基于STFT图像和迁移学习的次同步振荡源定位方法

直驱风机与电网交互引发次同步振荡，严重威胁电网的安全稳定运行。为快速定位诱发机组，提出了一种基于短时傅里叶变换(STFT)图像和迁移学习的次同步振荡源定位方法。首先，采用压缩感知技术将出口数据转化为观测信号，再对观测信号进行STFT得到具备振荡特征的映射图，构建映射图与振荡源机组之间的联系；然后，采用对抗式迁移学习架构，结合电力系统，实现对目标域无标签振荡数据的快速泛化；最后，与传统迁移学习方法进行比较，结果表明所提方法在定位准确率和效率方面表现更优，且具备较强的抗噪能力。

Localization Method for Sub-Synchronous Oscillation Sources Based on STFT Images and Transfer Learning

Sub-synchronous oscillations induced by the interaction between direct-drive wind turbines and the grid pose a serious threat to the safe and stable operation of the power grid. To rapidly identify the triggering unit, a localization method for sub-synchronous oscillation source based on short-time Fourier transform (STFT) images and transfer learning is proposed. Firstly, compressive sensing technology is employed to transform output data into observation signals, and then the STFT is performed on the observation signals to obtain the mapping image with oscillation characteristics, and the link between the mapping image and the oscillation source unit is constructed. Secondly, an adversarial transfer learning architecture is utilized in conjunction with the power system to achieve rapid generalization of unlabeled oscillation data in the target domain. Finally, the traditional transfer learning method is introduced for comparison, the results show that the proposed method performs better in terms of localization accuracy and efficiency, and has strong noise resistance.