Hysteresis‐Based Design of Dynamic Reference Trajectories to Avoid Saturation in Controlled Wind Turbines

Tutivén, Christian; Vidal, Yolanda; Acho, Leonardo; Rodellar, José

doi:10.1002/asjc.1383

Cited by 15 publications

(27 citation statements)

References 31 publications

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“…C q ( λ , β ) is generally determined experimentally and provided by the manufacturers []. In practice, a acceptable numerical approximation can be adopted []

C_{q} false(λ, β false) = \frac{0.5176}{λ} false(\frac{116}{λ_{i}} - 0.4 β - 5 false) e^{- 21 false/ λ_{i}} + 0.0068

where

\frac{1}{λ_{i}} = \frac{1}{λ + 0.08 β} - \frac{0.035}{β^{3} + 1} .

…”

Section: Model Of Vswtmentioning

confidence: 99%

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Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Jiao

Yang

Fu³

et al. 2018

Asian Journal of Control

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As wind energy becomes one of the fastest growing renewable energy resources, the control of large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. In this paper, an adaptive neural pitch angle control strategy is proposed for the variable-speed wind turbines (VSWT) operating in pitch control region. The control objective is to maintain the rotor speed and generator power at the prescribed reference values in the presence of external disturbance, without the need of the information of system parameters and aerodynamics. First, the order of the system dynamics is increased by defining a filtered regulation error. By this means, the non-affine characteristics of the VSWT model is transformed into a simple affine control problem and thus the feedback linearization technique can be employed. The continuousness of control signal is also guaranteed to relax the requirement on the bandwidth of actuators, and the mechanical load on pitching systems is reduced. Subsequently, an online learning approximator (OLA) is utilized to estimate the unknown nonlinear aerodynamics of the wind turbine and extend the practicability of the proposed adaptive parameter-free controller. In addition, a high-gain observer is implemented to obtain an estimation of rotor acceleration, which rejects the need of additional sensors. Rigid theoretical analysis guarantees the tracking of rotor speed/generator power and the boundedness of all other signals of the closed-loop system. Finally, the effectiveness of the proposed scheme is testified via the Wind Turbine Blockset simulation package in Matlab/Simulink environment. Moreover, comparison results reveal that the introduced solution is able to provide better regulation performance than the conventional PI counterpart.

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“…C q ( λ , β ) is generally determined experimentally and provided by the manufacturers []. In practice, a acceptable numerical approximation can be adopted []

C_{q} false(λ, β false) = \frac{0.5176}{λ} false(\frac{116}{λ_{i}} - 0.4 β - 5 false) e^{- 21 false/ λ_{i}} + 0.0068

where

\frac{1}{λ_{i}} = \frac{1}{λ + 0.08 β} - \frac{0.035}{β^{3} + 1} .

…”

Section: Model Of Vswtmentioning

confidence: 99%

“…The dynamics of pitch actuators is usually represented by a first‐order system

\overset{β}{true} = - \frac{1}{τ_{c}} β + \frac{1}{τ_{c}} β_{r e f}

, with 0.2 < τ c < 0.25 []. For large‐scale wind turbines, τ c is commonly much smaller than J t , i.e.…”

Section: Model Of Vswtmentioning

confidence: 99%

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Jiao

Yang

Fu³

et al. 2018

Asian Journal of Control

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“…An interesting characteristic of the reported functions

g false({\overset{x}{true}}_{b} false) = {\overset{x}{true}}_{b}, g false({\overset{x}{true}}_{b} false) = sgn ((), {\overset{x}{true}}_{b})

, and the function in Equation is that all of them are passive in the sense that

g ({\dot{x}}_{normalb}) \cdot {\dot{x}}_{normalb} \geq 0, g (0) = 0 .

In this work, a different function g is proposed that is based on an evolutionary variable η as follows:

g false({\overset{x}{true}}_{b} false) = η,

\overset{η}{true} = ϕ ({}, - η + b sgn ([], c {\overset{x}{true}}_{b} + a sgn ((), η))),

where ϕ is a positive real number and a , b , and c —also positive—are the hysteresis loop parameters shown in Figure . It is worth noting that this is a bounded‐input bounded‐output stable system based on the hysteretic system previously proposed in Tutivén et al In the current approach, the system proposed in Tutivén et al is modified by adding c , a third hysteresis loop parameter that multiplies the velocity. The transition speed between b and − b or viceversa is controlled by the positive parameter ϕ , whereas b is an upper bound on the magnitude of η ( t ), that is, | η ( t )| ≤ b , t ≥ 0.…”

Section: Control Designmentioning

confidence: 99%

“…It is worth noting that this is a bounded-input bounded-output stable system based on the hysteretic system previously proposed in Tutivén et al 34 In the current approach, the system proposed in Tutivén et al 34 is modified by adding c, a third hysteresis loop parameter that multiplies the velocity. The transition speed between b and −b or viceversa is controlled by the positive parameter , whereas b is an upper bound on the magnitude of (t), that is,…”

Section: Hysteretic Control 221 Control Objective and Designmentioning

confidence: 99%

Hysteretic active control of base-isolated buildings

Pozo

Vidal

Garcia

et al. 2018

Struct Control Health Monit

Self Cite

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Summary In this work, an active control law for base‐isolated buildings is proposed. The crucial idea comes from the observation that passive base‐isolation systems are hysteretic. Thus, an hysteretic active control strategy is designed in a way that the control force is smooth and limited by a prescribed bound. Furthermore, given a specific actuator with a physically limited maximum force and maximum rate of change, it is proven that the design parameters in the contributed control law can be chosen such that the control signal inherently satisfies the actuator constraints. Eight different ground‐acceleration time‐history records and a model of a 5‐story building are used to study and compare the performance of a passive pure friction damper alone, with the addition of the proposed active control. Numerical analysis demonstrates that our control strategy effectively mitigates base displacement and shear without an increase in superstructure drift or acceleration.

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“…Mathematical models of the wind turbines are presented in [18] and [19]. Mathematical models of the wind turbines are presented in [18] and [19].…”

Section: Introductionmentioning

confidence: 99%

Control of two Electrical Plants

Rubio¹,

López

Pacheco³

et al. 2017

Asian Journal of Control

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In this paper, a controller is recommended for the regulation of two electrical plants. Since electrical plants generate electricity all the time, the regulation to get that all the plant states reach constant behaviors is important. Two main characteristics of the introduced method are: (i) it is based in the separation of the plant model equations, only some model equations are chosen for the regulation while the other model equations are ignored, it avoids the difficulty in the consideration of the full plant model; (ii) the Lyapunov strategy is employed to analyze the stability of the selected model equations in the electrical plant, it lets to ensure the regulation purpose. The advised method is applied in a gas turbine and a wind turbine for the electricity generation.

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Hysteresis‐Based Design of Dynamic Reference Trajectories to Avoid Saturation in Controlled Wind Turbines

Cited by 15 publications

References 31 publications

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Adaptive Continuous Neural Pitch Angle Control for Variable‐Speed Wind Turbines

Hysteretic active control of base-isolated buildings

Control of two Electrical Plants

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