A machine learning approach for active/reactive power control of grid-connected doubly-fed induction generators

Tavoosi, Jafar; Mohammadzadeh, Ardashir; Pahlevanzadeh, Bahareh; Kasmani, Morad Bagherzadeh; Band, Shahab S.; Safdar, Rabia; Mosavi, Amir

doi:10.1016/j.asej.2021.08.007

Cited by 43 publications

(27 citation statements)

References 33 publications

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“…To solve the parameter calculations, several conventional deterministic optimization approaches have been used (Easwarakhanthan et al , 1986; Cao et al , 2021; Shamshirband et al , 2019; Shamshirband et al , 2020; Tavoosi et al , 2021). These two types of cells, however, are nonlinear, nonconvex and closely coupled systems, and most of these traditional methods require conditions of consistency, convexity and differentiability, making it impossible for them to manage the above-mentioned parameter estimation problems.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid particle swarm optimization rat search algorithm for parameter estimation of solar PV and fuel cell model

Singla

Nijhawan

Oberoi

2022

COMPEL

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Purpose The purpose of the proposed hybrid method aims to increase population efficiency, and a local search is used to further improve the value of the global best solution. An experimental observation suggests that the model’s statistical outcomes are more aligned with the real-time experimental findings. Design/methodology/approach A novel metaheuristic efficient hybrid algorithm, i.e. hybrid particle swarm optimization rat search algorithm, is introduced and applied for parameter extraction of hybrid energy system. This proposed hybrid method rules out the chances of local minima, hence enhancing the precision of the parametric estimation. The parameter extraction and error is calculated for the solar photovoltaic (PV)–fuel cell system using the proposed algorithm. Findings Nonparametric statistical tests are also conducted to indicate the findings of the outcome parameters using various metaheuristic algorithms. The proposed algorithm is better than the rest of the compared algorithms in the study. Originality/value The authors proposed a novel algorithm, and this proposed algorithm is implemented on hybrid solar PV and fuel cell-based system for parameter extraction. The nonparametric test results clearly suggest that the proposed algorithm is far more effective for parameter estimation of the test system.

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Section: Introductionmentioning

confidence: 99%

A novel hybrid particle swarm optimization rat search algorithm for parameter estimation of solar PV and fuel cell model

Singla

Nijhawan

Oberoi

2022

COMPEL

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“…The integration of renewable energy resources in the smart grid supports sus-tainable development [51]. The techniques for effective energy management in smart grids are regarded to be a huge challenge above and beyond the full integration of renewable energy resources, and a current issue with domestic energy demand is predicted to worsen by another 24% in the next decades [52]. The major posi-tion played by wind energy in the current and future generations will only continue to grow.…”

Section: Discussionmentioning

confidence: 99%

“…Also, Nickel et al used machine learning methods to predict the wave energy converter power. They supposed that high-frequency waves could affect modeling efficiency [35]. Also, Mousavi et al [12] utilized the LSTM algorithm to predict the generated power of Searaser.…”

Section: Introductionmentioning

confidence: 99%

https://www.academia.edu/93557562/Predicting_Power_and_Hydrogen_Generation_of_a_Renewable_Energy_Converter_Utilizing_Data_Driven_methods_A_Sustainable_Smart_Grid_Case_Study

Rituraj¹

2022

Preprint

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This study proposes a data-driven methodology for modeling power and hydrogen generation of a sustainable energy converter. The wave and hydrogen production at different wave heights and wind speeds are predicted. Furthermore, this research emphasizes and encourages the possibility of extracting hydrogen from ocean waves. By using the extracted data from FLOW-3D software simulation and the experimental data from the special test in the ocean, the comparison analysis of two data-driven learning methods is conducted. The results show that the amount of hydrogen production is propor-tional to the amount of generated electrical power. The reliability of the proposed renewable energy converter is further discussed as a sustainable smart grid application.

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“…According to international statistics on the usage of electrical equipment in farming, more than 1,000,000 electrical tractors will be employed until 2025 [4]. Because these farms are not connected to the power grid, they rely heavily on renewable energy sources like solar panels, wind turbines, or a mix of the two [5][6][7]. The decision to use one of such energy sources is influenced by many criteria, including the farm's budget, location, and the amount of power required daily [8,9].…”

Section: Introductionmentioning

confidence: 99%

Decreasing the Battery Recharge Time if Using a Fuzzy Based Power Management Loop for an Isolated Micro-Grid Farm

et al. 2022

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An isolated micro-grid has different requirements from the traditional power grids. Several energy sources may be linked for the purpose of sharing load demand without being linked to the grid. The isolated micro-grid is made up of at least one energy generator, an energy storage system, and a load portion. Because there are several energy sources and a range of models, the power flow must be managed to ensure the safety of all hardware. Monitoring the flow of power from multiple energy sources necessitates adherence to many parameters and other requirements. As a result, the goal of this work is to identify a worthwhile solution for providing the appropriate portions with the necessary power while also obtaining the necessary energy from other sources. The approach is based on the fuzzy logic controller, which is an intelligent technology. This regulator is used in an efficient process that tries to control all of the equipment in the isolated micro-grid under investigation. The MATLAB/Simulink platform is employed for simulating this proposed system, and then, the depicted results were discussed and compared. Showing the traditional relay control, the standard PI regulator, and a neural control combination process, the achieved results prove that it is possible to reduce the battery recharge time to half; if the proposed fuzzy controller is used. Then, the established controller specifications have been used for evaluating the energy performances of the hybrid energy system under a real case situation in a specific location in the world. Consequently, the obtained results prove that this proposal power management system will be largely beneficial for such energy storage applications and an energy yield can be assured during all climatic conditions and specifications.

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A machine learning approach for active/reactive power control of grid-connected doubly-fed induction generators

Cited by 43 publications

References 33 publications

A novel hybrid particle swarm optimization rat search algorithm for parameter estimation of solar PV and fuel cell model

A novel hybrid particle swarm optimization rat search algorithm for parameter estimation of solar PV and fuel cell model

https://www.academia.edu/93557562/Predicting_Power_and_Hydrogen_Generation_of_a_Renewable_Energy_Converter_Utilizing_Data_Driven_methods_A_Sustainable_Smart_Grid_Case_Study

Decreasing the Battery Recharge Time if Using a Fuzzy Based Power Management Loop for an Isolated Micro-Grid Farm

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