MPPT control design for variable speed wind turbine

Rachedi, Meriem Otmane; Saidi, Mohammed; Arbaoui, Fayçel

doi:10.11591/ijece.v10i5.pp4604-4614

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…For instance, some studies have focused on developing fuzzy fractional-order proportional-integral controllers to enhance the performance of direct-drive permanent-magnet synchronous generator wind turbines. This highlights the flexibility of fuzzy logic in adapting to rapidly changing wind speed conditions [17]. Further research includes comparative analyses of different fuzzy logic controllers in semisubmersible platform wind turbines [18].…”

Section: Advanced Control Of Wind Turbinesmentioning

confidence: 96%

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

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As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

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Section: Advanced Control Of Wind Turbinesmentioning

confidence: 96%

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

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“…This module is composed of ns cells in series and np cells in parallel. The next step is to model the chopper with its control system [15]. It is important to know that several combinations are possible depending on the type of chopper and MPPT [16], [17] chosen.…”

Section: B Association Of Photovoltaic Cellsmentioning

confidence: 99%

Ecological impact due to the implementation of a modeled and optimized hybrid system

Echarradi

Benlafkih

Hadjoudja

et al. 2023

IJECE

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<span lang="EN-US">This paper presents a very alarming forecasts about our future and particularly for a medium and long-term future. That is to say, several actions are being carried out by different civil and state parties to deal with these very concrete threats. And it is within this framework that this paper fits, and its objective is to highlight the hybrid systems and more precisely the photovoltaic-wind hybrid systems coupled with storage batteries, as an efficient alternative to the classical means of electricity production. This work will adopt a method of obtaining results called performance evaluation, so this manuscript will present firstly the mathematical model of this hybrid system to best conceive what it is about, then as second part will determine the exact figures of the largest amount of carbon dioxide (CO<sub>2</sub>) that can be avoided using this technology and following a precise methodology, this can be applied to our situation, i.e., a simple house or to any other type of installation.</span>

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“…For example, Abdullah and Ali present in [1] an application of motor's torque control using a classical controller proportional, integral, derivative, or proportional integral derivative (PID) controller. Rachedi et al in [2] presents a speed controller design for a wind turbine is presented, and in [3] the control of a DC-DC converter. For water management systems oriented to applications such as precision agriculture [4], the use of control strategies is also required, which can be very varied, as explained in [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Embedded fuzzy controller for water level control

Baquero

Ortega

Moreno

2022

IJECE

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This article presents the design of a fuzzy controller embedded in a microcontroller aimed at implementing a low-cost, modular process control system. The fuzzy system's construction is based on a classical proportional and derivative controller, where inputs of error and its derivate depend on the difference between the desired setpoint and the actual level; the goal is to control the water level of coupled tanks. The process is oriented to control based on the knowledge that facilitates the adjustment of the output variable without complex mathematical modeling. In different response tests of the fuzzy controller, a maximum over-impulse greater than 8% or a steady-state error greater than 2.1% was not evidenced when varying the setpoint.

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MPPT control design for variable speed wind turbine

Cited by 6 publications

References 21 publications

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Ecological impact due to the implementation of a modeled and optimized hybrid system

Embedded fuzzy controller for water level control

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