Model-Free Predictive Current Control of a DFIG Using an Ultra-Local Model for Grid Synchronization and Power Regulation
Yongchang Zhang, Tao Jiang, Jian Jiao
Abstract
Traditional model predictive control has difficulty in achieving satisfactory control performance when parameter identification is inaccurate or when a parameter changes. To solve this problem, this article proposed a new model-free predictive current control (MFPCC) scheme for a doubly fed induction generator (DFIG), which combines an ultra-local model with predictive current control (PCC). This method replaces the mathematical model of a DFIG with an ultra-local model and has strong parameter robustness. In addition, this method is suitable for the synchronization and power regulation processes of a DFIG, and it is also easy to apply to a DFIG with uncertain parameters. The proposed method is compared to traditional model-based deadbeat control with space vector modulation (SVM), and the presented experimental results confirm its superiority and effectiveness.