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MinGen: Minimal generator set selection for small signal stability in power systems: A submodular framework

机译:MinGen:最小的发电机组选择,用于电力系统中的小信号稳定性:亚模块化框架

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Increased power transfers over a wide geographical area impact the small-signal stability of the power system, defined as the ability to damp oscillations between generators in different geographical areas. Wide-area control architectures have been proposed to control the generators in order to mitigate these oscillations. Ensuring that all unstable oscillating modes are removed depends on selecting a subset of generators to participate in wide-area control, which is inherently a discrete optimization problem that in the current literature does not have any solution algorithms with provable stability guarantees. In this paper, we present MinGen, a submodular optimization framework for generator selection for small-signal stability. We prove that small-signal stability is achieved when the unstable modes are controllable and observable from the set of selected generators, and map these properties to submodular constraints. We develop a computationally efficient and submodular MinGen algorithm with provable optimality bounds for generator selection, which can be generalized to enhance robustness to communication link failures. We evaluate our approach via a numerical study on the IEEE New England test case.
机译:在广阔的地理区域内增加的电力传输会影响电力系统的小信号稳定性,该稳定性被定义为抑制不同地理区域中的发电机之间的振荡的能力。为了减轻这些振荡,已经提出了广域控制架构来控制发电机。确保消除所有不稳定的振荡模式取决于选择发电机的一个子集来参与广域控制,这本质上是一个离散的优化问题,在当前文献中,没有任何具有可证明的稳定性保证的求解算法。在本文中,我们介绍了MinGen,这是用于小信号稳定性发生器选择的次模块优化框架。我们证明,当不稳定模式可从选定的生成器集中控制和观察到时,可以实现小信号稳定性,并将这些属性映射到亚模约束。我们开发了一种计算有效的亚模MinGen算法,具有可证明的最优边界用于发电机选择,可以对其进行概括以增强对通信链路故障的鲁棒性。我们通过对IEEE New England测试用例的数值研究来评估我们的方法。

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