Feedback linearization control based particle swarm optimization for maximum power point tracking of wind turbine equipped by PMSG connected to the grid

Soufi, Youcef; Kahla, Sami; Bechouat, Mohcene

doi:10.1016/j.ijhydene.2016.06.010

Cited by 58 publications

(46 citation statements)

References 10 publications

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“…Modeling imprecisions and disturbances lead to a significant increase in tracking error when feedback linearization is applied without a compensation scheme [41]. Hence, neural networks [42][43][44][45][46][47][48], fuzzy logic [49][50][51][52], and evolutionary computation [53][54][55], for instance, have been used to surpass this limitation and to improve control performance.…”

Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

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Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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“…The pitch angle of the fixed pitch wind turbine ϕ is zero. According to [18], the C p expression of fixed pitch wind turbine may be obtained as follows:…”

Section: Equation Of the Wind Turbinementioning

confidence: 99%

Experiments and Simulation on a Late-Model Wind-Motor Hybrid Pumping Unit

Zhang

Wang

2020

Energies

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Many oil fields are full of wind energy. At present, wind power generation technology has catered to oil fields. A larger wind turbine is used to supply power to several pumping units. As a result of the structural characteristics of the pumping unit, the efficiency of the electromotor is very low, which leads to a reduction in the utilization rate of wind energy. At the same time, considering the high cost of large wind turbines, the energy saving effect is not obvious in practical applications. This paper proposes an energy supply model of a pumping unit driven by a small wind turbine and a new wind-motor hybrid structure. Instead of wind power generation technology, wind energy drives the pumping unit directly via a mechanical–hydraulic transmission system. This new mechanical-hydraulic system can optimize the power confluence of wind and electric power. To enhance the efficiency of the motor, a mathematical model and a test station were established. The correctness of the energy conservation method and the mathematical model was verified, and the performance of the wind-motor system was studied.

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“…The system satisfies the Lyapunov stability condition described in equation (27). The stability of the system is illustrated in Figure 6.…”

Section: Lyapunov Stabilitymentioning

confidence: 99%

Proposed particle swarm optimization technique for the wind turbine control system

Iqbal

Ying

Saleem

et al. 2020

Measurement and Control

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Wind energy is a useful and reliable energy source. Wind turbines are attracting attention with the dependency of the world on clean energy. The turbulent nature of wind profiles along with uncertainty in the modeling of wind turbines makes them more challenging for prolific power extraction. The pitch control angle is used for the effective operation of wind turbines at the above-nominal wind speed. To extract stable power as well as to keep wind turbines in a safe operating region, the pitch controller should be intelligent and highly efficient. For this purpose, proportional-integral-derivative controllers are mostly used. The parameters for the proportional-integral-derivative controller are unknown and calculated by numerous techniques, which is a quite cumbersome task. In this research, the particle swarm optimization technique is used but the conventional particle swarm optimization technique cannot tackle the system's nonlinearity and uncertainties. Hence, the proposed particle swarm optimization algorithm is employed for the calculation of the controller's optimal parameters. The proposed technique is implemented on a 5-MW wind turbine, which is designed using the Bladed software. Simulation is performed using MATLAB/Simulink to validate the effectiveness of the proposed technique. A variable wind profile is fed as input into the system and the proposed controller provides satisfactory results for the power, rotor speed, and torque. The system is stable and the settling time is reduced.

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Feedback linearization control based particle swarm optimization for maximum power point tracking of wind turbine equipped by PMSG connected to the grid

Cited by 58 publications

References 10 publications

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Experiments and Simulation on a Late-Model Wind-Motor Hybrid Pumping Unit

Proposed particle swarm optimization technique for the wind turbine control system

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