An Algorithm Suite for Computational Nonlinear Analysis of Power Systems

用于电力系统计算非线性分析的算法套件

• 批准号: 1016467
• 负责人: Harry Dankowicz
• 金额: $ 47.79万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016467&HistoricalAwards=false
• 关键词: Algorithm; Suite; Computational; Nonlinear; Analysis

An Algorithm Suite for Computational Nonlinear Analysis of Power SystemsThis effort targets the original development of CNAPS, an innovative suite of numerical algorithms for continuation analysis of multi-segment trajectories in large-scale, nonlinear dynamical systems with multiple slow and fast timescales, coupled components, and with triggers, resets and switches. Continuation methods have proven very successful for analyzing system behavior of low-dimensional systems. In CNAPS, we aim to dramatically scale continuation methods to complex networked systems with hybrid system trajectories and tens of thousands of states, by developing new multiscale, multisegment, trajectory-discretization algorithms based on asynchronous collocation methods; developing new mesh adaptation algorithms suitable for the asynchronous collocation methods, which accommodate segment-specific discretization error bounds; and constructing domain decomposition methods particular to the network topology and the asynchronous collocation formulation, which enable efficient parallel execution.The core application of CNAPS considered in this multidisciplinary effort is modern power systems that include renewable sources of generation, specifically wind power, and newer forms of load, characterized by multiple coexisting time scales and trigger-induced switching behavior. Analysis of large-disturbance dynamic phenomena in such systems currently relies almost exclusively on forward simulation. While such tools may reveal complex behavior, they offer little help in the design process required to address unacceptable behavior, especially emerging phenomena associated with the increased use of power electronic converters. The development of CNAPS enables intelligent and efficient exploration of transient and steady-state responses of complex power systems, aimed at quantifying design and uncertainty margins for stable, faultless operation.

用于电源系统计算非线性分析的算法套件，该努力的原始开发是CNAP的原始开发，这是一个创新的数值算法套件，用于在大型非线性动力学系统中持续分析多段轨迹，具有多个缓慢和快速的时间尺度和快速的时间表，并与Triggers，ReSETS，RESESTS，RESETS，RESETS，RESETS和SOSSESS和SOSSESS和SOSSESS和SOSESTENT和SOSESTENT和SOSESTENT和SOSESTENT和SOSESTENT。持续方法已被证明在分析低维系统的系统行为方面非常成功。在CNAP中，我们旨在通过开发基于异步搭配方法的新的多尺度，多尺度，轨迹 - 差异算法来显着将连续方法扩展到具有混合系统轨迹和数万个状态的复杂网络系统；开发新的网格适应算法适合异步搭配方法，该算法适合特定于细分的离散误差界限； and constructing domain decomposition methods particular to the network topology and the asynchronous collocation formulation, which enable efficient parallel execution.The core application of CNAPS considered in this multidisciplinary effort is modern power systems that include renewable sources of generation, specifically wind power, and newer forms of load, characterized by multiple coexisting time scales and trigger-induced switching behavior.目前，对此类系统中大扰动动态现象的分析几乎完全依赖于正向模拟。尽管这种工具可能会揭示复杂的行为，但它们在解决不可接受的行为所需的设计过程中几乎没有帮助，尤其是与增加电力电子转换器使用相关的新兴现象。 CNAPS的开发能够对复杂功率系统的瞬态和稳态响应进行智能，有效的探索，旨在量化稳定，无障碍操作的设计和不确定性余量。