Transient Stability Assessment of Power Systems Containing Wind Power Based on Hybrid Simulation and Deep Learning

To address the issue that traditional pure electromechanical transient simulation cannot accurately simulate the dynamic characteristics and strong nonlinear characteristics of power electronics devices, this paper establishes a hybrid electromechanical-electromagnetic transient simulation model for wind power grid-connected power systems. For the power system time series data, a long-short term memory (LSTM) network-based transient stability evaluation method is proposed, and a focal loss (FL) function is introduced to enhance the mining of the model for a few classes of samples. To improve the interpretability of the evaluation results, the Local Interpretable Model-agnostic Explanations (LIME) algorithm is used for predictive attribution analysis of example samples, which provides a reliable basis for online evaluation of grid transient stability.