基于贝叶斯深度学习的新能源多场站临界短路比区间预测方法OA北大核心CSTPCD

After large-scale renewable energy is connected to the grid,the problem of voltage security and stability is prominent,so it is urgent to find a method with accuracy and practicability to evaluate the voltage support strength of the system.Therefore,an intelligent enhancement method of multiple renewable energy station short circuit ratio(MRSCR)based on Bayesian deep learning is proposed in this paper.First,focusing on the lack of accurate critical short circuit ratio(CSCR)of MRSCR,the construction process of CSCR sample set is proposed,and the batch simulation program of samples is developed accordingly.Then,the multi-gate mixture-of-experts is used to synchronously predict the CSCR of each new energy access point,and Bayesian deep learning is combined to improve the prediction accuracy and quantify the prediction uncertainty.Finally,considering the disadvantages of point estimation,an inequality method based on dynamic threshold values is proposed to provide reliable and clear interval estimation,which can provide multiple attributes of predicted values for different decision requirements.The test results on the CEPRI-FS-102 bus system show that the proposed method can effectively improve the evaluation accuracy and speed of voltage support strength,and the prediction information can provide important guidance for the decision-making process.