An elite approach for enhancement of LVRT in doubly fed induction generator (DFIG)-based wind energy conversion system (WECS): a FAMSANFIS approach

Manohar, G. Arun; Venkateshwarlu, S.; JayaLaxmi, Askani

doi:10.1007/s00500-022-07419-8

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…The LVRT methods are classified as additional auxiliaries, additional auxiliaries along with controller designs and only controller designs [11][12][13][14][15]. Addition auxiliary methods even though overcome LVRT issues, they result in additional cost, floor area and complex control strategies.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Comparative Analysis of Low Voltage Ride Through Techniques of DFIG Connected to Grid using AI Techniques

Gangikunta,

Venkateshwarlu,

Jaya Laxmi

2024

E3S Web Conf.

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The requirement for clean and eco-friendly energy initiated the production of renewable energy. Wind energy promises attractive features such as bulk power production and reduced maintenance cost. Advancement of technologies in the field of wind turbines and generators made the gates open for attractive investments. Adjustable-speed wind turbines tied to Doubly Fed Induction Generators (DFIG) became the most excellent choice of power utilities due to their low-cost power converters and four-quadrant control of useful and wattless powers. The ability to extract a high amount of power for a specified wind speed is a major advantage of DFIG which attracted Power system Operators (PSOs). Low voltage ride-through and wattless Spower support to the grid are estimated and compared using conventional PI controllers, Artificial Neural Network (ANN), and Random Forest Optimization (RFO) algorithm.

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Section: Simulation and Resultsmentioning

confidence: 99%

Comparative Analysis of Low Voltage Ride Through Techniques of DFIG Connected to Grid using AI Techniques

Gangikunta,

Venkateshwarlu,

Jaya Laxmi

2024

E3S Web Conf.

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“…It was found that the proposed method improved LVRT capability for the single wind turbine DFIG system and the practical wind farm under transient conditions. In reference 38 , G. Manohar et al proposed a hybrid system based on fertile field algorithm and momentum search algorithm with ANFIS to enhance LVRT capability of DFIG based WECS. The optimization technique is employed to solve the objective function that is related to LVRT.…”

Section: Introductionmentioning

confidence: 99%

Optimal low voltage ride through of wind turbine doubly fed induction generator based on bonobo optimization algorithm

Mostafa

El-Hay

Elkholy

2023

Sci Rep

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The large-scale wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) has gained popularity in recent years because of its various economic and technical merits. The fast integration of WECS with existing power grids has caused negative influence on the stability and reliability of power systems. Grid voltage sags produce a high overcurrent in the DFIG rotor circuit. Such these challenges emphasise the necessity of the low voltage ride through (LVRT) capability of a DFIG for ensuring power grid stability during voltage dips. To deal with these issues simultaneously, this paper aims to obtain the optimal values of injected rotor phase voltage for DFIG and wind turbine pitch angles for all operating wind speeds in order to achieve LVRT capability. Bonobo optimizer (BO) is a new optimization algorithm that is applied to crop the optimum values of injected rotor phase voltage for DFIG and wind turbine pitch angles. These optimal values provide the maximum possible DFIG mechanical power to guarantee rotor and stator currents do not exceed the rated values and also deliver the maximum reactive power for supporting grid voltage during faults. The ideal power curve of a 2.4 MW wind turbine has been estimated to get the allowable maximum wind power for all wind speeds. To validate the results accuracy, the BO results are compared to two other optimization algorithms: particle swarm optimizer and driving training optimizer. Adaptive neuro fuzzy inference system is employed as an adaptive controller for the prediction of the values of rotor voltage and wind turbine pitch angle for any stator voltage dip and any wind speed.

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“…Conventionally, PI-based controllers regulate rotor current vectors through the machine side converter (MSC) as well as grid current vectors through the grid side converter (GSC) [11,12]. Due to intrinsic restrictions of the PI controllers, particularly with nonlinear systems and for better grid fault mitigation, various advanced control techniques as well as hardware solutions are presented in the literature [13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…A compound method having repetitive control and fuzzy based PI control is proposed in [13] for power smoothening in a PMSG based GCWT system. Likewise, better THD with enhanced LVRT ability is achieved in a grid connected WECS using adaptive parameter for a neural-fuzzy hybrid control [15]. Using a feedback linearizing scheme, a sliding mode control is exploited in [16] to mitigate the sub synchronous oscillations under various grid disturbances in the connected wind energy system.…”

Section: Introductionmentioning

confidence: 99%

Finite-Control-Set Model Predictive Control for Low-Voltage-Ride-Through Enhancement of PMSG Based Wind Energy Grid Connection Systems

et al. 2022

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Grid faults are found to be one of the major issues in renewable energy systems, particularly in wind energy conversion systems (WECS) connected to the grid via back-to-back (BTB) converters. Under such faulty grid conditions, the system requires an effective regulation of the active (P) and reactive (Q) power to accomplish low voltage ride through (LVRT) operation in accordance with the grid codes. In this paper, an improved finite-control-set model predictive control (FCS-MPC) scheme is proposed for a PMSG based WECS to achieve LVRT ability under symmetrical and asymmetrical grid faults, including mitigation of DC-link voltage fluctuation. With proposed predictive control, optimized switching states for cost function minimization with weighing factor (WF) selection guidelines are established for robust BTB converter control and reduced cross-coupling amid P and Q during transient conditions. Besides, grid voltage support is provided by grid side inverter control to inject reactive power during voltage dips. The effectiveness of the FCS-MPC method is compared with the conventional proportional-integral (PI) controller in case of symmetrical and asymmetrical grid faults. The simulation and experimental results endorse the superiority of the developed FCS-MPC scheme to diminish the fault effect quickly with lower overshoot and better damping performance than the traditional controller.

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An elite approach for enhancement of LVRT in doubly fed induction generator (DFIG)-based wind energy conversion system (WECS): a FAMSANFIS approach

Cited by 7 publications

References 41 publications

Comparative Analysis of Low Voltage Ride Through Techniques of DFIG Connected to Grid using AI Techniques

Comparative Analysis of Low Voltage Ride Through Techniques of DFIG Connected to Grid using AI Techniques

Optimal low voltage ride through of wind turbine doubly fed induction generator based on bonobo optimization algorithm

Finite-Control-Set Model Predictive Control for Low-Voltage-Ride-Through Enhancement of PMSG Based Wind Energy Grid Connection Systems

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