Optimizing the wind power capture by using DTC technique based on Artificial Neural Network for a DFIG variable speed wind turbine

Bakouri, Anass; Mahmoudi, Hassane; Abbou, Ahmed; Moutchou, Mohamed

doi:10.1109/sita.2015.7358425

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…The application of these techniques seeks to reduce the uncertainties faced by classical control methods based on linear models [22]. The artificial neural networks were implemented as an 3 of 14 adaptive control strategy allowed the suppression of noise and the disturbances produced by the turbulence caused by the wind flow [23]. A speed control strategy for PMSG with an artificial neural network was described, taking advantage of its learning capacity for the nonlinear dynamics of the system and ignoring the knowledge of the parameters of the machine [24].…”

Section: Figure 1 Configuration Of a Wecs Based On Pmsgmentioning

confidence: 99%

Multiple Linear Regression Approach for Sensorless MPPT of PMSG Wind Power Generation Systems

Abo‐Khalil

Sobhy

Sayed

2022

Preprint

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In this paper, a novel approach for extracting the maximum power from a wind energy conversion system (WECS) eqquiped with a permanent magnet synchronous generator (PMSG) is presented. Wind power generation systems have gained prominence as an alternative to conventional power plants worldwide, being the renewable energy source with the best growth prospects. However, the intermittent nature of wind power reduces energy reliability and hinders the development of technologies in this area. Therefore, a sensorless controller for a WECS with a PMSG is developed to enhance the conversion efficiency of the wind enegy system. The proposed control algorithm predicts the wind speed from the wind turbine power-speed characteristics. It uses the predicted speed to determine the optimum reference rotating speed to extract the maximum power point (MPPT) based on the optimum tip-speed ratio. The proposed control approach is based on a multiple linear regression (MLR) algorithms which is used to predict the wind speed value by calculating the regression coefficients from the predetermined training samples of the turbine power, rotating speed, and wind speed. The MPPT operating mode is maintained as long as the available power is less than the rated power of the converter. Otherwise, the MPPT algorithm is disabled, and a mechanical power control loop guarantees nominal power. A complete theoretical analysis and the experimental results considering the prototype with the nominal power of the 3 kW wind turbine used are presented. The experimental results has proven the effectiveness and robustness of the proposed control approach.

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Section: Figure 1 Configuration Of a Wecs Based On Pmsgmentioning

confidence: 99%

Multiple Linear Regression Approach for Sensorless MPPT of PMSG Wind Power Generation Systems

Abo‐Khalil

Sobhy

Sayed

2022

Preprint

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“…It can be calculated by considering the DFIG representation in the d-q frame. [34][35][36] Therefore, it is given in the α, β frame using inverse Park transformation according to Equation 15:…”

Section: Classical Direct Torque Control Of the Dfigmentioning

confidence: 99%

“…Recently, the artificial intelligent control techniques have been widely applied to DTC for induction motor drives and for DFIG. [15][16][17][18][19][20][21][22][23][24] In Zemmit et al, 16 the authors proposed an approach that consists of generating the optimal electromagnetic torque from a genetic algorithms tuned proportional integral (PI) speed controller. The results showed a significant reduction in the flux and the torque ripples.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed a significant reduction in the flux and the torque ripples. In Bakouri et al and Djeriri et al, the authors proposed the use of artificial neural networks to improve the DTC by replacing the switching table by a neural selection table that has the same inputs as the classical table: the flux error, the torque error, and the angle of sector location of the rotor flux. In Bakouri et al, the authors have not shown the effectiveness of the proposed strategy because they have only published the results concerning the proposed strategy and not the comparison with the classical strategy.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy 12 sectors improved direct torque control of a DFIG with stator power factor control strategy

Ayrir

Haddi

2019

Int Trans Electr Energ Syst

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Summary In this paper, an improved direct torque control (DTC) strategy based on a fuzzy inference system is proposed to control a doubly fed induction generator (DFIG)‐based wind energy conversion system (WECS). The proposed method is based on increasing the number of sectors to 12 instead of six in the classical DTC (C‐DTC), and the use of a fuzzy inference system to generate the optimal voltage vector to apply to the rotor side converter (RSC). The proposed method aims to reduce the flux and torque undulations which are considered the main disadvantage of the C‐DTC and to control the power factor at the stator terminals by generating the referential rotor flux from the referential reactive power. The effectiveness of the proposed strategy is well tested by the simulation. Moreover, a total harmonic distortion (THD) analysis was performed for the rotor and stator currents waveforms. The results show a considerable reduction of the undulations for both flux and torque, and a significant reduction of the THD.

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“…The schematic diagram Fig. 1 of a system composed of mechanical and electrical parts with a control which has proposed [4,7].…”

Section: Introductionmentioning

confidence: 99%

The DTC with ANN of a DFIG Driven by a WECS

Bouhafna¹,

Toumi²,

Benaggoune³

et al. 2020

IJISRT

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This paper treats the modeling of a wind energy conversion system connected to the grid, which is composed of a horizontal axis wind turbine operating at variable wind speeds, a doubly- fed induction generator and two converters ( AC / DC and DC / AC) controlled by direct torque control (DTC) with application of neural networks. The results of simulations of the studied system are presented in the Matlab / Simulink environment.

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Optimizing the wind power capture by using DTC technique based on Artificial Neural Network for a DFIG variable speed wind turbine

Cited by 14 publications

References 9 publications

Multiple Linear Regression Approach for Sensorless MPPT of PMSG Wind Power Generation Systems

Multiple Linear Regression Approach for Sensorless MPPT of PMSG Wind Power Generation Systems

Fuzzy 12 sectors improved direct torque control of a DFIG with stator power factor control strategy

The DTC with ANN of a DFIG Driven by a WECS

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