基于微分神经网络的风电机群低电压穿越特性建模

针对目前缺少分析风电机群低电压穿越特性高效建模方法的问题，提出了一种基于微分神经网络的风电机群低电压穿越特性建模 方法 。模型输入为风电场并网点电压与各台风机风速，输出为并网点电流。该模型能够较好地表征其强非线性切换过程，刻画风电场不同风速分布场景下风电机群的低电压穿越特性。在CloudPSS云仿真平台上建立了包含3台双馈风力发电机的风电机群仿真算例，对所提方法进行测试，测试结果验证了微分神经网络模型的泛化能力与有效性。

Low Voltage Ride-through Modeling for Wind Turbines Based on Neural ODEs

Considering the difficulty of equivalent modeling of low voltage ride-through(LVRT) characteristics of a wind farm, a neural ordinary differential equation(ODE)-based wind farm LVRT modeling methodis proposed. The input of the model is the voltage and wind speed of each wind turbine at the grid connection point of wind farm, and the output is the current at the grid connection point. The model can better characterize the strong nonlinear switching process and describe LVRT characteristics of wind farms under different wind speed scenarios. A simulation example of a wind farm including three doubly-fed induction generators(DFIGs) is established on CloudPSS simulation platform to test the proposed method. The test results verify the generalization ability and effectiveness of Neural ODEs model.