GPRS/EGPRS standards applied to DTC of a DFIG using fuzzy – PI controllers

Rocha-Osorio, C. M.; Chaves, Juan Sebastián Solís; Casella, Ivan R. S.; Capovilla, C. E.; Azcue, Jose L.; Filho, Alfeu J. Sguarezi

doi:10.1016/j.ijepes.2017.05.033

Cited by 28 publications

(12 citation statements)

References 16 publications

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“…Due to these advantages, the SM-PMSM has been applied in several EV applications [123,124]. Some PMSM control methods are discussed in the scientific literature, such as DTC [125][126][127], predictive control [128], and adaptive structures [129,130]. However, FOC is the most popular control strategy used for PMSM drives [131][132][133][134][135].…”

Section: Permanent Magnet Synchronous Motor (Pmsm)mentioning

confidence: 99%

Technology Development of Electric Vehicles: A Review

et al. 2019

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To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

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Section: Permanent Magnet Synchronous Motor (Pmsm)mentioning

confidence: 99%

Technology Development of Electric Vehicles: A Review

et al. 2019

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“…For the referential rotor flux, it is chosen to achieve the required stator power factor. It can be calculated by considering the DFIG representation in the d‐q frame . Therefore, it is given in the α , β frame using inverse Park transformation according to Equation :

φ_{ref} = \sqrt{(()||, (\frac{σ {normalL}_{r}}{M} + \frac{M}{L_{s}}), φ_{s}, - \frac{{σ normalL}_{r} L_{s}}{M V_{s}} {Q_{s}}_{ref}) 2 + ((), \frac{- {normalL}_{r} {normalL}_{s} σ {T_{italicem}}_{italicref}}{M ∣ φ_{s} ∣}) 2 .}

…”

Section: Classical Direct Torque Control Of the Dfigmentioning

confidence: 99%

“…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%

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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“…Artificial intelligence methods have been combined with sliding mode control to control non-linear systems with uncertainties and at least to eliminate the chattering phenomenon. Fuzzy logic control [14,17] is often used in complex systems to overcome the limitations of conventional mathematical tools.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator

2020

IJE

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This paper presents a hybrid scheme for the control of active and reactive powers using the direct vector control with stator flux orientation (SFO) of the DFIG. The hybrid scheme consists of Fuzzy logic, Reference Signal Tracking (F-RST) controllers. The proposed (F-RST) controller is compared with the classical Proportional-Integral (PI) and the Polynomial (RST) based on the pole placement theory. The various strategies are analyzed and compared in terms of tracking, robustness, and sensitivity to the speed variation. Simulations are done using MATLAB software. The simulation results prove that the proposed approach leads to good performances such as the tracking test, the rejection of disturbances and the robustness concerning the parameter variations. The hybrid controller is much more efficient compared to those of PI and RST controller, it also improves the performance of the powers and ensures some important strength despite the parameter variation of the DFIG.

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GPRS/EGPRS standards applied to DTC of a DFIG using fuzzy – PI controllers

Cited by 28 publications

References 16 publications

Technology Development of Electric Vehicles: A Review

Technology Development of Electric Vehicles: A Review

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

Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator

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