Enhancement of the LVRT Capability for DFIG-Based Wind Farms Based on Short-Circuit Capacity

Rafiee, Zahra; Heydari, Rasool; Rafiee, Mansour; Aghamohammadi, Mohammad Reza; Blaabjerg, Frede

doi:10.1109/jsyst.2022.3153887

Cited by 22 publications

(9 citation statements)

References 47 publications

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“…Rafiee in Ref. [53] concentrated on the transient voltage regulation to enhance LVRT. Herzog 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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“…Rafiee in Ref. [53] concentrated on the transient voltage regulation to enhance LVRT. Herzog 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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“…The RSC and GSC are connected by the DC-link, which acts as a temporary evergy storage. The nonlinear dynamics of a DC-link capacitor voltage can be described by (10) as follows.…”

Section: G Dc-link Modelmentioning

confidence: 99%

“…dV DC dt = P r − P g (10) where C VDC and V DC refer to the DC-link capacitor and its corresponding voltage, respectively. P g = 1 2 (v qg i qg + v dg i dg ) and P g = 1 2 (v qg i qg + v dg i dg ) indicate the power flowing from the rotor and into the GSC filter, respectively.…”

Section: Vdc V Dcmentioning

confidence: 99%

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Dynamic State Estimation of a High-Order Model of Doubly-Fed Induction Generator Using Unscented Kalman Filter

Ramadhan,

Ali,

Irnawan

2024

IEEE Access

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The utilization of renewable energy in power generation has been increasing in recent years, with the use of wind power sources being the most promising solution for sustainable power generation. The doubly-fed induction generator (DFIG) is one of the most commonly used generators in wind power generation applications, as it offers some technical of advantages. However, the increasing penetration of wind power generation poses tremendous technical challenges in power system operation as this can potentially affect system stability, requiring better control and monitoring schemes. Dynamic state estimation (DSE) offers the ability to achieve this purpose. With respect to this, the present paper proposes a DSE framework on a high-order model of DFIG consisting of 18 states. The method uses the unscented Kalman filter (UKF) which provides an accurate estimate of DFIG states under a strong system non linearity present in the wind turbine system. Furthermore, this paper demonstrates the robustness of the proposed method under different faults and noisy conditions. Finally, the paper also extends the use of UKF to estimate the unknown inputs of a DFIG system, such as control references in the rotor-side converter (RSC) and grid-side converter(GSC). INDEX TERMSDoubly fed induction generator, Dynamic state Estimation, Power system monitoring, Unscented kalman filter, Wind power generation I. INTRODUCTION

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“…Acceleration occurs as a result of mechanical power from the turbine and lack or absence of electrical power drawn from the generator [17,18].…”

Section: Introductionmentioning

confidence: 99%

Blades Pitch Angle Control and Crowbar Protection of a Wind Turbine using Adaptive Neuro-Fuzzy Inference System at Severe Faulty Conditions

Samir

Bahgat²,

Abdelghany³

et al. 2022

International Journal of Advanced Engineering and Business Scie

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Due to their superior efficiency, stability, and ability to produce maximum power at various typical operating situations, wind turbines driving doubly fed induction generator systems are widely utilized in the extraction of wind energy. These systems have stability issues, particularly at severe faulty conditions. This paper focuses on the fault ride-through of a doubly fed induction generator (DFIG) driven by a wind turbine using Adaptive Neuro-Fuzzy Inference System (ANFIS). The measured voltages and currents at the generator's terminals are used by the

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Enhancement of the LVRT Capability for DFIG-Based Wind Farms Based on Short-Circuit Capacity

Cited by 22 publications

References 47 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.

Dynamic State Estimation of a High-Order Model of Doubly-Fed Induction Generator Using Unscented Kalman Filter

Blades Pitch Angle Control and Crowbar Protection of a Wind Turbine using Adaptive Neuro-Fuzzy Inference System at Severe Faulty Conditions

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