A deep learning-based transient stability constrained optimal power flow (TSCOPF) model with wind power uncertainty is proposed to address stability challenges faced by traditional TSCOPF models in power systems with increasing renewable energy integration. A cascaded prediction method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and long short-term memory (LSTM) networks is firstly developed to enhance wind power prediction accuracy and robustness. An improved multi-layer perceptron (IMLP) model is then employed to establish the mapping relationship between system operating states and transient stability index (TSI) for rapid and accurate transient stability assessment. An improved weIghted mean of vectors (IINFO) algorithm based on Lévy flight is subsequently adopted to solve the TSCOPF optimization problem. Simulation experiments are finally conducted on modified IEEE 39-bus and IEEE 68-bus test systems. Results demonstrate that the proposed cascaded method achieves superior prediction performance compared to traditional methods in wind power forecasting. The proposed TSCOPF model is verified to maintain stable system operation under wind power integration conditions. The improved IINFO algorithm exhibits significantly faster convergence speed and lower optimization costs than other optimization algorithms.

There is uncertainty in the output of wind farms, and there are also certain correlation in time and space. The random variables brought by these wind farms are introduced into the system, which may bring certain deviations to the calculation of the transient stability constrained optimal power flow (TSCOPF) if their temporal and spatial correlations are not adequately taken into account. Therefore, a TSCOPF model and calculation method considering the spatio-temporal correlation of wind power output are proposed. Firstly, a wind power output model containing spatio-temporal correlation is constructed to consider the correlation between wind farm power output in time and space dimensions. Secondly, a probability constraint is constructed based on the joint chance constraint (JCC) theory and a TSCOPF model based on JCC theory is established on this basis. Then a mix sample average approximation (MSAA) is used to process JCC, and JCC problem is transformed into a linear programming (LP) problem, which is solved by the CPLEX solver. Finally, simulation analysis is carried out at the improved IEEE 39-bus system, and the simulation results show that the proposed method can obtain the optimal operation scheme while ensuring system safety and stability.