Comparison study between SVPWM and FSVPWM strategy in fuzzy second order sliding mode control of a DFIG-based wind turbine

Benbouhenni, Habib

doi:10.2478/cjece-2019-0009

Cited by 15 publications

(8 citation statements)

References 28 publications

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“…This proposed modulation use of maximum (MAX) and minimum (MIN) of three-phase voltages (Va, Vb, Vc). This modulation strategy is detailed in [24][25][26][27]. The graphical representation of the hysteresis comparators of the SVPWM technique is shown in Figure 3.…”

Section: Three-level Svpwm Techniquementioning

confidence: 99%

Utilization of an ANFIS-STSM Algorithm to Minimize Total Harmonic Distortion

Benbouhenni

2020

ijSmartGrid

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Recently applications of second order super-twisting sliding mode (STSM) algorithm become more convenient in control of AC machines. The conventional technique, the capability of creating a high value of current or voltage. On the other hand, the STSMC algorithm popular in more applications. There are several types of intelligent techniques such as neural networks (NNs), adaptive network-based fuzzy inference system (ANFIS) and fuzzy logic controller (FLC). The ANFIS controller is very popular than other controllers because its control is very simple in comparison with others. The new technique of STSMC is utilized to reduce the harmonic distortion in direct torque control (DTC). Cat ANFIS-STSM algorithm is introduced as a new STSM algorithm to minimize harmonic distortion and electromagnetic torque ripples. The simulation results have been carried out using Matlab/Simulink software.

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Section: Three-level Svpwm Techniquementioning

confidence: 99%

Utilization of an ANFIS-STSM Algorithm to Minimize Total Harmonic Distortion

Benbouhenni

2020

ijSmartGrid

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“…15 Additionally, require prior training needs to be established neural network approaches. 16 Imposing SOSMC and HOSMC methods provide a higher speed of the control system 17 but these techniques are involved with complex and high computational steps in controller designing. Consequently, they are usually less recommended.…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimized fractional order control of doubly‐fed induction generator (DFIG) based wind turbine using disturbance observer

Veisi

Delavari

2023

Env Prog and Sustain Energy

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Wind energy systems are pollution free and clean form of the renewable energy production. The dynamic model of a wind turbine system based on a doubly fed induction generator (DFIG) is exposed to external disturbances, uncertainties, and nonlinear dynamics. In this paper to ensure the system robustness against external disturbance and uncertainty in system parameters, a novel optimized fractional order robust adaptive sliding mode controller is proposed by utilizing a disturbance observer. The controller's main goal is to track the maximum power point of the wind turbine. In order to show the superiority of the proposed method, the results under normal conditions and in the presence of disturbance and uncertainty have been compared with the classical sliding mode control (SMC) and adaptive sliding mode control (ASMC). The parameters of all three controllers have been optimized by ant colony optimization (ACO) algorithm. The proposed method does not need the knowledge of the upper bounds of model uncertainty and disturbance. Also by using the fractional order operators in the control signal of the proposed method, its robustness against model uncertainty and disturbance is increased and it can extract the maximum power than the other compared methods.

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“…DPC method was proposed [35] based on three-level SVPWM strategy, where the designed technique minimized the THD value of current and power ripple compared to conventional DPC technique of DFIGURE DTC strategy and fuzzy STSM algorithm was combined to regulate the active/reactive power and torque of DFIG [36]. Fuzzy SVPWM technique reduce the THD value of current compared to conventional SVPWM strategy of DFIG controlled by fuzzy SOSMC method [37]. Neural SOSMC technique reduce the torque ripple compared to neural SMC method of DFIG [38].…”

Section: Introductionmentioning

confidence: 99%

Robust Direct Power Control of a DFIG Fed by a Five- Level NPC Inverter Using Neural SVPWM Technique

Benbouhenni¹

2021

TI-IJES

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The work presents a new direct power command (DPC) strategy based on a second order sliding mode controller (SOSMC) of a doubly fed induction generator (DFIG) integrated in a wind energy conversion system (WECS). In the first step we propose to use a five-level inverter based on the neural space vector pulse width modulation (NSVPWM) to supply the DFIG rotor side. This is the harmonic distortion (THD) of the DFIG rotor voltage and then performs provides the power to the grid by the stator side. The traditional DPC with space vector pulse width modulation (DPC-SVPWM) using proportional-integral (PI) controllers has considerable reactive and active power oscillations at a steady-state operation. In order to ensure a robust DFIG DPC-SVPWM technique and minimize the reactive and active power ripples, a SOSMC algorithms is used in the second step. The Simulation results show the efficiency of the designed control scheme especially in terms of the quality of the provided power compared to DPC-SVPWM.

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Comparison study between SVPWM and FSVPWM strategy in fuzzy second order sliding mode control of a DFIG-based wind turbine

Cited by 15 publications

References 28 publications

Utilization of an ANFIS-STSM Algorithm to Minimize Total Harmonic Distortion

Utilization of an ANFIS-STSM Algorithm to Minimize Total Harmonic Distortion

Adaptive optimized fractional order control of doubly‐fed induction generator (DFIG) based wind turbine using disturbance observer

Robust Direct Power Control of a DFIG Fed by a Five- Level NPC Inverter Using Neural SVPWM Technique

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