scholarly journals Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 184 ◽  
Author(s):  
Xiaobing Kong ◽  
Lele Ma ◽  
Xiangjie Liu ◽  
Mohamed Abdelkarim Abdelbaky ◽  
Qian Wu
Keyword(s):  
Model Predictive Control ◽  
Wind Turbine ◽  
Predictive Control ◽  
Economic Model ◽  
Mechanical Load ◽  
Wind Turbine Control ◽  
Dynamic Economy ◽  
Economic Model Predictive Control ◽  
Power Maximization ◽  
Installed Capacity

With the gradual increase in the installed capacity of wind turbines, more and more attention has been paid to the economy of wind power. Economic model-predictive control (EMPC) has been developed as an effective advanced control strategy, which can improve the dynamic economy performance of the system. However, the variable-speed wind turbine (VSWT) system widely used is generally nonlinear and highly coupled nonaffine systems, containing multiple economic terms. Therefore, a nonlinear EMPC strategy considering power maximization and mechanical load minimization is proposed based on the comprehensive VSWT model, including the dynamics of the tower and the gearbox in this paper. Three groups of simulations verify the effectiveness and reliability/practicability of the proposed nonlinear EMPC strategy.

An Adaptive Economic Model Predictive Control Approach for Wind Turbines

2017 ◽  
Vol 140 (5) ◽  
Author(s):  
Mohamed L. Shaltout ◽  
Zheren Ma ◽  
Dongmei Chen
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Wind Energy ◽  
Wind Turbine ◽  
Predictive Control ◽  
Economic Model ◽  
Adaptive Approach ◽  
Energy Capture ◽  
Model Plant ◽  
Economic Model Predictive Control

Motivated by the reduction of overall wind power cost, considerable research effort has been focused on enhancing both efficiency and reliability of wind turbines. Maximizing wind energy capture while mitigating fatigue loads has been one of the main goals for control design. Recent developments in remote wind speed measurement systems (e.g., light detection and ranging (LIDAR)) have paved the way for implementing advanced control algorithms in the wind energy industry. In this paper, an LIDAR-assisted economic model predictive control (MPC) framework with a real-time adaptive approach is presented to achieve the aforementioned goal. First, the formulation of a convex optimal control problem is introduced, with linear dynamics and convex constraints that can be solved globally. Then, an adaptive approach is proposed to reject the effects of model-plant mismatches. The performance of the developed control algorithm is compared to that of a standard wind turbine controller, which is widely used as a benchmark for evaluating new control designs. Simulation results show that the developed controller can reduce the tower fatigue load with minimal impact on energy capture. For model-plant mismatches, the adaptive controller can drive the wind turbine to its optimal operating conditions while satisfying the optimal control objectives.

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