Dynamic Modeling and Vector control of DFIG-based Wind Turbine

Zgarni, Ines; Amraoui, Lilia El

doi:10.1109/ic_aset53395.2022.9765917

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…He concluded that the choice of specific regulation method relies on the request. VC is used by reference [44] to control DFIG to decouple active and reactive power. Yuan in Ref.…”

Section: Figure 1 Lvrt Code Requirementmentioning

confidence: 99%

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double-fed induction generator using hybrid model contains an adaptive neurofuzzy inference system controller and a crowbar circuit.

2024

Preprint

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The focal target of this paper is to increase the LVRT of the DFIG driven by WT. This task is realized, firstly, by inserting a crowbar circuit (CB) into rotor circuit (RC), in case of voltage dip (VD), to guard it from high current and high voltage that could result from the transient flux produced by the VD. In case of no CB circuit, the system may be vulnerable to instability. Secondly, a proposed ANFIS controller is proposed to control both the rotor side converter (RSC) and the grid side converter (GSC). While the RSC controller regulates the electromagnetic torque, the GSC controller control both the real power (RP) and reactive power (QP) supplied to the system during normal operation conditions beside with it keeps the value of the dc link voltage (DCLV) fixed. To enhance the LVRT, the GSC is controlled such that it injects the necessary reactive power required during severe voltage dips to improve the LVRT competency of the DFIG. From the results, it is obvious that the hybrid ANFIS and crowbar technique provides an improved LVRT response in challenging operating conditions.

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“…He concluded that the choice of specific regulation method relies on the request. VC is used by reference [44] to control DFIG to decouple active and reactive power. Yuan in Ref.…”

Section: Figure 1 Lvrt Code Requirementmentioning

confidence: 99%

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double-fed induction generator using hybrid model contains an adaptive neurofuzzy inference system controller and a crowbar circuit.

2024

Preprint

View full text Add to dashboard Cite

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Parameter identification of DFIG model based on HHT marginal spectrum

He,

Zhu,

et al. 2023

2023 3rd New Energy and Energy Storage System Control Summit Forum (NEESSC)

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Observer based finite-time adaptive fractional-order sliding mode control of DFIG wind turbines with unknown dynamic parameters

Moghadam,

Alibeiki,

Khosravi

2024

Transactions of the Institute of Measurement and Control

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In this study, we investigate adaptive finite-time fractional-order sliding mode control (AFFSMC) of DFIG wind turbines (DWTs) under model uncertainty in finite time. A fractional-order sliding surface is first constructed to develop the concept. A robust terminal adaptive sliding mode controller is then designed for pitch angle control of wind turbine to achieve finite-time stability at high wind speeds to regulate the produced electric power in Zone III of the turbine’s working range. This is all with this assumption that there is no any information about the dynamics of the turbine due to the significant uncertainties. Through the use of a stable adaptive law, the vector of the wind turbine’s unknown dynamic parameters is approximated in this method. An observer is also proposed to estimate both turbine speed and external disturbances term. The closed-loop system’s finite-time stability study and the finite-time convergence of the turbine output were both carried out using the dynamic model of the system and the proposed Lyapunov theory. Some numerical examples are also presented together with a comparison to the standard integer sliding mode control to demonstrate the correctness of the control law.

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Dynamic Modeling and Vector control of DFIG-based Wind Turbine

Cited by 3 publications

References 12 publications

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double-fed induction generator using hybrid model contains an adaptive neurofuzzy inference system controller and a crowbar circuit.

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double-fed induction generator using hybrid model contains an adaptive neurofuzzy inference system controller and a crowbar circuit.

Parameter identification of DFIG model based on HHT marginal spectrum

Observer based finite-time adaptive fractional-order sliding mode control of DFIG wind turbines with unknown dynamic parameters

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