基于深度神经网络的DFIG低电压穿越技术研究

[目的]双馈风机(DFIG)的低电压穿越(LVRT)性能在一定程度上依赖于控制参数的优化,而目前对控制参数的优化基本都是离线模式,原因在于优化算法难以满足实时控制对计算速度的要求.[方法]基于深度神经网络(DNN)原理,提出基于"离线训练、在线计算"思路的低电压穿越实时优化控制方法.首先针对含DFIG电网在不同运行方式...

Research on Low Voltage Ride Through Technology of DFIG Based on Deep Neural Networks

  Introduction  During the power grid fault， the low voltage ride through （LVRT） performance of the doubly-fed induction generator （DFIG） depends on the control parameters. At present， the optimization of control parameters is basically in the offline mode， which lies in the fact that it's hard for algorithm optimization to meet real-time control's requirement of the calculation speed.  Method  Therefore， the real-time optimization control method of LVRT following“offline training， online computation” was presented based on the principles of deep neural networks （DNN）. Firstly， the appropriate LVRT strategy for optimization control was proposed for different fault severity levels. The parameters were clustered and optimized according to the respective objectives of each strategy， then the parameter list was formed.  Result  At the moment of power grid fault， the input parameters can be directly input into the trained DNN networks to quickly realize the optimization of control scheme and optimal parameters.  Conclusion  The joint simulation results based on PSCAD and MATLAB demonstrate the advantages of the proposed idea in optimization effect and optimization speed and the practicability is also illustrated.