Levenberg-Marquardt SMC control of grid-tied Doubly Fed Induction Generator (DFIG) using FRT schemes under symmetrical fault

Azeem, Babar; Ullah, Zahid; Rehman, Faisal; Ali, Sahibzada Muhammad; Haider, Aun; Saeed, Suleman; Hussain, I.; Mehmood, C. A.; Khan, Bilal

doi:10.1109/icpesg.2018.8384493

Cited by 11 publications

(6 citation statements)

References 5 publications

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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]. The bigger the turbine blades, the more energy is produced from the turbine [62].…”

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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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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“…These inventive approaches encompass specific control methodologies to enhance failure ride-through capabilities, voltage regulation, and reactive power injection, effectively addressing the complexities of grid failures. This endeavor draws support from meticulous comparative analyses and simulation findings, which further chart a course for prospective research directions [2][3][4][5]. Looking ahead, the role of artificial intelligence (AI) techniques in smart grid management, spanning wind, solar, and biogas systems, is poised for pivotal significance [6,7].…”

Section: Introductionmentioning

confidence: 98%

“…Yet, the integration of these renewable assets into intelligent electricity grids presents multifaceted challenges. Scholars are responding by innovating hybrid solutions aimed at bolstering grid stability and refining control mechanisms [2][3][4][5]. These inventive approaches encompass specific control methodologies to enhance failure ride-through capabilities, voltage regulation, and reactive power injection, effectively addressing the complexities of grid failures.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Power Generation Forecasting in Smart Grids Using Hybrid Autoencoder Long Short-Term Memory Machine Learning Model

Zafar,

Che,

Ahmed

et al. 2023

IEEE Access

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This study explores the implementation of advanced machine learning techniques to enhance the integration of renewable energy into smart grids, focusing specifically on predicting solar power generation for the upcoming year. Three distinct machine learning models are employed: the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and a hybrid model that combines Autoencoder and Long Short-Term Memory (AE-LSTM). Using real-time solar power production data spanning a year, these models are trained and evaluated using mean absolute error (MAE) and mean squared error (MSE) as performance metrics. Results highlight the superior accuracy of the hybrid AE-LSTM model compared to the LSTM model as well as Bi-LSTM model, attributed to its capability to capture intricate temporal patterns and correlations within the data. This research underscores the significant potential of machine learning techniques, particularly the hybrid AE-LSTM approach, in facilitating the seamless integration of renewable energy resources into smart grids, contributing to more efficient and environmentally conscious power systems. Furthermore, a comprehensive analysis reveals the hybrid AE-LSTM model's capacity to produce superior predictions due to its additional training, solidifying its advantage over models solely reliant on the other model's architecture. In summary, this study demonstrates the effectiveness of advanced machine learning methodologies in revolutionizing renewable energy integration, with the hybrid AE-LSTM model standing out as a promising avenue for enhanced prediction accuracy.

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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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Levenberg-Marquardt SMC control of grid-tied Doubly Fed Induction Generator (DFIG) using FRT schemes under symmetrical fault

Cited by 11 publications

References 5 publications

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

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

Enhancing Power Generation Forecasting in Smart Grids Using Hybrid Autoencoder Long Short-Term Memory Machine Learning Model

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

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