An Optimal Torque Control based on Intelligent Tracking Range (MPPT-OTC-ANN) for Permanent Magnet Direct Drive WECS

Meghni, Billel; Chojaa, Hamid; Boulmaiz, Amira

doi:10.1109/icecocs50124.2020.9314304

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…However, a critical consideration prior to the development of the controller is the appropriate selection of the generator. Constant-speed operation, despite its drawbacks such as low conversion efficiency, direct impact of varying wind speeds on electrical utility, and high sensitivity to voltage drops and grid faults [5], has been surpassed by the adoption of variable-speed wind generators [6]. Various types of generators are used in wind energy production, including asynchronous, synchronous, DC, and doubly-fed induction generator (DFIG) [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Dardabi,

Djebli,

Chojaa

et al. 2024

PLoS ONE

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This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

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Section: Introductionmentioning

confidence: 99%

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Dardabi,

Djebli,

Chojaa

et al. 2024

PLoS ONE

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“…Indirect techniques include the optimal torque (OT) method. In this method, control is achieved by monitoring optimum torque to achieve optimum wind energy utilization [34][35][36]. In [37] the authors described the power signal feedback (PSF), an indirect technique for maximizing the output power of a PMSG generator by determining the reference power for a particular wind speed.…”

Section: Introductionmentioning

confidence: 99%

Hybrid MPPT Control: P&O and Neural Network for Wind Energy Conversion System

Dahmane,

Boulaoutaq,

Bouachrine

et al. 2023

Journal of Robotics and Control

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In the field of wind turbine performance optimization, many techniques are employed to track the maximum power point (MPPT), one of the most commonly used MPPT algorithms is the perturb and observe technique (PO) because of its ease of implementation. However, the main disadvantage of this method is the lack of accuracy due to fluctuations around the maximum power point. In contrast, MPPT control employing neural networks proved to be an effective solution, in terms of accuracy. The contribution of this work is to propose a hybrid maximum power point tracking control using two types of MPPT control: neural network control (NNC) and the perturbation and observe method (PO), thus the PO method can offer better performance. Furthermore, this study aims to provide a comparison of the hybrid method with each algorithm 𝑃𝑂 and NNC. At the resulting duty cycle of the 2 methods, we applied the combination operation. A DC-DC boost converter is subjected to the hybrid MPPT control. This converter is part of a wind energy conversion system employing a permanent magnet synchronous generator (PMSG). The chain is modeled using MATLAB/Simulink software. The effectiveness of the controller is tested at varying wind speeds. In terms of the Integral time absolute error (ITAE), using the PO technique, the ITAE is 9.72. But, if we apply the suggested technique, it is smaller at 4.55. The corresponding simulation results show that the proposed hybrid method performs best compared to the PO method. Simulation results ensure the performance of the proposed hybrid MPPT control.

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“…If the step size is large, it causes power fluctuations; if it is small, the response time of the model to changes in wind speed increases. To address these challenges, hybrid [13][14] , modified [15][16][17][18] , and optimized [19][20] versions of traditional indirect and direct power control algorithms have been presented. These hybrid and modified methods solve challenges associated with conventional algorithms.…”

Section: Introduction1mentioning

confidence: 99%

A Novel Modified Fuzzy-Predictive Control of Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

Akbari,

Shadlu

2023

Chin. J. Electr. Eng.

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A wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG) is an effective solution for renewable energy generation in modern power systems. The main advantages of PMSG include high performance at high and low speeds, minimal control effort owing to lower rotor inertia, self-excitation, high reliability, and simplicity of structure compared with induction generators. However, the intermittent nature of wind energy implies that maximum efficiency is not obtained from this system. Accordingly, maximum power point tracking (MPPT) in wind turbine systems has been proposed to address this problem. Traditional MPPT strategies suffer from severe output power fluctuations, low efficiency, and significant ripples in turbine rotation speed. This paper presents a novel MPPT control strategy based on fuzzy logic control (FLC) and model predictive control (MPC) to extract the maximum power from a PMSG-WECS and control the machine-side and grid-side converters. The simulation results obtained from Matlab/Simulink confirm the superiority of the control model in eliminating the output power fluctuations of the wind generators and accurately tracking the maximum power point. A comparative study between conventional MPPT and control methods is also conducted.

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An Optimal Torque Control based on Intelligent Tracking Range (MPPT-OTC-ANN) for Permanent Magnet Direct Drive WECS

Cited by 8 publications

References 10 publications

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

Hybrid MPPT Control: P&O and Neural Network for Wind Energy Conversion System

A Novel Modified Fuzzy-Predictive Control of Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

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