A sliding mode model reference adaptive control of PMSG wind turbine

Torki, Wafa; Grouz, Faten; Sbita, Lassaâd

doi:10.1109/gecs.2017.8066199

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…This coefficient, or blade efficiency, is upper-bounded by the Betz limit and is a function of the blade angle and the tip speed ratio . This tip speed ratio is described using [21] (4)…”

Section: System Modelmentioning

confidence: 99%

“…The coefficient is an incredibly nonlinear quantity. Additionally, it is not measurable in existing windmill control systems, and therefore there are numerous attempts at mathematically estimating this quantity, which are similar in structure but have nuanced differences [10,20,21,28,36,37].…”

Section: System Modelmentioning

confidence: 99%

“…The electrical subsystem of a PMSG system can be mathematically modelled in the rotating-reference frame as [21]…”

Section: System Modelmentioning

confidence: 99%

“…Transitioning away from linear strategies, improvements have been made by utilizing nonlinear schemes such as the sliding-mode control [20][21][22], which targets the issue of parameter uncertainties. Sliding mode controllers manage uncertainties through variable structure control and use a model-based approach to improve dynamic response times, oftentimes utilizing backstepping techniques [23].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Nonlinear Controller for PMSG Wind Turbines

Hawkins

McIntyre

2021

Energies

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In this paper, a nonlinear backstepping controller is proposed to manage the performance of a full-variable permanent magnet synchronous generator wind turbine. This nonlinear controller achieves a rotational speed objective for the generator and is validated through Lyapunov-based stability analysis. Additionally, this objective is accomplished without a measurement for wind speed that influences the wind torque. The proposed scheme is compared to a typical linear controller through simulation results. Simulation tests are performed to compare the response time and average error of each controller in both a drastic and realistic dynamic wind environment. These results demonstrate that the proposed controller is far more robust to wind turbulence than traditional control schemes. The simulation results of this study indicate a 1000% increase in response time and 3000% decrease in average controller error.

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“…This coefficient, or blade efficiency, is upper-bounded by the Betz limit and is a function of the blade angle and the tip speed ratio . This tip speed ratio is described using [21] (4)…”

Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

“…The electrical subsystem of a PMSG system can be mathematically modelled in the rotating-reference frame as [21]…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Robust Nonlinear Controller for PMSG Wind Turbines

Hawkins

McIntyre

2021

Energies

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“…K′ = σ′c y 4Fsin ϕcosϕ (19) The induction factors (a, a′) are obtained depending on the momentum region. If ϕ > 0 and K < 2/3, then the axial induction factor is…”

Section: Blade Element Momentum Modelmentioning

confidence: 99%

Wind turbine power coefficient models based on neural networks and polynomial fitting

Carpintero-Rentería

Santos-Martín

Lent

et al. 2020

IET Renewable Power Generation

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The power coefficient parameter represents the aerodynamic wind turbine efficiency. Since the 1980s, several equations have been used in the literature to study the power coefficient as a function of the tip speed ratio and the pitch angle. In this study, these equations are reviewed and compared. A corrected blade element momentum algorithm is used to generate three sets of data representing different ranges of wind turbines, going from 2 to 10 MW. With this information, two power coefficient models are proposed and shared. One model is based on a polynomial fitting, whereas the other is based on neural network techniques. Both were trained with the blade element momentum model output data and showed good behaviour for all operating ranges. In the results, compared to all the algorithms found in the literature, the proposed models reduced the power coefficient error by at least 55% compared to the best numerical approximation from the literature. An error reduction in the power coefficient parameter may have a large impact on many wind energy conversion system studies, such as those treating dynamic and transient behaviours.

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Multi‐input enhanced model reference adaptive control strategies and their application to space robotic manipulators

Montanaro

Martini

Hao

et al. 2023

Intl J Robust & Nonlinear

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The Enhanced Model Reference Adaptive Control (EMRAC) algorithm, augmenting the MRAC strategy with adaptive integral and adaptive switching control actions, is an effective solution to impose reference dynamics to plants affected by parameter uncertainties, unmodeled dynamics and disturbances.However, the design of the EMRAC solutions has so far been limited to single-input systems. To cover the gap, this paper presents two extensions of EMRAC to multi-input systems. The adaptive mechanism of both solutions includes the 𝜎-modification strategy to assure the boundedness of the adaptive gains also in presence of persistent disturbances. The closed-loop system is analytically studied, and conditions for the asymptotic convergence of the tracking error are presented. Furthermore, when the plant is subjected to unmatched disturbances, the ultimate boundedness of the closed-loop dynamics, which are made discontinuous by the adaptive switching control actions, is systematically proven by using Lyapunov theory for Filippov systems. The problem of trajectory tracking for space robotic arms in presence of unknown and noncooperative targets is used to test the effectiveness of the novel multi-input EMRAC algorithms for taming uncertain systems. Four EMRAC solutions are designed for this engineering application, and tested within a high fidelity simulation framework based on the Robot Operating System. Finally, the tracking performance of the EMRAC implementations is quantitatively evaluated via a set of key performance indicators in the joint space and operational space, and compared with that of four benchmarking controllers.

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A sliding mode model reference adaptive control of PMSG wind turbine

Cited by 8 publications

References 8 publications

A Robust Nonlinear Controller for PMSG Wind Turbines

A Robust Nonlinear Controller for PMSG Wind Turbines

Wind turbine power coefficient models based on neural networks and polynomial fitting

Multi‐input enhanced model reference adaptive control strategies and their application to space robotic manipulators

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