Modeling, nonlinear dynamic analysis and control of fractional PMSG of wind turbine

Si, Gangquan; Zhu, Jianwei; Diao, Lijie; Ding, Zhiqiang

doi:10.1007/s11071-016-3289-9

Cited by 24 publications

(7 citation statements)

References 39 publications

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“…with nominal values C x � 1 nF and s � 2π(15.91 kHz)j, and with F(s) given by (13). As illustrated, the tendency is rising towards the 1% of F(s) variation as C x changes 1% of its nominal value along with the α increment.…”

Section: Sensitivity Analysismentioning

confidence: 89%

“…Fractional-order derivatives are characterized by their memory properties providing an excellent approach for describing the physical phenomena elegantly with improved accuracy [1,2]. As a result, the researchers have proposed novel mathematical models with applications in various fields, such as biology, economics, chaos theory, botany, hidden dynamics, digital circuits, cryptography, control, image processing, wind turbines, viscoelastic studies, and ferroelectric materials [3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Active Realization of Fractional‐Order Integrators and Their Application in Multiscroll Chaotic Systems

et al. 2021

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This paper presents the design, simulation, and experimental verification of the fractional-order multiscroll Lü chaotic system. We base them on op-amp-based approximations of fractional-order integrators and saturated series of nonlinear functions. The integrators are first-order active realizations tuned to reduce the inaccuracy of the frequency response. By an exponential curve fitting, we got a convenient design equation for realizing fractional-order integrators of orders from 0.1 to 0.95. The results include simulations in SPICE of the mathematical description and the electronic implementation and experimental measurements that confirm them. Monte Carlo and sensitivity tests revealed a robust realization. Contrary to its passive counterparts, the suggested realizations significantly reduce design and implementation efforts by favoring resistors and capacitors with commercial values and reducing hardware requirements.

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Section: Sensitivity Analysismentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Active Realization of Fractional‐Order Integrators and Their Application in Multiscroll Chaotic Systems

et al. 2021

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“…The PMSG model developed in this work is based on some simplifying assumptions. They are the most adopted to simplify the mathematical model (Arroyo, 2011;Fan, 2012;Si et al, 2017). First, the magnetic circuit is not saturated.…”

Section: Pmsg Modelmentioning

confidence: 99%

Artificial neural network–based sensorless control of wind energy conversion system driving a permanent magnet synchronous generator

et al. 2020

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Due to the wind characteristic, wind speed measure requires more than one sensor. However, to track the maximum power point of the wind, knowing the wind speed or mechanical speed is necessary. So, the solution is to use a sensorless control. This article is mainly focused on a sensorless control of a wind energy conversion system that employs an artificial neural network observer. The detailed mathematical model of the studied system is presented. It includes a permanent magnet synchronous generator. The contribution of the studied wind energy conversion system is to integrate a three-cell DC–DC converter. For the generation of maximum power from the wind, an algorithm to track the maximum power is developed. Then, to avoid the disadvantages of using sensors, an artificial neural network observer is implemented. The capabilities and contributions of the proposed control scheme are demonstrated by simulation results using MATLAB/Simulink.

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“…For a class of uncertain fractional-order nonlinear systems, an adaptive fuzzy backstepping control scheme is developed in [8]. In [9], the difference between integer-order system and fractional-order system is revealed, it is found that the dynamics of the system may be influenced by the systemic order in many aspects, such as bifurcation behavior, chaos pattern, and shape of strange attractor. Therefore, it is meaningful to investigate the fractional-order systems.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Command Filtered Adaptive Neural Network Tracking Control for Fractional-Order Chaotic PMSM

Wang

2019

IEEE Access

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In this paper, an observer-based command filtered adaptive neural network tracking control problem is addressed for a fractional-order chaotic permanent magnet synchronous motor (PMSM) with the immeasurable state, parameter uncertainties, and external load disturbance. First, the Chebyshev neural networks are introduced to approximate the nonlinear and unknown functions. Next, a neural network reducedorder state observer is designed to obtain the unmeasured state. Then, the command filtering approach based on the first-order Levant differentiator is developed to solve the ''explosion of complexity'' issue of backstepping, and a novel fractional-order error compensation mechanism is employed, which can remove the filtering errors in finite time. After that, the continuous frequency distributed model is investigated to design proper Lyapunov function, and it is demonstrated that the proposed control method not only ensures that all signals in the fractional-order PMSM system are bounded but also suppresses chaotic oscillation. Finally, the simulation studies are provided to verify the correctness and effectiveness of the proposed scheme.

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Modeling, nonlinear dynamic analysis and control of fractional PMSG of wind turbine

Cited by 24 publications

References 39 publications

Active Realization of Fractional‐Order Integrators and Their Application in Multiscroll Chaotic Systems

Active Realization of Fractional‐Order Integrators and Their Application in Multiscroll Chaotic Systems

Artificial neural network–based sensorless control of wind energy conversion system driving a permanent magnet synchronous generator

Observer-Based Command Filtered Adaptive Neural Network Tracking Control for Fractional-Order Chaotic PMSM

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