Optimal Tuning of a New Multi-input Multi-output Fuzzy Controller for Doubly Fed Induction Generator-Based Wind Energy Conversion System

Nasef, Sahar Abd Elmoniem; Hassan, Amal A.; Elsayed, Hanaa T.; Zahran, Mohamed; El-Shaer, Mohamed K.; Abdelaziz, Almoataz Y.

doi:10.1007/s13369-021-05946-4

Cited by 16 publications

(6 citation statements)

References 38 publications

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“…Discovery that MFO‐FLC controller is identified as the best‐optimized controller, exhibiting excellent behavior in variable wind speed conditions. [ 23 ] The study provides an overview of the current state‐of‐the‐art ML methods impacting sustainable energy production in various sectors. Emphasis is placed on applications with significant long‐term sustainability potential, including renewable energies (wind power, solar power, hydropower, and biomass), computer‐aided catalyst design, smart grids, and energy storage devices.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

Abubakar,

Che,

Faheem

et al. 2024

Adv Energy and Sustain Res

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To address the rising energy demands in industrial and public sectors, integrating zero‐carbon emission energy sources into the power grid is crucial. Smart grids, equipped with advanced sensing, computing, and communication technologies, offer an efficient way to incorporate renewable energy resources and manage power systems effectively. However, improving solar energy efficiency, which currently contributes around 3.6% to global electricity, is a challenge in smart grid infrastructures. This research tackles this issue by deploying machine learning models, specifically recurrent neural network (RNN), long short‐term memory (LSTM), and gate recurrent unit (GRU), to predict measurements that could enhance solar power generation in smart grids. The objective is to boost both performance and accuracy of solar power generation in the smart grid. The study conducts experimental analyses and performance evaluations of these models in smart grid environments, considering factors like power output, irradiance, and performance ratio. The results, presented through graphical visualizations, show notable improvements, particularly with the LSTM model, which achieves a 97% accuracy, outperforming the RNN and GRU models. This outcome highlights the LSTM model's effectiveness in accurately predicting measurements, thereby advancing solar power generation efficiency in the smart grid framework.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

Abubakar,

Che,

Faheem

et al. 2024

Adv Energy and Sustain Res

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“…For a 1.5 MW doubly fed induction generator (DFIG) in a grid-connected wind energy conversion system (WECS), the authors in [11,12] presented optimal design and tuning of fuzzy logic controllers (FLCs) using sophisticated methodologies like the particle swarm optimizer (PSO), the gray wolf optimization (GWO), the moth-fame optimizer (MFO), and the multi-verse optimizer (MVO). Te grid-side converter, current regulator, and rotor-side converter of the back-to-back DFIG wind turbine all have FLC scaling factors that are optimized.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Controller Design and Fault Prediction Using Machine Learning Model

Kumar

Pande

Kumar

et al. 2023

International Transactions on Electrical Energy Systems

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In a solar power plant, a solid phase transformer and an optimization coordinated controller are utilized to improve transient responsiveness. Transient stability issues in a contemporary electrical power system represent one of the difficult tasks for an electrical engineer due to the rise in uncertain renewable energy sources (RESs) as a result of the need for green energy. The potential for terminal voltage to be adversely impacted by this greater RES raises the possibility of electrical device damage. It is possible to use a solid state transformer (SST) or smart transformer to address a transient response issue. These devices are frequently employed to interact between RES and a power grid. SST features a variety of regulated converters to maintain the necessary voltage levels. This method can therefore simultaneously lessen power fluctuations and transient responsiveness. In order to improve the quality of RES power injections and the electrical system’s transient stability, this work provides a controller design for a solar photovoltaic (SPV) system that is connected to the grid by SST. The optimization of a controller model is proposed by modifying a PI controller taken from a commercial one. With the use of IEEE 39 standard buses, the proposed controller is tested. When evaluating the effectiveness of a suggested controller, it is important to take into account a variety of solar radiation patterns as well as a time delay uncertainty that can range from 425 ms to 525 ms. According to simulation results, the proposed controller can be employed to lessen power fluctuation brought on by unpredictable RES. Additionally, the proposed coordinated regulation of SPV and SST can prevent catastrophic damage in the event of substantial disturbances like a circuit breaker collapsing to expand a power line due to a fault by inhibiting significant voltage cycles within an electronic appliance’s rated voltage limit. The results indicate that a transitory stability issue in a modern power system caused by an unforeseen increase in RES may be addressed utilizing the suggested controllers as alternatives.

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“…In [150], Nasef et al introduced an optimum tuning of FLC for a 1.5 MW grid connected DFIG-based WECS by using intelligent methods like the gray wolf, particle swarm optimizer, multi-verse optimizer, and moth-flame optimizer for both grid and generator side controllers. A multi-objective optimization was proposed with the aim of reducing the steady-state errors of these controllers to enhance the transient operation of the system under variable wind speed conditions.…”

Section: Dfig Based Wecsmentioning

confidence: 99%

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

Mostafa

El-Hay

Elkholy

2022

Arch Computat Methods Eng

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Wind energy is an effective and promising renewable energy source to produce electrical energy. Wind energy conversion systems (WECS) have been developing on a wide scale worldwide. The expansion of wind energy demand tends to produce high-quality output power in terms of grid integration. Due to the intermittent nature of wind energy, great challenges are found regarding WECS modeling, control, and grid integration. This paper introduces a comprehensive review of WECS and their grid-interface systems based on soft computing methods. To achieve this aim, more than 300 articles are organised and only 160 papers are presented in this review. This is intended to cover a broad range of topics concerning the configurations of WECS, electrical generators, and various topologies of power converters used for control and grid integration. Furthermore, international grid codes for wind energy integration with electric grids, particularly frequency, power factor, and low voltage ride through (LVRT) capability are investigated. The major controller approaches and topologies for grid and generator converters are discussed. Different aspects of modern control of WECS are introduced either for grid-side or generator-side. Moreover, control strategies for maximum power point tracking methods are compared along with methods of frequency control. This review paper introduces a comprehensive and a useful summery for the recent work in literature regarding WECS. Detailed modelling, control, and grid integration along with comparisons and discussion are introduced.

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Optimal Tuning of a New Multi-input Multi-output Fuzzy Controller for Doubly Fed Induction Generator-Based Wind Energy Conversion System

Cited by 16 publications

References 38 publications

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

Intelligent Controller Design and Fault Prediction Using Machine Learning Model

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

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