Collective Pitch Control with Active Tower Damping of a Wind Turbine by Using a Nonlinear PID Approach

Gambier, Adrián; Nazaruddin, Yul Y.

doi:10.1016/j.ifacol.2018.06.072

Cited by 19 publications

(11 citation statements)

References 7 publications

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“…The linear models are given by the transfer functions from (13) to (16), where G ωg (s) is the transfer function relating the generator speed (ω g ) in rpm to the pitch angle (β) in degrees; Gd ωg (s) is the transfer function relating the controlled variable ω g to the wind speed (v w ) in m/s as disturbance input; G x t (s) is the transfer function of the tower f-a displacement (x t ) in m as output and the pitch angle (β) as input, and Gd x t (s) is the transfer function relating the controlled variable x t to the wind speed v w .…”

Section: Identification Of Linear Models Of the Wind Turbinementioning

confidence: 99%

“…Adaptive feedforward control is performed based on the multiple linear models identified previously in (13) and (14). Depending on the wind speed v w and generator speed ω g , the value of the gain and time constant of the dynamics G ωg (s) and Gd ωg (s) are linearly modified, and then the transfer function of the feedforward compensator Ca(s) is updated and calculated according to (21).…”

Section: Adaptive Feedforward Compensationmentioning

confidence: 99%

“…The fore-aft movement is the tower rocking from back to front. There is a wide variety of works dealing with the mitigation of this vibration mode [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

et al. 2021

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This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform profile of the wind causes tower displacements that must be reduced to improve the wind turbine lifetime. In this work, an adaptive control structure operating on the pitch angle variable is proposed for a nonlinear model of a wind turbine provided by FAST software. The proposed control structure is composed of a gain scheduling proportional–integral (PI) controller, an adaptive feedforward compensation for the wind speed, and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as a Pareto optimization problem that minimizes the tower fore-aft displacements and the deviation of the generator speed using multi-objective genetic algorithms. Three multi-criteria decision making (MCDM) methods are compared, and a satisfactory solution is selected. The optimal solutions for power generation and for tower fore-aft displacement reduction are also obtained. The performance of these three proposed solutions is evaluated for a set of wind pattern conditions and compared with that achieved by a classical baseline PI controller.

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Section: Identification Of Linear Models Of the Wind Turbinementioning

confidence: 99%

Section: Adaptive Feedforward Compensationmentioning

confidence: 99%

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Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

et al. 2021

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“…The fore-aft movement is the tower rocking from back to front. There is a wide variety of works dealing with the mitigation of this mode [11], [12], [13]. Some commercial controllers use pitch control to actively damp the tower fore-aft (f-a) motion when the wind turbine is operating at region IV.…”

Section: B Structural Loadsmentioning

confidence: 99%

Adaptive PI control and active tower damping compensation of a wind turbine

Lara¹,

Garrido²,

Vázquez³

2020

REPQJ

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This paper deals with the control problem of a wind turbine model working in the nominal zone. This process is a nonlinear system whose dynamics vary strongly depending on the operation point. In the nominal region, the wind turbine speed is controlled by means of the pitch angle to generate the nominal power. The wind fluctuations and its non-uniform special profile act as disturbances on the power generation and the tower deflections. These oscillations must be reduced to improve the wind turbine lifetime.In this work, an adaptive control structure operating on the pitch variable is proposed. It is composed of a gainscheduling PI control, an adaptive feedforward compensation of the wind speed and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as an optimization problem that minimizes the tower fore-aft displacements and the deviation of the wind turbine speed from its nominal value. It is resolved using genetic algorithms for different linear models that are obtained from the nonlinear model.The proposed controller is compared with a classical baseline PI (Proportional-integral) controller and the simulation results show a significant improvement of the system performance when the proposed strategy is applied.

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“…The controller requires tuning the values of PID gain parameters in order to get the best performance of the controller. Changing these Parameters will cause changes in the system response compared to the required response [56], [57].…”

Section: ) Fuzzy Rulesmentioning

confidence: 99%

Expert Control Systems Implemented in a Pitch Control of Wind Turbine: A Review

et al. 2019

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Wind energy is the strongest renewable energy source developed in recent decades. Being systems that are directly connected to the grid of the electrical system, it is essential to use the maximum available power of the wind and obtain the maximum electrical power converted from the turbine. In this paper, the fundamental problem of the wind turbine is how to obtain at all times the maximum output power of the turbine in a wide range of wind speed. The randomness of the wind adds an intrinsic difficulty to be able to plan the available wind energy in advance. To solve this problem, it is not necessary to know the dynamic operation of the system; we must anticipate the control response to each one of the different probable scenarios. An expert control system can be used based on human knowledge and experience, which, through proper management of its variables and adequate control of criteria to manipulate stored data, provides a way to determine solutions. In other words, it is a model of the experience of professionals in this field. The more variables in the system are considered, the more complete the model will be, and the more information will be available for decision-making, with a more efficient system and higher results in power generation as a response. For this reason, the objective of this paper is to present expert systems developed in recent years and, thus, offer a control solution that approximates the conditions of different wind turbines.INDEX TERMS Artificial neural network, fuzzy logic, genetic algorithms, wind power generation, control systems.

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Collective Pitch Control with Active Tower Damping of a Wind Turbine by Using a Nonlinear PID Approach

Cited by 19 publications

References 7 publications

Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

Adaptive PI control and active tower damping compensation of a wind turbine

Expert Control Systems Implemented in a Pitch Control of Wind Turbine: A Review

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