Low-Frequency Oscillation Analysis of Power System Containing DFIG Wind Farm Based on Wind Speed Interval Prediction and Eigenvalue Trajectory

The interaction between wind and power system dynamics intensifies, which in turn affects the system low-frequency oscillation characteristics. Since the uncertainty of wind power is mainly caused by wind speed, there is an urgent need to quantify the relationship between low-frequency oscillation modes and wind speed and to analyze and evaluate it. In this paper, we propose an analysis method for grid-connected low-frequency oscillations of doubly-fed induction generator (DFIG) based on wind speed interval prediction and eigenvalue trajectory. Complete ensemble empirical mode decomposition (CEEMD), and then solve the wind speed interval prediction by bidirectional long short-term memory network quantile regression (BiLSTM-QR). The uncertainty of wind speed is solved, and then the implicit function relationship between low-frequency oscillation and wind speed is described by regression equation, which solves the problems of repeated iterations of state matrix and repeated sampling and improves the operational efficiency. Finally, the stability of DFIG grid-connected low-frequency oscillations can be analyzed and evaluated by combining the predicted wind speed and the regression equation. The analysis of the future low-frequency oscillation characteristics of wind farms through the four-machine two-area system and the actual wind speed data of wind farms illustrates the correctness and effectiveness of the method.