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

机译:Mingen:电力系统中小信号稳定性的最小发电机设置选择:子模块框架

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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:增加的功率转移在宽的地理区域上会影响电力系统的小信号稳定性,定义为不同地理区域中发电机之间的振荡的能力。已经提出广域控制架构来控制发电机以减轻这些振荡。确保删除所有不稳定的振荡模式取决于选择要参与广域控制的发电机子集,这本质上是一个离散的优化问题,即当前文献中没有任何具有可证实稳定保证的解决方案算法。本文展示了Mingen,用于小信号稳定性的发电机选择子模块优化框架。我们证明,当不稳定模式可控制和从所选发生器集合可观察到不稳定模式时,可以实现小信号稳定性,并将这些属性映射到子骨折约束。我们开发了一种具有可提供的发电机选择的可提供的最优性界的计算高效和子模具Mingen算法,这可以推广以增强通信链路故障的鲁棒性。我们通过对IEEE新英格兰测试用例的数值研究来评估我们的方法。

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