Power optimization and control in wind energy conversion systems using extremum seeking

Ghaffari, Azad; Krstić, Miroslav; Seshagiri, Sridhar

doi:10.1109/acc.2013.6580168

Cited by 39 publications

(51 citation statements)

References 19 publications

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“…This technique can be sensitive to noise and perturbation size, particularly close to the power curve peak. Similarly, Ghaffari et al [5] presented a maximum seeking algorithm using a sinusoidal dither signal added to the reference (command) rotor speed to estimate the gradient of the output power with respect to the rotor speed. The dither signal frequency must be low and its amplitude should be sufficiently small compared to the rotor speed [5].…”

Section: Review Of Maximum Power Seeking Techniquesmentioning

confidence: 99%

“…Similarly, Ghaffari et al [5] presented a maximum seeking algorithm using a sinusoidal dither signal added to the reference (command) rotor speed to estimate the gradient of the output power with respect to the rotor speed. The dither signal frequency must be low and its amplitude should be sufficiently small compared to the rotor speed [5]. In these techniques, one major problem that can lead to failure of the tracking process is the lack of distinction between the power differences resulting from the change in the wind with that resulting from a power change due to adding a perturbation [16].…”

Section: Review Of Maximum Power Seeking Techniquesmentioning

confidence: 99%

“…where ̂ is the estimated value of the power capture coefficient and ( , ) = ( 3 /2 ) ∝ 2 as defined in (5). The strategy is to make follows the desired value, * , which results in a linear input-output dynamic behavior for the equation of motion, i.e.…”

Section: A Feedback Linearization For the Torque Controlmentioning

confidence: 99%

“…INTRODUCTION arious control schemes for maximum power seeking in wind turbines have been presented in the literature [1]- [17]. Many are based on different nonlinear control strategies that can generally be categorized into the conventional control law [1]- [3], perturb-and-observe [4], hill-climbing [5] and hybrid techniques [6]- [17] in which, for example, the step size in a hill-climbing method is updated using fuzzy logic or neural network algorithms. In the conventional technique, the electrical torque is proportional to the square of the rotor speed while the controller gain needs to adaptively be adjusted in real-time.…”

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confidence: 99%

See 3 more Smart Citations

A Maximum Power Tracking Technique for Grid-Connected DFIG-Based Wind Turbines

Fateh

White

Gruenbacher

2015

IEEE J. Emerg. Sel. Topics Power Electron.

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In this paper, a maximum power tracking technique is presented for doubly fed induction generator (DFIG)-based wind turbines. The presented technique is a novel version of the conventional method, i.e. the electrical torque is proportional to the square of the rotor speed, in which the proportional-coefficient is adaptively adjusted in real-time through three control laws. The first control law calculates the desired electrical torque using feedback linearization, assuming that the power capture coefficient and the desired rotor speed are instantaneously identified. The second control law estimates realtime values of the power capture coefficient from a Lyapunovbased analysis, and the third control law provides the desired rotor speed. These control laws cause the turbine to adaptively adjust the rotor speed towards a desired speed in which the operating point moves in the direction of increasing the power capture coefficient. The proposed maximum power tracking method differs distinctly from the perturb-and-observe scheme by eliminating a need for adding a dither or perturbation signal, and robustly tracks the trajectory of maximum power points even in the event of a sudden wind speed change that can cause the perturb-and-observe technique to fail. In this paper, the NREL 5 MW reference wind turbine model is used to demonstrate the validity and robustness of the proposed method.

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Section: Review Of Maximum Power Seeking Techniquesmentioning

confidence: 99%

Section: Review Of Maximum Power Seeking Techniquesmentioning

confidence: 99%

Section: A Feedback Linearization For the Torque Controlmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A Maximum Power Tracking Technique for Grid-Connected DFIG-Based Wind Turbines

Fateh

White

Gruenbacher

2015

IEEE J. Emerg. Sel. Topics Power Electron.

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“…The simulation results are compared by using HCS, extremum seeking control (ESC) [21][22][23] and sliding mode ESC (SMESC) [24,25]. The dynamic response of the system is analyzed by the output power of the three algorithms.…”

Section: Introductionmentioning

confidence: 99%

Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems

2014

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This paper proposes a sliding mode extremum seeking control (SMESC) of chaos embedded particle swarm optimization (CEPSO) Algorithm, applied to the design of maximum power point tracking in wind power systems. Its features are that the control parameters in SMESC are optimized by CEPSO, making it unnecessary to change the output power of different wind turbines, the designed in-repetition rate is reduced, and the system control efficiency is increased. The wind power system control is designed by simulation, in comparison with the traditional wind power control method, and the simulated dynamic response obtained by the SMESC algorithm proposed in this paper is better than the traditional hill-climbing search (HCS) and extremum seeking control (ESC) algorithms in the transient or steady states, validating the advantages and practicability of the method proposed in this paper.

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Study of robust adaptive step‐sizes P&O MPPT algorithm for high‐inertia WT with direct‐driven multiphase PMSG

Mousa

Youssef

Mohamed

2019

Int Trans Electr Energ Syst

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Summary In this study, an efficient robust adaptive perturb and observe (RA‐PO) maximum power point tracking (MPPT) algorithm–based wind energy conversion system (WECS) is suggested to avoid drawbacks of conventional P&O (CPO) algorithms. It depends on observing the distance between the actual and optimal rotor speed, calculating the required adaptive ratio at each operating point, perturbing the rotor speed with an appropriate step‐size, and observing power variations until the P‐ω curve slope equals zero. Therefore, it exhibits a rapid speed tracking with low oscillations. Moreover, it offers a well‐defined relationship among the extracted mechanical power, the rotor speed, the perturbing step‐size, and the operating wind speed so wrong direction and loss of tracking problems can be avoided on high‐inertia wind turbine (WT) during rapid wind speed fluctuations. It improves the WECS efficiency from 87% to 91%. The performance of WECS is confirmed via MATLAB/SIMULINK.

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Power optimization and control in wind energy conversion systems using extremum seeking

Cited by 39 publications

References 19 publications

A Maximum Power Tracking Technique for Grid-Connected DFIG-Based Wind Turbines

A Maximum Power Tracking Technique for Grid-Connected DFIG-Based Wind Turbines

Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems

Study of robust adaptive step‐sizes P&O MPPT algorithm for high‐inertia WT with direct‐driven multiphase PMSG

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