Large wind turbines control using stable economic model predictive pitch technique with state constraints.

Area of Science:

• Renewable Energy Systems
• Control Engineering
• Mechanical Engineering

Background:

• Wind turbine pitch controllers face challenges with over-rated speeds, including nonlinearity, stability, and economic operation.
• Existing controllers struggle to balance power regulation with economic efficiency under variable conditions.

Purpose of the Study:

• To develop and validate a novel pitch control strategy for variable speed/pitch wind turbines (VSPWTs) operating at over-rated wind speeds.
• To enhance economic performance and ensure closed-loop stability despite persistent disturbances.

Main Methods:

• Implementation of a stable economic model predictive control (EMPC) technique.
• Integration of dynamic economic and tracking optimization into a single online framework.
• Design of an optimal linear feedback controller using constrained linear matrix inequalities (LMIs) for stability.

Main Results:

• The proposed EMPC pitch controller demonstrated superior performance compared to standard and advanced controllers.
• Validation using a nonlinear VSPWT model and a 5-MW VSPWT simulator confirmed effectiveness.
• The controller successfully regulated generator speed to harvest rated power under over-rated wind conditions.

Conclusions:

• The developed EMPC-based pitch control strategy offers a robust and economically superior solution for VSPWTs.
• This approach effectively addresses pitch angle and state constraints while ensuring system stability and economic operation.
• The findings highlight the potential of EMPC for optimizing wind energy capture and turbine performance.