Measurement-Driven Diagnostics of Mechanism and Source of Subsynchronous Oscillations in Power Systems With Renewable Power Generation

As the proportion of renewable power generation (RPG) based on power electronic interfaces increases annually, the issue concerning the subsynchronous oscillations (SSOs) induced by the interactions between RPG and power systems is becoming increasingly prominent. A variety of mechanisms can induce SSOs, and each one necessitates a corresponding countermeasure for suppression or elimination. Therefore, precise and rapid SSO diagnosis, which enables the identification of SSO mechanisms and the tracing of SSO sources, is crucial. Consequently, a novel SSO diagnostic method based on deep learning (DL) using measurement data is proposed in this article. To overcome the challenges of obtaining sufficient and labeled data for DL training in an actual power system, a deep transfer learning (DTL) architecture is proposed to transfer knowledge from a simplified simulation power system with sufficient labeled data to an actual power system with limited unlabeled data. Furthermore, gradient-weighted class activation mapping (Grad-CAM) is utilized to analyze the interpretability of the trained DTL model for SSO diagnosis. Finally, the validity and adaptability of the proposed method are confirmed through comprehensive experiments conducted on a test example.