Optimization of DFIG Wind Turbine Power Quality Through Adaptive Fuzzy Control

Chojaa, Hamid; Derouich, Aziz; Taoussi, Mohammed; Zamzoum, Othmane; Yessef, Mourad

doi:10.1007/978-3-030-73882-2_113

Cited by 13 publications

(14 citation statements)

References 10 publications

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“…The latter can be expressed by equation (11). The mechanical component depicts how torque and speed of the DFIG are related [13].…”

Section: Modeling Of the Wtssmentioning

confidence: 99%

“…To calculate the value of each of the active and reactive power, equations ( 16) and ( 17) can be used. Through these two equations, the value of the reactive and active power is related to the value of quadrature and direct stator voltage and rotor flux [13].…”

Section: Proposed Dpc Strategymentioning

confidence: 99%

“…In addition, the dynamic performance of DFIG-based WTS controlled by the FOC strategy strongly depends on the design of the PI parameters. The PI parameters of the FOC strategy are proposed based on a single operating point [13]. In other words, the PI controller can not adapt its parameters if the operating point is changed [14].…”

Section: Introductionmentioning

confidence: 99%

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A Novel DPC Approach for DFIG-Based Variable Speed Wind Power Systems Using DSpace

et al. 2023

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The integration of wind energy systems into the electric grid has become inevitable despite the many problems associated with this integration. Most of these problems are due to variations in wind speed. The problems are for example oscillations in the power generated, which implies the lack of guarantee of obtaining the maximum energy and the ripple in the electromechanical torque of the generator. This work aims at mitigating these problems for wind energy conversion system-driven doubly-fed induction generator (DFIG), which is the main wind turbine utilized for energy applications. This mitigation is performed through direct reactive and active powers control of the DFIG using an artificial neural network. A DSP (Digital Signal Processor-dSPACE DS1104) was used to experimentally test the proposed strategy. The dynamic performances of the controlled generator are analyzed by using the designed intelligent control strategy in the case of variable wind speeds and upon sudden change of the active power demand. Based on the obtained experimental results, it can be said that the designed intelligent control strategy outperforms traditional methods like direct power (DPC) and vector control in terms of reducing the current harmonics, and torque ripples, and enhancing dynamic response.

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“…The latter can be expressed by equation (11). The mechanical component depicts how torque and speed of the DFIG are related [13].…”

Section: Modeling Of the Wtssmentioning

confidence: 99%

Section: Proposed Dpc Strategymentioning

confidence: 99%

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A Novel DPC Approach for DFIG-Based Variable Speed Wind Power Systems Using DSpace

et al. 2023

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“…To capture the maximum of the wind energy, the rotational speed of the turbine must be continuously adjusted according to the wind speed variations [19]. The main goal of this command is to adjust continuously the turbine rotational speed at the value that guarantees an optimal speed ratio (λopt).…”

Section: Modeling Of the Wind Turbine And Mppt Control Strategymentioning

confidence: 99%

Performance improvement of the variable speed wind turbine driving a DFIG using nonlinear control strategies

Chojaa¹,

Derouich²,

Hallabi³

et al. 2021

IJPEDS

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In this research paper, a nonlinear Backstepping controller has been proposed in order to improve the dynamic performance of a doubly fed induction generator (DFIG) based Wind Energy conversion System, connected to the grid through a back-to-back converter. Firstly, an overall modeling of proposed system has been presented. Thereafter, three control techniques namely backstepping (BSC), sliding mode (SMC) and field-oriented control (FOC) using a conventional PI regulator have been designed in order to control the stator active and reactive powers of the DFIG. In addition, the maximum power point tracking (MPPT) strategy has been investigated in this work with three mechanical speed controllers: BSC, SMC and PI controller with the aim of making a synthesis and a comparison between their performances to determine which of those three techniques is more efficient to extract the maximum power. Finally, a thorough comparison between the adopted techniques for the DFIG control has been established in terms of response time, rise time, total harmonic distortion THD (%) of the stator current, static errors and robustness. The effectiveness and robustness of each control approach has been implemented and tested under MATLAB/Simulink environment by using a 1.5 MW wind system model.

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“…There are two types of wind energy generators (WEGs): fixed speed WEGs and variable speed WEGs (Chen et al, 2017; Chojaa et al, 2021). However, the second type offers a wide range operation in terms of wind speed, improved overall efficiency, and higher power-capturing capabilities.…”

Section: Introductionmentioning

confidence: 99%

Overview of control strategies for wind turbines: ANNC, FLC, SMC, BSC, and PI controllers

Yessef¹,

Bossoufi²,

Taoussi³

et al. 2022

Wind Engineering

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The design of robust and precise control of obtaining maximum power yield is an important area of research in wind engineering. In the context of maximizing the amount of power extraction in wind energy conversion systems (WECS), this research work proposes and evaluates five MPPT algorithms. These types are respectively a proportional integral controller (PI), a non-linear control based on sliding modes (SMC), a backstepping approach (BSC), a control using artificial intelligence based on neural network (ANNC), and a fuzzy logic control (FLC). Two different wind profiles, a step wind profile and a real wind profile, were considered for the comparative study. The response time, dynamic error percentage, and static error percentage were the quantitative parameters compared, and the qualitative parameters included set-point tracking and precision. This test demonstrated the superiority of the ANNC controller with an error static that not exceed 0.39% and a response time ~0.0024 seconds.

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Optimization of DFIG Wind Turbine Power Quality Through Adaptive Fuzzy Control

Cited by 13 publications

References 10 publications

A Novel DPC Approach for DFIG-Based Variable Speed Wind Power Systems Using DSpace

A Novel DPC Approach for DFIG-Based Variable Speed Wind Power Systems Using DSpace

Performance improvement of the variable speed wind turbine driving a DFIG using nonlinear control strategies

Overview of control strategies for wind turbines: ANNC, FLC, SMC, BSC, and PI controllers

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