Transient Stability Assessment of Power System Based on Bi-directional Gated Recurrent Unit

The conventional machine learning models are weak in overall perception of time series when applied to power system transient stability assessment, so it is difficult to mine the dynamic information contained in the electrical response trajectory, and the reliability of critical sample prediction results is low. Focusing on the aforementioned problems, a two-stage transient stability assessment method based on the bi-directional gated recurrent unit (BiGRU) is proposed. The method takes the dynamic trajectories of the underlying measurement data after disturbance as inputs, first screens out credible samples through continuous dynamic assessment, and then predicts the fault severity of uncertain samples and credible stable samples through the regression model. BiGRU classifier is improved by introducing truncation function and weight coefficient into the loss function, which strengthens the study strength of the model for difficult samples and unstable samples. The experimental results on the modified New England 10-machie 39-bus system show that the proposed method not only significantly reduces the misjudgment of unstable samples, but also improves the recognition ability of stable samples. © 2021 Automation of Electric Power Systems Press.