A wind turbine achieves its highest energy efficiency during partial load operation when it operates at the optimal tip speed ratio (TSR), thus the optimal power coefficient. In this paper, real-time controllers are developed to improve the performance of tracking the optimal TSR during partial load operation. Dynamic programming (DP) is first applied to determine the control actions that maximize wind energy capture. A DP-based real-time controller (DPRC) is then explored to overcome the high computational expense associated with DP, which limits DP to be an offline optimization algorithm. However, the DPRC is not robust against plant-model mismatch and model uncertainties. A gain-modified optimal torque controller (GMOTC) is subsequently designed as an alternative to the DPRC. The GMOTC applies internal Proportional-Integral technique to track a reference TSR, and adapts the reference TSR to the optimal TSR in real time to improve the controller robustness. The light detection and ranging technology is used to further strengthen the controller performance by providing reliable previewed wind speed measurements. Simulation results show that the DRPC generates more wind power than the standard torque controller (STC), while the GMOTC demonstrates a performance similar to that of the DRPC on wind power generation with much better robustness in the presence of modeling error. Fatigue loading on a wind turbine is another important issue that needs to be considered during control design. The analysis shows that both the DRPC and the GMOTC are comparable with the STC in generating variable torsional loads due to the torque commands.

Hybrid electric vehicles (HEVs) have attracted considerable attention due to their potential to reduce fuel consumption and emissions. The objective of this paper is to enhance our understanding of the associated tradeoffs among the HEV subsystems, e.g., the engine, the motor, and the battery, and investigate the related implications for fuel consumption and battery capacity and lifetime. Addressing this problem can provide insights on how to prioritize these objectives based on consumers' needs and preferences. The results of the proposed optimization approach can also be used to investigate the implications for HEV costs related to ownership and warranty.

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.

An optimal control approach for a wind turbine drivetrain with a variable ratio gearbox is presented. The objective is to find the optimum shifting sequence of the variable ratio gearbox in order to maximize power generation and extend gear life. The employment of a variable ratio gearbox enhances the capabilities of the wind turbine to cope with wind speed variations. Based on the authors' preliminary study, the gear ratios of the variable ratio gearbox were carefully selected to maximize the wind energy capture. In this paper, a new control approach is proposed to achieve both extended gear service life and optimal energy harvesting. This new approach finds the gear shifting sequence that will minimize the tangential force on the gear tooth while maximizing the wind energy capture. The wind turbine drivetrain with a variable ratio gearbox is modeled and simulation results based on recorded wind data of different wind classes are presented and compared.

Estimation of wind speed distribution is essential for wind energy resources assessment, design of wind farms, and selection of suitable wind turbines. Two-parameter Weibull distribution function is widely used worldwide for wind energy resources assessment. As a case study, 1one-year field measurements at Gabal Al-Zayt wind farm in Egypt are used to estimate the Weibull parameters and to accurately assess the wind energy resource. In this work, seven statistical methods are adopted to estimate the Weibull parameters and their estimation accuracy is compared based on some common estimation errors. However, the improvement in one estimation error does not necessarily improve other types of errors. Consequently, a multi-objective genetic algorithm (MOGA) is adopted to investigate the tradeoffs among the competing estimation errors and to enhance the assessment of wind energy resources. The results show significant improvement in the estimation accuracy of the Weibull parameters using MOGA as compared to conventional statistical estimation methods. On the other hand, the case study at Gabal Al-Zayt wind farm reveals that the selection of wind turbines does not depend only on wind characteristics of the site but also on its environmental characteristics.