Pitch angle controller of variable-speed wind turbine based on L&lt;inf&gt;1&lt;/inf&gt; adaptive control theory

Luo, Qingshun; Yang, Qinmin; Cheng, Peng

doi:10.1109/icmc.2014.7231695

Cited by 4 publications

(9 citation statements)

References 13 publications

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“…It should be noted that the aerodynamic torque T a is a decreasing function of β [], which indicates that

g false(v, ω_{r}, β false) < 0 .

…”

Section: Controller Methodologymentioning

confidence: 99%

“…To testify the performance of the proposed controller in extreme wind condition, a simplified but widely adopted wind gust model [] is utilized, which can be written as follows

v = ({, \begin{matrix} array \\ array 13, t < T_{s g} \\ array 13 + A_{g} [1 - \cos (\frac{2 π (t - T_{s g})}{D_{g}})] \\ array 13, T_{e g} < t \end{matrix}), T_{s g} \leq t \leq T_{e g}

where A g , T s g , T e g and D g are the amplitude, starting time, end time, and duration of the wind gust, respectively. In this study, these parameters are chosen as: T s g = 10s, A g = 5.7m/s, T e g = 40s, D g = 30s.…”

Section: Verification Studiesmentioning

confidence: 99%

“…For large-scale wind turbines, c is commonly much smaller than J t , i.e., the dynamics of pitch systems runs much faster than that of the driven-train mechanical systems. Therefore, ref can be assumed to be equal to [16] from the perspective of driven-train systems. Assumption 1.…”

Section: Remarkmentioning

confidence: 99%

“…To testify the performance of the proposed controller in extreme wind condition, a simplified but widely adopted wind gust model [16] is utilized, which can be written as follows…”

Section: Wind Gustmentioning

confidence: 99%

“…Moreover, it can be readily obtained from (17) that the regulation error e is also bounded. From (16), the boundness of e implies the boundness of r . Further, from (15), P g is also bounded.…”

Section: Appendix Bmentioning

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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“…It should be noted that the aerodynamic torque T a is a decreasing function of β [], which indicates that

g false(v, ω_{r}, β false) < 0 .

…”

Section: Controller Methodologymentioning

confidence: 99%

“…To testify the performance of the proposed controller in extreme wind condition, a simplified but widely adopted wind gust model [] is utilized, which can be written as follows

v = ({, \begin{matrix} array \\ array 13, t < T_{s g} \\ array 13 + A_{g} [1 - \cos (\frac{2 π (t - T_{s g})}{D_{g}})] \\ array 13, T_{e g} < t \end{matrix}), T_{s g} \leq t \leq T_{e g}

Section: Verification Studiesmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

“…To testify the performance of the proposed controller in extreme wind condition, a simplified but widely adopted wind gust model [16] is utilized, which can be written as follows…”

Section: Wind Gustmentioning

confidence: 99%

Section: Appendix Bmentioning

confidence: 99%

See 3 more Smart Citations

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

Jiao

Yang

Fu³

et al. 2018

Asian Journal of Control

Self Cite

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show abstract

Emulation of wind conversion chain equipped with a doubly fed induction generator

Azzaoui

Mahmoudi

Bossoufi

et al. 2016

2016 International Symposium on Fundamentals of Electrical Engineering (ISFEE)

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The fuzzy-PID based-pitch angle controller for small-scale wind turbine

Ngo¹,

Chai²,

Nguyen³

2020

IJPEDS

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<p>This paper aims to design the pitch angle control based on proportional–integral–derivative (PID) controller combined with fuzzy logic for small-scale wind turbine systems. In this control system, the pitch angle is controlled by the PID controller with their parameter is tuned by the fuzzy logic controller. This control system can compensate for the nonlinear characteristic of the pitch angle and wind speed. A comparison between the fuzzy-PID-controller with the conventional PID controller is carried out. The effectiveness of the method is determined by the simulation results of a small wind turbine using a permanent magnet generator (PMSG).</p>

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Pitch angle controller of variable-speed wind turbine based on L<inf>1</inf> adaptive control theory

Cited by 4 publications

References 13 publications

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

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

Emulation of wind conversion chain equipped with a doubly fed induction generator

The fuzzy-PID based-pitch angle controller for small-scale wind turbine

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