Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions

Song, Dongran; Yang, Jian; Fan, Xinyu; Liu, Yao; Liu, Anfeng; Chen, Guo; Joo, Young Hoon

doi:10.1016/j.enconman.2017.12.019

Cited by 105 publications

(57 citation statements)

References 24 publications

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“…20, In particular, the obtained results are showing the superiority of delay-dependent stability criteria than that by Raja et al 26 with free-weighting matrix method, that by Zhang et al 39 with extended Wirtinger's integral inequalities, and that by Qian et al 47 with decomposing the delay interval into multiple equidistant subinterval method. 3. Numerical examples and simulation results are carried out to prove the importance of proposed stability analysis through comparisons with existing results.…”

Section: Corollarymentioning

confidence: 99%

“…A real‐time control system designing with wireless communication networks has received constant attention in many control fields such as spacecraft systems, wind turbine models, and automated vehicles . This form of control systems is often called networked control systems (NCSs), and its communication channels are interconnected sensors with controllers and/or controllers with actuators.…”

Section: Introductionmentioning

confidence: 99%

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Robust reliable H_∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

Arunagirinathan

Muthukumar

Joo

2019

Intl J Robust & Nonlinear

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Summary In this paper, the robust reliable H∞ controller is designed for the problem of nonlinear actuator fault case in the uncertain networked control systems with randomly occurring missing data. More precisely, the occurrence of missing measurements is modeled by a stochastic variable in terms of Bernoulli random distribution. Also, the consideration of a nonlinear term in the input control scheme is a novel work for the proposed model. Suitable robust reliable design of control for a practical actuator fault model is constructed to guarantee the asymptotic stability of the system with H∞ performance level. A new form of Lyapunov‐Krasovskii functional with triple integral terms are formulated, and the reciprocally convex technique is utilized to establish the sufficient stability criterion in the form of linear matrix inequalities. Finally, the effectiveness of the proposed control method is shown through numerical examples, and we can confirm that the derived condition attained less conservatism than the existing results.

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Section: Corollarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust reliable H_∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

Arunagirinathan

Muthukumar

Joo

2019

Intl J Robust & Nonlinear

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“…Additionally, some advanced control strategies applied to enhance the operation of the yaw system of individual wind turbines have been found in the literature. Song et al present two variations of a predictive control strategy applied to the yaw operation of the wind turbine. The use of estimators to anticipate the wind direction is shown to improve the performance of the classical yaw control methods.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

et al. 2019

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The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)-based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5-MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.

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“…Environment problems such air pollution and global warming caused by fossil fuels have drawn the world's attention to exploration and utilization of renewable energy sources, in recent years [1][2][3][4][5]. At present, wind energy is the fastest growing renewable energy source and is most prevalent in coastal regions spanning temperate and boreal climates [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A PI‐Type Sliding Mode Controller Design for PMSG‐Based Wind Turbine

Zhou

Zhao

et al. 2019

Complexity

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Because the PMSG (permanent magnet synchronous generator)-based WECS (wind energy conversion system) has some uncertainties, the conventional control strategy with poor robustness is sometimes difficult to meet the performance requirements of control. In order to ensure efficient and stability of the system, this paper proposed a novel PI (proportional-integral)-type SMC (sliding mode control) strategy for PMSG-based WECS uncertainties and presented the detailed analysis and design process. Compared with the conventional control method, the PI-type SMC proposed in this paper not only can make the closed-loop system globally stable, but also has a better robustness and slightly reduced the current ripple and distortion. Finally, the simulation results verify the correctness and effectiveness of this algorithm.

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Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions

Cited by 105 publications

References 24 publications

Robust reliable H_∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

Robust reliable H_∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

A PI‐Type Sliding Mode Controller Design for PMSG‐Based Wind Turbine

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Maximum power extraction for wind turbines through a novel yaw control solution using predicted wind directions

Cited by 105 publications

References 24 publications

Robust reliable H∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

Robust reliable H∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

A PI‐Type Sliding Mode Controller Design for PMSG‐Based Wind Turbine

Contact Info

Product

Resources

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Robust reliable H_∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements

Robust reliable H_∞ control design for networked control systems with nonlinear actuator faults and randomly missing measurements