A power system with a large number of wind farms may consist of several thousand or more wind turbine generators (WTGs). Owing to the high dimension of the linearized model of such a power system, applying conventional modal analysis may be impossible due to the numerical complexity. This study proposes a reduced-order method of modal analysis to detect the risk and trace the sources of oscillatory instability within the power system. The proposed method does not use the representation of wind farms by aggregating WTGs to reduce model dimension. The maximum dimension of the matrix involved in the reduced-order modal analysis is either the total number of WTGs in the power system or the order of dynamic models of individual WTGs. Hence, the proposed method effectively avoids the numerical complexity of applying modal analysis to the high-dimensional model. Improvements of the reduced-order method in practical applications are suggested, particularly for situations in which detailed parametric information for every WTG is not available. Initially, a few representative WTGs are selected to derive their parametric models via field or laboratory tests. Then, reduced-order modal analysis is applied to detect the risk and trace the sources of oscillatory instability.

中文翻译：

---

具有大量风电场的电力系统中风险检测和跟踪小信号振荡不稳定源的降阶方法

具有大量风电场的电力系统可能包含数千个或更多的风力涡轮发电机（WTG）。由于这种电力系统的线性化模型的高维度，由于数值复杂性，应用常规模态分析可能是不可能的。这项研究提出了一种模态分析的降阶方法，以检测风险并追踪电力系统内振荡不稳定的根源。所提出的方法没有通过汇总WTG来减小模型尺寸而使用风电场的表示。降阶模态分析中涉及的矩阵的最大维度是电力系统中WTG的总数或单个WTG的动态模型的顺序。因此，该方法有效地避免了将模态分析应用于高维模型的数值复杂性。建议在实际应用中对降阶方法进行改进，尤其是在无法获得每个WTG的详细参数信息的情况下。最初，选择了一些具有代表性的WTG，以通过现场或实验室测试得出其参数模型。然后，应用降阶模态分析来检测风险并追踪振荡不稳定的根源。