论文标题
一种在线数据驱动的方法，用于基于非线性动力学（Sindy）的稀疏识别，从发电厂找到强制振荡来源
An Online Data-Driven Method to Locate Forced Oscillation Sources from Power Plants Based on Sparse Identification of Nonlinear Dynamics (SINDy)
论文作者
论文摘要
强制振荡可能会危害电源系统的安全操作。为了减轻强制振荡，定位来源至关重要。在本文中，开发了一种在线纯数据驱动的方法来定位强制振荡的在线纯粹的识别（Sindy）。在所有模拟情况下（在WECC 179总线系统中）和IEEE工作队测试用奇库中的实际振荡事件（在ISO新英格兰系统中）进行了验证，这些事件均已进行，这表明，即使在大多数情况下，在共鸣状态以及自然模态下，这些算法都无法准确地位于源。少量调整要求和低计算成本使所提出的方法可在线应用可行。
Forced oscillations may jeopardize the secure operation of power systems. To mitigate forced oscillations, locating the sources is critical. In this paper, leveraging on Sparse Identification of Nonlinear Dynamics (SINDy), an online purely data-driven method to locate the forced oscillation is developed. Validations in all simulated cases (in the WECC 179-bus system) and actual oscillation events (in ISO New England system) in the IEEE Task Force test cases library are carried out, which demonstrate that the proposed algorithm, requiring no model information, can accurately locate sources in most cases, even under resonance condition and when the natural modes are poorly damped. The little tuning requirement and low computational cost make the proposed method viable for online application.
