Stability of Large Scale Systems With Order Model Reduction

在新兴的电力系统新结构中，具有模型简化的大型系统的Pai稳定性将变得非常重要。需要更快、更可靠的工具来计算动态可用传输能力（DATC）。这项建议涉及将影响这一领域的三个方面。首先，轨迹敏感性已经为仅基于故障后系统的动态安全评估（DSA）提供了可靠的结果，现在将扩展到涵盖故障和故障后动态。这将意味着为代数部分不连续的微分代数方程系统制定动态灵敏度函数。这将能够直接计算对临界清除时间的敏感性，以及负载，抽头设置等的变化。第二个目标是通过迭代方法找到求解Ax = b型大型稀疏系统的更好的鲁棒预条件。然后将其用作使用同步隐式方法的快速动态仿真算法的一部分。最后将采用两种新的模型约简方法。首先，我们将使用非线性动态响应的轨迹灵敏度来识别相干发生器。到目前为止，只有围绕一个工作点的线性化模型被用于一致性分组或一些其他启发式标准。其次，我们将使用Krylov投影技术来开发降阶动态模型。这是最近开发的用于DAE系统的数值线性代数工具。

ECS-9803055 Pai Stability of large scale systems with model reduction is going to be important in the emerging new structure of power systems. Faster and reliable tools for computing dynamic available transfer capabilities (DATC) will be needed. This proposal addresses three aspects which will impact this area. Firstly, trajectory sensitivities which have yielded reliable results for dynamic security assessment (DSA) based on post-fault systems only, will now be extended to cover both faulted and post-fault dynamics. This will imply formulating dynamic sensitivity functions for systems of differential algebraic equations with discontinuities in the algebraic portion. This will enable the direct computation of sensitivities to critical clearing time as well to changes in load, tap settings, etc. A second objective is to find better and robust preconditioners for solving large sparse systems of the type Ax = b by iterative methods. This will then be used as part of the fast dynamic simulation algorithm using the simultaneous implicit method. Finally two new approaches to model reduction will be taken. First, we will use trajectory sensitivities of the nonlinear dynamic response to identify coherent generators. To date only linearized models around an operating point have been used for coherency grouping or some other heuristic criteria. Secondly, we will use the Krylov projector technique to develop reduced order dynamic models. This is a recently developed tool in numerical linear algebra for DAE systems.