Abstract Wind farms are increasingly installed in power systems. Such power generation units increase stochastic power system variables, affecting power systems stability. Therefore, the impact of stochastic generation on power systems stability should be carefully taken into account, an aim which can be realized by probabilistic evaluation. This paper presents hybrid methods for the evaluation of probabilistic small-signal stability (PSSS) in power systems. These methods are based on clustering approaches and Monte Carlo simulation (MCS) which is employed in a probabilistic problem to achieve acceptable results. There are two steps for evaluation of PSSS in the hybrid methods. Clustering methods divide stochastic sets into small sets; the member of small sets is employed to calculate eigenvalues similar to MCS. Consequently, this method is faster than MCS. Two case studies are employed based on the IEEE 9-bus and 39-bus test systems for the evaluation of the proposed method. It is shown that the proposed methods yield accurate results and are faster than MCS.

中文翻译：

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风电场高渗透电力系统概率小信号稳定性评估

摘要 风电场越来越多地安装在电力系统中。这种发电机组增加了电力系统的随机变量，影响了电力系统的稳定性。因此，应该仔细考虑随机发电对电力系统稳定性的影响，这一目标可以通过概率评估来实现。本文介绍了评估电力系统概率小信号稳定性 (PSSS) 的混合方法。这些方法基于聚类方法和蒙特卡罗模拟 (MCS)，该方法用于概率问题以实现可接受的结果。在混合方法中评估 PSSS 有两个步骤。聚类方法将随机集划分为小集；使用小集合的成员来计算类似于 MCS 的特征值。最后，这种方法比 MCS 快。基于 IEEE 9 总线和 39 总线测试系统的两个案例研究用于评估所提出的方法。结果表明，所提出的方法产生准确的结果并且比 MCS 更快。