A Subsynchronous Oscillation Assessment Method in Power Systems with DFIGs Based on Improved Transformer Network and Interference Level Adaptation

With the increasing popularity of renewable energy, many power electronics are connected to the grid. This integration can induce subsynchronous oscillations (SSO) due to interactions between power electronic converters or between converters and the AC grid. SSO poses a severe threat to the safe and stable operation of power systems, necessitating the development of a real-time online SSO assessment method. Focusing on the SSO caused by integrating doubly-fed induction generators (DFIGs) into the power grid via series compensation transmission lines, this study proposes an SSO assessment method based on improved transformer networks and adaptive selection classifiers. First, the IMF-IE information is extracted to describe SSO characteristics, and a scenario classification model based on P-DDQN is established. The introduction of the prioritized experience replay (PER) mechanism accelerates the learning process and enhances performance. Next, the interference level is calculated based on data energy information for scenarios that include SSO. Subsequently, an SSO parameter estimator is developed for PMU data corresponding to different interference levels. This estimator integrates multi-head attention (MHA) with an improved sparse autoencoder to balance accuracy and speed in the assessment. Finally, a simulation model is constructed to validate the proposed method. The simulation results demonstrate that the proposed method can quickly and accurately estimate multiple oscillation modes' frequencies and damping ratios in complex field scenarios.