Evaluating a Hybrid Circuit Topology for Fault-Ride through in DFIG-Based Wind Turbines

Saeed, Sarmad; Asghar, Rafiq; Mehmood, Faizan; Saleem, Haider; Azeem, Babar; Ullah, Zahid

doi:10.3390/s22239314

Cited by 19 publications

(8 citation statements)

References 32 publications

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“…A hybrid scheme is introduced in Ref. [35] , which consists of a fault current limiter and a DC-DC converter or chopper circuit (CC). A double protection scheme is used in [36], the first is a CB circuit to protect the RSC, while the other is a CC to guard the DCL.…”

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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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 wind is a renewable source used to run wind turbines (WTs) to convert wind speed into electrical energy [58][59][60]. The energy generated from WTs depends upon the turbine's size and shape, wind availability, and wind speed quotient [61].…”

Section: Wind Energy Modellingmentioning

confidence: 99%

Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Alzahrani

Hafeez

Ali³

et al. 2023

Sustainability

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Multi-objective energy optimization is indispensable for energy balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective optimization is challenging due to uncertainty and multi-conflicting parameters at both the generation and demand sides. Thus, opting for a model that can solve load and distributed energy source scheduling problems is necessary. This work presents a model for operation cost and pollution emission optimization with renewable generation in the SPG. Solar photovoltaic and wind are renewable energy which have a fluctuating and uncertain nature. The proposed system uses the probability density function (PDF) to address uncertainty of renewable generation. The developed model is based on a multi-objective wind-driven optimization (MOWDO) algorithm to solve a multi-objective energy optimization problem. To validate the performance of the proposed model a multi-objective particle swarm optimization (MOPSO) algorithm is used as a benchmark model. Findings reveal that MOWDO minimizes the operational cost and pollution emission by 11.91% and 6.12%, respectively. The findings demonstrate that the developed model outperforms the comparative models in accomplishing the desired goals.

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“…However, the integration of these resources into the smart grid is a challenge that needs to be addressed. Almost all researchers focus on enhancing stability and control in power grid systems through the proposal of hybrid solutions, emphasizing the importance of addressing challenges associated with grid faults, and utilizing specific control strategies to improve fault ride-through capabilities, voltage regulation, and reactive power injection, supported by simulation results and comparisons, while also suggesting future directions for further research [2][3][4][5]. In the near future, smart grids such as wind, solar, biogas, and so on will need to be controlled through artificial intelligence techniques to enhance their efficiency [6,7].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM

et al. 2023

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The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these models on performance measures such as mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems.

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Evaluating a Hybrid Circuit Topology for Fault-Ride through in DFIG-Based Wind Turbines

Cited by 19 publications

References 32 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.

Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM

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