Tube-based model predictive control for wind power system using adaptive just-in-time learning modeling methodology
Abstract
The wind power generation process exhibits strong nonlinearity and multiple constraints, making it difficult to establish an accurate global model for model predictive control. In practical applications, model mismatch often leads to a decline in control performance. To address this, a tube-based model predictive control for wind power system using adaptive just-in-time learning modeling methodology is proposed. There are two core innovations: (1) A pre-clustering adaptive just-in-time learning method is adopted to construct a local dynamic model online as the nominal system, which ensures modeling accuracy while significantly reducing computational burden and (2) without explicitly distinguishing between the maximum power point tracking region and the pitch control region, a tube-based model predictive control strategy is developed so that the power tracking error is constrained within a Tube invariant set centered on the nominal system, effectively suppressing the effects of wind speed randomness and model mismatch. Simulation results on a 5-MW wind turbine demonstrate that the proposed strategy can smooth power fluctuations, improve tracking accuracy, and achieve superior robustness and computational efficiency.