论文标题
计算有效的不稳定电力系统模型的最佳控制
Computationally efficient optimal control for unstable power system models
论文作者
论文摘要
在本文中，重点主要在于获得不稳定的电力系统模型的最佳控制，并通过基于Riccati的反馈稳定过程稳定它们，并使用稀疏技术。我们要找到从巴西相互连接的电力系统（BIPS）模型的不稳定功率系统模型（CARES）的连续时间代数Riccati方程（CARES）的解决方案，该模型是大规模稀疏索引-1描述符系统。我们提出了基于投影的理性Krylov子空间方法（RKSM），以迭代CARES解决方案。 RKSM的新颖性是保留稀疏的计算和时间符合自适应移位参数的实现。我们将基于嵌套的迭代Kleinman-Newton（KN）方法修改了低级cholesky-factor集成的交流方向隐式（LRCF-ADI）技术，以稀疏形式进行调整并调整以解决所需的护理。我们将Kleinman-Newton方法与使用RKSM的结果进行了比较。通过MATLAB模拟，提出的技术的适用性和适应性是合理的。研究目标模型的瞬态行为通过表格和图形方法进行了比较分析。
In this article, the focus is mainly on gaining the optimal control for the unstable power system models and stabilizing them through the Riccati-based feedback stabilization process with sparsity-preserving techniques. We are to find the solution of the Continuous-time Algebraic Riccati Equations (CAREs) governed from the unstable power system models derived from the Brazilian Inter-Connected Power System (BIPS) models, which are large-scale sparse index-1 descriptor systems. We propose the projection-based Rational Krylov Subspace Method (RKSM) for the iterative computation of the solution of the CAREs. The novelties of RKSM are sparsity-preserving computations and the implementation of time-convenient adaptive shift parameters. We modify the Low-Rank Cholesky-Factor integrated Alternating Direction Implicit (LRCF-ADI) technique based nested iterative Kleinman-Newton (KN) method to a sparse form and adjust this to solve the desired CAREs. We compare the results achieved by the Kleinman-Newton method with that of using the RKSM. The applicability and adaptability of the proposed techniques are justified numerically with MATLAB simulations. Transient behaviors of the target models are investigated for comparative analysis through the tabular and graphical approaches.
