Adaptive Disturbance Tracking Control for Large Horizontal Axis Wind Turbines with Disturbance Estimator in Region II Operation

Balas, Mark J.; Magar, Kaman S. Thapa; Li, Qian

doi:10.2514/6.2011-817

Cited by 5 publications

(10 citation statements)

References 7 publications

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“…The design point for linearization is chosen in Region 3 where blade pitch angle is controlled to accommodate the generator speed to rated value and mitigate the structural loads. A design point with wind speed 15m/s v  , pitch angle 10 …”

Section: Model Descriptionmentioning

confidence: 99%

“…In order to achieve the maximum power in below-rated wind speeds (Region 2), Johnson presented an adaptive pitch controller to obtain the highest aerodynamic efficiency [9], Balas developed an adaptive Disturbance Tracking Control (DTC) theory to track the optimal tip speed ratio based on a wind speed estimator [10]. However, in above-rated wind speeds (Region 3), while adaptive methods exhibit good robustness in tracking specific speed under various operating conditions, they may fail to reduce the fatigue effect [11].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive pitch control for load mitigation of wind turbines

Yuan¹,

Tang²

2015

SPIE Proceedings

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In this research, model reference adaptive control is examined for the pitch control of wind turbines that may suffer from reduced life owing to extreme loads and fatigue when operated under a high wind speed. Specifically, we aim at making a trade-off between the maximum energy captured and the load induced. The adaptive controller is designed to track the optimal generator speed and at the same time to mitigate component loads under turbulent wind field and other uncertainties. The proposed algorithm is tested on the NREL offshore 5-MW baseline wind turbine, and its performance is compared with that those of the gain scheduled proportional integral (GSPI) control and the disturbance accommodating control (DAC). The results show that the blade root flapwise load can be reduced at a slight expense of optimal power output. The generator speed regulation under adaptive controller is better than DAC.

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Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive pitch control for load mitigation of wind turbines

Yuan¹,

Tang²

2015

SPIE Proceedings

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“…Balas further developed an idea of ADTC and introduced in Balas, Li and Peterman for the control of wind turbine in Region II, where the wind speed was assumed to be measured. Despite its simple construction, eliminating the dependence on many wind turbine parameters and superior performance over fixed gain counterpart, the ADTC proposed in Balas, Thapa Magar and Li required the measurement of wind speed, which is not an obvious task. LiDAR and anemometer being two widely used wind speed measuring device, both of them cannot be a viable option because the former is expensive and the latter does not give accurate and reliable measurement.…”

Section: Adtc Theory With Partial State Estimation and State Feedbackmentioning

confidence: 99%

“…Instead of measuring the wind speed directly, an idea of estimating it using a simple wind speed estimator was proposed in Balas, Thapa Magar and Li. As the name suggests, this estimator had very simple construction and could estimate the wind speed from the rotor speed measurement, but from the theoretical analysis, this combination of estimator and controller was not found to be robust enough to use for the wind turbines. In this paper, another way of estimating the wind speed is proposed, which uses a simple one‐state linearized model of wind turbine to estimate the wind speed.…”

Section: Adtc Theory With Partial State Estimation and State Feedbackmentioning

confidence: 99%

Direct adaptive torque control for maximizing the power captured by wind turbine in partial loading condition

2015

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In this paper, a direct adaptive control approach is used to track the tip speed ratio (TSR) of wind turbine to maximize the power captured during the below rated wind speed operation. Assuming a known optimum value of TSR, the deviation of actual TSR from the optimum one is mathematically expressed as TSR tracking error. Since the actual TSR is not a measurable quantity, this expression for TSR tracking error is linearized and simplified to express it in terms of wind speed and rotor speed, where rotor speed can easily be measured. Although it is possible to measure the wind speed with high accuracy using LiDAR, using it raises the overall cost of wind turbine installation; hence, a method to estimate the wind speed is also proposed. The adaptive controller operates on this simplified TSR tracking error to drive it to zero and to keep the TSR constant at desired optimum value. The performance of the proposed control scheme is illustrated by implementing and simulating it in the National Renewable Energy Laboratory 5MW wind turbine model and comparing the results with the existing baseline fixed gain controller. Copyright © 2015 John Wiley & Sons, Ltd.

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“…Another extension of this adaptive control theory, developed by Balas et al, is to maximize the power captured by a wind turbine where the wind speed is assumed to be directly measured [20]. The direct measurement of wind speed is either expensive when LiDAR is used or unreliable when a cup anemometer is used; therefore, we extended this theory to incorporate wind-speed estimation based on the lower-order linear model of wind turbines [21,22]. We demonstrate the effectiveness of this extended control system with wind-speed estimation through controller implementation and simulation on the National Renewable Energy Laboratory's (NREL's) 5 MW nonlinear wind turbine model [23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive State Feedback—Theory and Application for Wind Turbine Control

et al. 2017

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A class of adaptive disturbance tracking controllers (ADTCs) is augmented with disturbance and state estimation and adaptive state feedback, in which a controller and estimator, which are designed on the basis of a lower-order model, are used to control a higher-order nonlinear plant. The ADTC requires that the plant be almost strict positive real (ASPR) to ensure stability. In this paper, we show that the ASPR property of a plant is retained with the addition of disturbance and state estimation and state feedback, thereby ensuring the stability of the augmented system. The proposed adaptive controller with augmentation is presented in the context of maximum power extraction from a wind turbine in a low-wind-speed operation region. A simulation and comparative study on the National Renewable Energy Laboratory's (NREL's) 5 MW nonlinear wind turbine model with an existing baseline Proportional-Integral-Derivative(PID) controller shows that the proposed controller is more effective than the existing baseline PID controller.Keywords: wind energy; adaptive control; wind turbine control; maximum power point tracking BackgroundElectrical energy generated from wind power is one of the major sources of renewable energy. The size of the wind turbine has to be large in order to reduce the cost of the energy; however, this increase in size comes with the cost of added weights in its structures [1]. For large wind turbines, the blade is one of the most important structures that needs to be considered for both fatigue life and weights. Using stiff materials to construct wind turbine blades increases the blade's life, but it also increases its weight significantly. This requires a larger foundation and tower to support the blades, which results in high installation costs and an increase in the cost of energy. On the other hand, using lightweight materials reduces the weight significantly, but the blade becomes highly flexible, which makes it prone to damage by different types of aerodynamic and structural loading. Stiffening the blade is not a viable option for a large wind turbine. However, advents in modern control theory have made it possible to reduce the blade loading significantly, thereby making a flexible blade design a viable option [2]. Besides blade loading, other structural loading, such as drivetrain and tower loading, also needs to be considered to increase the life span of the wind turbine, which directly affects the cost of energy. Maximizing the power captured while operating the wind turbine at a below-rated wind speed is another important aspect of a wind turbine control system [3]. Modern wind turbines are capable of operating at variable rotor speeds to accompany the maximum power point tracking algorithm.

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Adaptive Disturbance Tracking Control for Large Horizontal Axis Wind Turbines with Disturbance Estimator in Region II Operation

Cited by 5 publications

References 7 publications

Adaptive pitch control for load mitigation of wind turbines

Adaptive pitch control for load mitigation of wind turbines

Direct adaptive torque control for maximizing the power captured by wind turbine in partial loading condition

Adaptive State Feedback—Theory and Application for Wind Turbine Control

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