Comparative Analysis of Control Schemes for DFIG-Based Wind Energy System

Kumar, Brijesh; Sandhu, K. S.; Sharma, Rahul

doi:10.1007/s40031-021-00660-z

Cited by 11 publications

(7 citation statements)

References 123 publications

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“…Other control techniques used vector control (VC) to enhance LVRT, for example Kumar in [43] made a comparison between four methods used for controlling DFIG, namely VC, direct torque control, direct power control, and the derivative of these methods. He concluded that the choice of specific regulation method relies on the request.…”

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 ANFIS controller and a crowbar circuit

Abdelaal

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 CB circuit provides an improved LVRT response in challenging operating conditions.

show abstract

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 ANFIS controller and a crowbar circuit

Abdelaal

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…[41,42] used predictive control without any CB to improve LVRT. Other control techniques used vector control (VC) to enhance LVRT, for example Kumar in [43] made a comparison between four methods used for controlling DFIG, namely VC, direct torque control, direct power control, and the derivative of these methods. He concluded that the choice of specific regulation method relies on the request.…”

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

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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“…the generated currents and poor current quality, leading to various faults [20,[28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. This not only increases maintenance costs but also poses a risk to the grid.…”

Section: Plos Onementioning

confidence: 99%

“…As a result, adopting the DPC strategy becomes more costly and requires more experimentation. Another proposed solution includes the use of a backstepping controller to enhance the quality of the output current in DFIG system [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. However, the inclusion of a backstepping controller amplifies the complexity and execution challenges associated with Direct Power Control.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Dardabi,

Djebli,

Chojaa

et al. 2024

PLoS ONE

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This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

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Comparative Analysis of Control Schemes for DFIG-Based Wind Energy System

Cited by 11 publications

References 123 publications

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double- fed induction generator using ANFIS controller and a crowbar circuit

RETRACTED: Enhancing the low-voltage ride-through capability of a wind turbine double- fed induction generator using ANFIS 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.

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

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