Stochastic inequality constrained closed-loop model-based predictive control of MW-class wind generating system in the electric power supply

Wind turbines have become the most cost-effective renewable energy systems available today and are now completely competitive with essentially all conventional generation systems. However, wind stochasticity results in fluctuations in output power as well as undesirable dynamic loading of the drive train during high turbulence. A model-based predictive control strategy for the field-oriented control of a doubly fed induction generator is presented. The control region is defined over two wind profiles: average wind speeds below and above equipment rating, subject to assigned constraints of the maximum allowable system frequency fluctuations and the power limit of the wind generating system. To meet the control objectives of maximising energy capture and alleviation of drive train fatigue loads, each of the WGS component blocks is modelled separately so as to explore the associated trade-offs. Simulations, carried out under a Matlab^?? environment, serve to verify that the proposed paradigm performs better than the classical linear proportional-integral controller in achieving the regulation of torsional dynamics while maintaining optimal operation.