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

Abstract

[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.

Keywords

• double-fed induction generator
• low voltage ride through
• deep neural networks