New intelligent direct power control of DFIG-based wind conversion system by using machine learning under variations of all operating and compensation modes

Sahri, Younes; Tamalouzt, Salah; Hamoudi, Farid; Belaid, Sofia Lalouni; Bajaj, Mohit; Alharthi, Mosleh M.; Alzaidi, Mohammed S.; Ghoneim, Sherif S. M.

doi:10.1016/j.egyr.2021.09.075

Cited by 40 publications

(30 citation statements)

References 30 publications

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“…The model of the studied DFIG connected to the electrical grid is given in Figure 1. By referencing to the dq Park reference, the mathematical modeling of the voltages, and active/reactive powers of the DFIG is [8]:…”

Section: Dfig Modelingmentioning

confidence: 99%

“…Tamalouzt et al [7], a method based on the three-level inverter topology and using a direct reactive power control is proposed for a (DFIG)-based wind power plant system. Sahri et al [8] introduce a direct power control strategy based on machine learning to overcome the difficulties caused by the DFIG and local reactive power variations from the grid.…”

Section: Introductionmentioning

confidence: 99%

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Wind farm connected to a grid using fuzzy logic controller based the unified power flow controller

Tarik

Brouri

2022

IJPEDS

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<span lang="EN-GB">The doubly-fed induction generator (DFIG) based wind farm is widely used for extracting energy from the wind. When the wind farm is connected to the grid, generally it causes some perturbations. It can disturb the function of the grid and the installations connected to the grid. This impact concerns the load flow, voltage drops, and frequency fluctuations. To overcome those issues, the unified power flow controller (UPFC) is recommended. In this paper, we propose to study the impact of the integration of a wind farm on the power system. Presently, we propose an original contribution by using UPFC based on fuzzy logic controllers (FLC). Finally, simulation and experimental results using MATLAB/Simulink are proposed to show the effectiveness of this solution. </span>

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Section: Dfig Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind farm connected to a grid using fuzzy logic controller based the unified power flow controller

Tarik

Brouri

2022

IJPEDS

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“…In [32], the authors have used particle swarm optimization (PSO) to automatically tune the PI controller for speed regulation of a PMSM. Artificial intelligence techniques are extensively used in the field of hybrid electric vehicles [33]. In [34], the authors have developed an effective energy management strategy under different driving cycles, and they have found that the optimized fuzzy logic strategy is better in terms of energy-saving compared to other techniques used.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles

et al. 2022

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In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods.

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“…This induction machine is connected to the AC grid by its stator and via reduced size static power converters in its rotor, where it is able to be driven by variable speed. It allows to offer a decoupled active and reactive power control over a large range [3]. However, the direct grid connection of the stator makes this kind of conversion system more sensible to grid disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…Linear and nonlinear control methods have been largely investigated in the literature and industry to address the aforementioned issues. Field-oriented control (FOC), conventional direct torque, and power controls (C-DTC/DPC) [3][4][5][6] are the basics of the DFIG control. The principal of C-DTC is the direct control of torque and flux by selecting voltage vector without using PWM block modulation nor PI regulators nor a coordinate transformer [6].…”

Section: Introductionmentioning

confidence: 99%

Advanced Fuzzy 12 DTC Control of Doubly Fed Induction Generator for Optimal Power Extraction in Wind Turbine System under Random Wind Conditions

et al. 2021

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A wind turbine (WT)-based doubly fed induction generator (DFIG) is the most often used generator in the wind conversion system market due to its advantages such as the ability of operating under variable wind speed and its high performance. However, nonlinear dynamical and parameter uncertainties of the DFIG make the controller design of this kind of system a challenging work. Thus, in this study, a novel control strategy was proposed to design the desired system dynamics, to highlight the efficacy of the proposed system, and to improve the performance of the closed-loop system. The proposed controller combines the twelve-sector direct torque control (12-DTC) and the fuzzy controller with modified rules to solve the limitations and shortcomings of the usual methods for the WT-DFIG system. All operation modes, successively and continually, were considered to reflect the true operation of WT-DFIG system subject to random wind speeds. The aims of this work was to ensure an optimal operation of the wind generator, extracting maximum power in the zone II of the WT characteristic, and limiting this power in its maximum value in the case (zone III), to transmit the power generated by the DFIG to the grid-side with minimum losses in the disturbances related to DFIG. Extensive numerical simulations were performed under MATALB/Simulink, where the proposed fuzzy twelve direct torque control (F12-DTC) was compared with conventional nonlinear controls: conventional DTC (C-DTC) and 12-DTC. The simulation results demonstrated clearly that the proposed one had the highest performance and robustness, with a significant reduction in rotor flux and electromagnetic torque ripples and better-generated power quality with low currents’ THD over the conventional strategies (C-DTC and 12-DTC).

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New intelligent direct power control of DFIG-based wind conversion system by using machine learning under variations of all operating and compensation modes

Cited by 40 publications

References 30 publications

Wind farm connected to a grid using fuzzy logic controller based the unified power flow controller

Wind farm connected to a grid using fuzzy logic controller based the unified power flow controller

Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles

Advanced Fuzzy 12 DTC Control of Doubly Fed Induction Generator for Optimal Power Extraction in Wind Turbine System under Random Wind Conditions

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