Optimization Using a Genetic Algorithm Based on DFIG Power Supply for the Electrical Grid

doi:10.5829/ije.2022.35.01a.11

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…These three operations must be applied to create new individuals to be evaluated with respect to their parents. The algorithm stops when we obtain individuals with optimal performances [29], [31], [32].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Robust control of wind turbine based on doubly-fed induction generator optimized by genetic algorithm

Elmouhi

Essadki

Elaimani³

2022

IJPEDS

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In this research paper we will cover the study of the modelling and the control methods of the variable speed wind turbine based on doubly-fed induction generator (DFIG). It represents the most stressed structure given its distinctive characteristics. To control the electrical powers generated by this system independently, the vector control with the stator flux orientation is founded according to two techniques: i) the control of the powers by the backstepping technique and ii) the robust control based on the active disturbances rejection control. After the synthesis of the controllers of those two methods, their performances will be tested and compared to evaluate their effectiveness. We are mainly interested in the robustness test of the two control strategies with respect to the internal parameters’ fluctuation of the generator. The computing of the different parameters regulators of these two strategies is carried out using a genetic algorithm. This computing method makes it possible to arrive at an optimal solution of the DFIG power control. The different parts are simulated using MATLAB/Simulink environment.

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Section: Genetic Algorithmmentioning

confidence: 99%

Robust control of wind turbine based on doubly-fed induction generator optimized by genetic algorithm

Elmouhi

Essadki

Elaimani³

2022

IJPEDS

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“…Reliability enhancing, power loss reduction and improving of voltage profile are of great importance for the power network, some or all of these indicators in one or two 14,30,32,33,38,57, 69 and 84 bus networks, using Markov processes (1), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) (2), dynamic reconfiguration of distribution networks with distributed generation (3)(4)(5), network stability through controlling the balance of active power between generation and consumption (6), Modified Monte Carlo Simulation (MMCS) (7), roulette wheel selection and the sequential Monte Carlo algorithm (8), Finite Markov Chain Imbedding Approach (FMCIA) and phase-type distributions (9), Multi-Objective Hybrid Teaching-Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) (10), particle swarm optimization (PSO) (11), backward-forward sweep method (12), advanced Genetic Algorithm (GA) (13), hybrid GWO-PSO (HPSGWO) algorithm (14)(15)(16), and Hybrid Particle Swarm Optimization (PSO) and Sea Horse Optimization (PSOSHO) algorithm (17), have been investigated and obtained favorable results.…”

Section: Introductionmentioning

confidence: 99%

Improving Reliability of Active Distribution Networks Using Probabilistic Assessment of Renewable Resource Units

Najjarpour,

Tousi,

Ebadi Zahedan

2024

IJE

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NOMENCLATUREVi min , Vi max The minimum and maximum voltage in the bus i, respectively GA, nb-1 Genetic algorithm, All basses except Slack bus, respectively Pi min , Pi max The minimum and maximum active power in the bus i, respectively OA Orthogonal Arrays Qi min , Qi max The minimum and maximum reactive power in the bus i, respectively Level The value of a random variable is based on an orthogonal array sij max Maximum apparent transmission power Delta The main effect of random variables on performance indicators 𝑇 𝑖 𝑚𝑎𝑥 . 𝑇 𝑖 𝑚𝑖𝑛 The maximum, and minimum taps number of transformers, respectively Rank, TM Random variable class, and Taguchi method, respectively fjψ, f*ψ The power and the nominal power passing through the ψ transmission line and experiment j, respectively Vi,j The voltage of bus i, and j respectively Nexp, ng, NC, NT, N, NB, NL Number of experiments, generator, compensators, transformers,modules, buses, and lines, respectively Gi,j , 𝐵 𝑖𝑗 Line ij conductance, and suspension respectively

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“…To highlight the high use of efficiency power, the REG is generally arranged using maximum power tracking (MPPT) algorithms [7][8][9][10]. As a result of the fluctuating and uncontrolled meteorological circumstances, it is classified as a generation that cannot be regulated [11][12][13][14][15]. Maximum power monitoring technology will be critical for getting the most energy out of a solar cell under a variety of weather situations.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

2022

IJE

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This research looks at how photovoltaic (PV) cells generate energy in different weather conditions. Photovoltaic power today plays a key role in the production of energy and satisfying the needs of consumers all over the world. The PV cell's ability to generate electricity was entirely dependent on sunshine and temperature fluctuations in the environment. Several researchers are working on a variety of MPPT methods for a photovoltaic system. Outdated MPPT techniques are unable to withstand a dramatic change in weather conditions. The fundamental purpose of this study is to associate the numerous unadventurous and clever controllers for MPPT of the PV system, such as the PSO, GA, and CNFF. The MATLAB environment was used to create and simulate the recommended intelligent controller for MPPT in the PV system. Furthermore, the aforementioned findings like Voltage, Current and Power with respect to different irradiance and temperature are compared and evaluated. The performance of the above-mentioned topologies has been related to the optimum intelligent controller for the PV system and cncluded that the CFFNN gives better efficiency with minimum time required to extract.

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Optimization Using a Genetic Algorithm Based on DFIG Power Supply for the Electrical Grid

Cited by 4 publications

References 19 publications

Robust control of wind turbine based on doubly-fed induction generator optimized by genetic algorithm

Robust control of wind turbine based on doubly-fed induction generator optimized by genetic algorithm

Improving Reliability of Active Distribution Networks Using Probabilistic Assessment of Renewable Resource Units

Intelligent Controllers to Extract Maximum Power for 10 KW Photovoltaic System

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