大规模双馈风电场作为实现我国“双碳”目标的陆上风电主力,其高比例电力电子设备的接入对电力系统电磁暂态高精度仿真技术提出日益严苛的要求,面向上百台发电单元的双馈场站全拓扑精细化微秒级仿真研究仍相对空白。基于现场可编程门阵...大规模双馈风电场作为实现我国“双碳”目标的陆上风电主力,其高比例电力电子设备的接入对电力系统电磁暂态高精度仿真技术提出日益严苛的要求,面向上百台发电单元的双馈场站全拓扑精细化微秒级仿真研究仍相对空白。基于现场可编程门阵列(field programmable gate array,FPGA)微秒级小步长并行仿真能力,提出一种面向双馈风电场站的发电单元FPGA并行仿真方法。首先,进行双馈感应电机微秒级高并行度离散化建模与换流器受控源建模;接着,对节点导纳矩阵分块降维以实现发电单元内部分网并行,并从整体电路解算层面提出单元级并行仿真框架;最后,考虑实时数字仿真器(real time digital simulator,RTDS)与FPGA仿真平台特点,分配发电单元的微秒级小步长仿真任务并搭建联合仿真硬件框架;通过对比RTDS标准模型与RTDS+FPGA联合仿真模型,验证所提并行仿真方法的准确性。展开更多
查询是数据库系统最主要的操作,查询性能直接决定了应用程序的响应速度和用户体验.多查询并行场景下,查询之间争用或共享数据库系统资源,产生查询交互(Query Interaction,QI),是影响查询性能的主要因素,准确度量QI是为查询选择合适执行...查询是数据库系统最主要的操作,查询性能直接决定了应用程序的响应速度和用户体验.多查询并行场景下,查询之间争用或共享数据库系统资源,产生查询交互(Query Interaction,QI),是影响查询性能的主要因素,准确度量QI是为查询选择合适执行计划及提升查询性能的关键.QI随着查询中操作的执行动态变化,现有度量方法只考虑新查询加入时刻系统资源的使用情况,不考虑系统资源在查询执行过程中的变化,度量不准确.为此,本文提出查询组合时序异构图,用于描述查询组合中QI随时间的动态变化;提出时间感知多边类型权重计算模型(Time-Aware Multi-edge Type Weight Calculation,TA-MTWC),计算异构图中操作节点之间任意执行时刻的边权重,捕捉QI随时间的动态变化;提出查询组合时序异构图分类模型(Query-mix Time-series Heterogeneous Graph Classification,QTHGC),采用长短期记忆神经网络(Long Short Term Memory,LSTM)学习多个时刻图表示之间的时序关系,为并行查询选择执行计划.在PostgreSQL上的实验证明,QTHGC的平均准确率比查询优化器提高51.2%,比现有最新的QHGC模型提高2.87%.展开更多
摘要On-line transient stability analysis of a power grid is crucial in determining whether the power grid will traverse to a steady state stable operating point after a disturbance. The transient stability analysis involves computing the solutions of the algebraic equations modeling the grid network and the ordinary differential equations modeling the dynamics of the electrical components like synchronous generators, exciters, governors, etc., of the grid in near real-time. In this research, we investigate the use of time-parallel approach in particular the Parareal algorithm implementation on Graphical Processing Unit using Compute Unified Device Architecture to compute solutions of ordinary differential equations. The numerical solution accuracy and computation time of the Parareal algorithm executing on the GPU are demonstrated on the single machine infinite bus test system. Two types of dynamic model of the single synchronous generator namely the classical and detailed models are studied. The numerical solutions of the ordinary differential equations computed by the Parareal algorithm are compared to that computed using the modified Euler’s method demonstrating the accuracy of the Parareal algorithm executing on GPU. Simulations are performed with varying numerical integration time steps, and the suitability of Parareal algorithm in computing near real-time solutions of ordinary different equations is presented. A speedup of 25× and 31× is achieved with the Parareal algorithm for classical and detailed dynamic models of the synchronous generator respectively compared to the sequential modified Euler’s method. The weak scaling efficiency of the Parareal algorithm when required to solve a large number of ordinary differential equations at each time step due to the increase in sequential computations and associated memory transfer latency between the CPU and GPU is discussed.
摘要A temporal multiscale hybridization method is presented that carefully couples coarse scale gyrokinetic models with exact charged particle solution trajectories (that is, with full phase information) in a magnetic field. The approach is based on the careful approximation of a sum, generally employed for time-parallel (TP) computing applications. While the hybridization method presented is highly parallelizable, a computational efficiency gain is seen from considering serial computations only. A complete numerical method is only presented for the aforementioned charged particle application, however, the general approach depicted likely has relevance to a wide swath of challenging multiscale/multiphysics problems. Additionally, the approach has obvious relevance to TP computing applications (such as variable selection on which to perform TP calculations and fine scale sampling strategies).
摘要大规模双馈风电场作为实现我国“双碳”目标的陆上风电主力,其高比例电力电子设备的接入对电力系统电磁暂态高精度仿真技术提出日益严苛的要求,面向上百台发电单元的双馈场站全拓扑精细化微秒级仿真研究仍相对空白。基于现场可编程门阵列(field programmable gate array,FPGA)微秒级小步长并行仿真能力,提出一种面向双馈风电场站的发电单元FPGA并行仿真方法。首先,进行双馈感应电机微秒级高并行度离散化建模与换流器受控源建模;接着,对节点导纳矩阵分块降维以实现发电单元内部分网并行,并从整体电路解算层面提出单元级并行仿真框架;最后,考虑实时数字仿真器(real time digital simulator,RTDS)与FPGA仿真平台特点,分配发电单元的微秒级小步长仿真任务并搭建联合仿真硬件框架;通过对比RTDS标准模型与RTDS+FPGA联合仿真模型,验证所提并行仿真方法的准确性。
摘要查询是数据库系统最主要的操作,查询性能直接决定了应用程序的响应速度和用户体验.多查询并行场景下,查询之间争用或共享数据库系统资源,产生查询交互(Query Interaction,QI),是影响查询性能的主要因素,准确度量QI是为查询选择合适执行计划及提升查询性能的关键.QI随着查询中操作的执行动态变化,现有度量方法只考虑新查询加入时刻系统资源的使用情况,不考虑系统资源在查询执行过程中的变化,度量不准确.为此,本文提出查询组合时序异构图,用于描述查询组合中QI随时间的动态变化;提出时间感知多边类型权重计算模型(Time-Aware Multi-edge Type Weight Calculation,TA-MTWC),计算异构图中操作节点之间任意执行时刻的边权重,捕捉QI随时间的动态变化;提出查询组合时序异构图分类模型(Query-mix Time-series Heterogeneous Graph Classification,QTHGC),采用长短期记忆神经网络(Long Short Term Memory,LSTM)学习多个时刻图表示之间的时序关系,为并行查询选择执行计划.在PostgreSQL上的实验证明,QTHGC的平均准确率比查询优化器提高51.2%,比现有最新的QHGC模型提高2.87%.