LIDAR-based FX-RLS feedforward control for wind turbine load mitigation

Wang, Na; Johnson, Kathryn; Wright, Alan

doi:10.1109/acc.2011.5991148

Cited by 12 publications

(16 citation statements)

References 14 publications

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“…Method A involves the cost of installing anemometers and the difficulty of determining the effective wind speed accurately by means of anemometers. Wind farm control by means of light detection and ranging systems (LIDAR) has been attracting attention, and if the deployment costs continue to be reduced and deployment in wind turbine facilities spreads, the scope of this form of control can be expected to increase [9,10].…”

Section: Mppt Operationmentioning

confidence: 99%

“…In the latter, problems include measurement errors due to the use of integrated values and the time taken to detect the maximum power point. Various efforts have been made to resolve these concerns [10][11][12][13][14][15][16][17]. Methods using fuzzy logic are promising, but they have problems such as the difficulty and the effort involved in designing an appropriate membership function [20,21].…”

Section: Mppt Operationmentioning

confidence: 99%

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A Dynamic Control Strategy for Wind Turbines in Wind Farms

Kawaguchi

Shimada

2015

Electrical Engineering Japan

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SUMMARY This paper presents a new control method for wind turbine groups by use of dynamic control including pump‐up operation. Under conditions of oscillating wind below the rated speed of the wind turbine, maximum power point tracking (MPPT) operation is critical to improving efficiency. Even though, owing to the huge inertia of a wind turbine, the operating point of the wind turbine produces an unavoidable delay at the maximum power point, if dynamic control is used wind turbines respond to the changes in the wind speed more rapidly. The wind turbines hence reach the maximum power point quickly and capture energy from the wind more efficiently. On the other hand, as a result of dynamic operation, the output from a wind turbine fluctuates more severely than during normal operation. In the proposed solution, by taking advantage of the kinematic energy stored in other wind turbines that belong to the same wind farm, not only the efficiency of output power but also the severity of the fluctuations is improved. In the results of simulations, group dynamic operation enables wind farms to improve their efficiency and smooth the output fluctuations from the wind farm.

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Section: Mppt Operationmentioning

confidence: 99%

Section: Mppt Operationmentioning

confidence: 99%

A Dynamic Control Strategy for Wind Turbines in Wind Farms

Kawaguchi

Shimada

2015

Electrical Engineering Japan

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“…Using LIDAR measurement, Dunne and Pao [20] designed a combined feedback/feedforward blade pitch controller to reduce the structural loads in blade root, tower base and top. In another study, FX-RLS-based feedforward control was suggested to mitigate both blade bending and tower moment while regulating the rotor speed with slightly sacrificed energy, which takes the advantage of the robustness characteristics and good disturbance rejection of the adaptive control [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive pitch control of wind turbine for load mitigation under structural uncertainties

Yuan

Tang

2017

Renewable Energy

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In this research, a new adaptive control strategy is formulated for the pitch control of wind turbine that may suffer from reduced life owing to extreme loads and fatigue when operated under high wind speed and internal structural uncertainties. Specifically, we aim at making a trade-off between the maximum energy captured and the load induced. The adaptive controller is designed to both regulate generator speed and mitigate component loads under turbulent wind field when blade stiffness uncertainties exist. The proposed algorithm is tested on the NREL offshore 5-MW benchmark wind turbine. The control performance is compared with those of the gain scheduled proportional integral (GSPI) control and the disturbance accommodating control (DAC) that are used as baselines. The results show that with the proposed adaptive control the blade root flapwise load can be reduced at a slight expense of optimal power output. Moreover, the blade load mitigation performance under uncertain blade stiffness reduction is improved over the baseline controllers. The control approach developed in this research is general, and can be extended to mitigating loads on other components.

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“…Especially methods based on Model Predictive Control (MPC) strategies and feed-forward disturbance compensation methods show promising results, e.g. (Schlipf et al, 2010;Wang and aerodynamics. Actuator limits play a very important role (rates and absolute limits) and the excitation of the flexible structure has also to be considered in both cases during gust load situations.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Inversion Control for Wind Turbine Load Mitigation based on Wind Speed Measurement

Reiner

Zimmer

2015

Linköping Electronic Conference Proceedings

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The design of an advanced controller for wind turbine load mitigation is presented. The controller is based on Nonlinear Dynamic Inversion control methods combined with Pseudo Control Hedging to account for the actuator limits and a two degree of freedom control system for the collective pitch control of the rotor blades. The controller uses wind speed measurement information to adjust to wind gust load. A newly developed wind turbine system dynamics library in the Modelica language is used to model an elastic wind turbine for a simulation study of the controller. The simulation results show a large reduction of the gust load on the wind turbine using the proposed controller.

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LIDAR-based FX-RLS feedforward control for wind turbine load mitigation

Cited by 12 publications

References 14 publications

A Dynamic Control Strategy for Wind Turbines in Wind Farms

A Dynamic Control Strategy for Wind Turbines in Wind Farms

Adaptive pitch control of wind turbine for load mitigation under structural uncertainties

Nonlinear Dynamic Inversion Control for Wind Turbine Load Mitigation based on Wind Speed Measurement

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