近年来风机并网规模不断提高，针对深度学习对风速预测需要大量数据，以及随机微分方程对风电系统建模未能刻画出风速相关性对出力及并网点电压影响，提出了一种考虑随机因素并计及风速相关性，对含风电的电力系统马尔科夫切换型随机微分方程建模方法，引入Nataf和长短时记忆网络（long short-term memory network, LSTM）法构建风速时空相关性模型，运用马尔可夫切换型随机微分方程将风电系统分段线性化为各线性区间，然后研究了风速相关性、随机激励强度对并网点电压的影响，分析了风电系统临界稳定激励强度，最后通过数值分析方法对所建系统模型进行随机仿真，结果显示在随机激励强度临界值内，系统状态变量波动在稳定区域，与Simulink仿真电路中电压稳定波形对比，验证了本文建模方法的有效性，为新建风电场接入电力系统稳定性分析提供理论依据。

In recent years, the scale of wind turbine grid connection has been increasing, for the deep learning of wind speed prediction requires a large amount of data, as well as stochastic differential equations for wind power system modeling fail to portray the impact of wind speed correlation on the output power and grid-connection point voltage, a Markov switching stochastic differential equation modeling method considering stochastic factors and wind speed correlation was proposed for power systems containing wind power. The Nataf and LSTM were introduced to construct the wind speed spatio-temporal correlation model, the Markov switching stochastic differential equation was used to segment and linearize the wind power system into various linear segments. Then the effects of wind speed correlation and stochastic excitation strength on the voltage at the grid-connection point were studied, and the critical stable excitation strength of the wind power system was analyzed. Finally, the stochastic simulation of the constructed system model was carried out by numerical analysis methods, and the results show that the system state variable fluctuates in the stable region within the critical value of the random excitation intensity, and the comparison with the stable waveform of voltage in the Simulink simulation circuit verifies the validity of the modeling method in this paper, and provides a theoretical basis for the stability analysis of the new wind farm access to the power system.