Improvement of Sliding Mode Control Strategy Founded on Cascaded Doubly Fed Induction Generator Powered by a Matrix Converter

Maafa, A.; Mellah, Hacène; Ghedamsi, Kaci; Aouzellag, Djamal

doi:10.2139/ssrn.4186954

Cited by 2 publications

(7 citation statements)

References 29 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total electromagnetic torque (T em ) of the cascade is determined by the addition of the torques generated by the two individual generators (25):…”

Section: Model Of the Cdfigmentioning

confidence: 99%

“…It involves coupling two wound rotor induction generators at their rotor windings, both mounted on a shared shaft (3,5,6). Compared to traditional wind power systems, the CDFIG system offers several advantages, including enhanced reliability and reduced maintenance costs (7). One key benefit of employing CDFIG system is its ability to operate at variable speeds (4,6).…”

Section: Introductionmentioning

confidence: 99%

“…The literature encompasses various control techniques employed in wind power conversion systems (WPCS) utilizing the cascaded doubly fed induction generator (CDFIG). Notable examples include direct torque and flux control (12), sliding mode control (SMC) as described by Maafa et al (7) and Zahedi et al (13), This control technique frequently encounters undesired chattering phenomena (14). However, this phenomena can be decreased by updating the traditional SMC into the terminal SMC by Abdolhadi et al (15) and the supertwisting SMC by Yan and Cheng (16) but it leads to more complexity of the controller (17).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Wind Power Conversion System Control Under Wind Constraints Using Single Hidden Layer Neural Network

Mazari,

Ait Abbas,

Laroussi

et al. 2024

IJE

View full text Add to dashboard Cite

In the realm of wind power generation, cascaded doubly fed induction generators (CDFIG) play a pivotal role. However, the classical proportional integral derivative (PID) controllers used within such systems often struggle with instability and inaccuracies arising from wind variability. This study proposes an enhancement to overcome these limitations by incorporating a single hidden layer neural network (SHLNN) into the wind power conversion systems (WPCS). The SHLNN aims to complement the PID controller by addressing its shortcomings in handling nonlinearities and uncertainties. This integration exploits the adaptive nature and low computational demand of SHLNNs, utilizing historical wind speed and power data to form a more resilient control strategy. Through Matlab/Simulink simulations, this approach is rigorously compared against traditional PID control methods. The results demonstrate a marked improvement in performance, highlighting the SHLNN's capacity to contend with the intrinsic variabilities of wind patterns. This contribution is significant as it offers a sophisticated yet computationally efficient solution to enhance CDFIG-based WPCS, ensuring more stable and accurate energy production.

show abstract

“…The total electromagnetic torque (T em ) of the cascade is determined by the addition of the torques generated by the two individual generators (25):…”

Section: Model Of the Cdfigmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Wind Power Conversion System Control Under Wind Constraints Using Single Hidden Layer Neural Network

Mazari,

Ait Abbas,

Laroussi

et al. 2024

IJE

View full text Add to dashboard Cite

show abstract

“…The rotors of the two DFIMs can be connected in two different ways. Connecting the identical phases produces a direct connection, while connecting the phases in the opposite order produces an inverse connection (Maafa, et al, 2022 ;Maafa, et al, 2016 ;Taka & Shibata, 1992). The second configuration is the focus of our study.…”

Section: Cdfig Modelingmentioning

confidence: 99%

“…(DFIM). (Maafa et al, 2022 ;Admowicz et al 2008 ;Maafa, et al 2016 ;Achkar et al 2017 ;Moazen et al, 2016 ;Zahedi Abdolhadi et al, 2021 ;Yan & Cheng, 2019). The increasing costs associated with maintaining conventional doubly-fed induction generator (DFIG) resulted in a need to explore alternate generator systems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Powers Exchanged between a Cascaded Doubly Fed Induction Generator and the Grid with a Matrix Converter

MAAFA,

ABDELGHANI,

HACENE

et al. 2023

EPSTEM

View full text Add to dashboard Cite

The use of an electromagnetic converter of great reliability in wind energy, like the cascaded doubly fed induction generator (CDFIG), can solve several problems compared to the doubly fed induction generator (DFIG), among them the elimination of the brushes and copper rings. The work consists of studying the performance of a CDFIG-based wind system using a matrix converter and a variable pitch control system. The traditional PI is able to successfully solve a number of linear control problems. The fuzzy logic controller used can dramatically improve the system's performance. The active and reactive energy transferred between the CDFIG and the grid can be controlled by adjusting the machine inverter. The DC bus voltage has been kept stable by controlling the grid inverter. The pitch controller's objective is to maximise aerodynamic power while staying within the converter's power limits. Numerical simulation results implemented in the MATLAB/Simulink package will be provided in this study to show that the suggested control strategy is both possible and effective.

show abstract

Improvement of Sliding Mode Control Strategy Founded on Cascaded Doubly Fed Induction Generator Powered by a Matrix Converter

Cited by 2 publications

References 29 publications

Enhancing Wind Power Conversion System Control Under Wind Constraints Using Single Hidden Layer Neural Network

Enhancing Wind Power Conversion System Control Under Wind Constraints Using Single Hidden Layer Neural Network

Optimization of the Powers Exchanged between a Cascaded Doubly Fed Induction Generator and the Grid with a Matrix Converter

Contact Info

Product

Resources

About