A Novel Hybrid MPPT Controller Using (P&amp;O)-neural Networks for Variable Speed Wind Turbine Based on DFIG

Azzouz, Said; Messalti, Sabir; Harrag, Abdelghani

doi:10.18280/mmc_a.920104

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…The hybrid methods proposed by [42,45,46], which combine P&O and FL-based techniques, can adapt to changes in wind speed and direction, resulting in improved tracking accuracy and energy conversion efficiency. Similarly, hybrid MPPT methods that combine P&O and neural network-based techniques yielded a hybrid system that successfully tracked the System MPPT [47]. Therefore, resulting in improved system performance, such as tracking accuracy and increased energy conversion efficiency.…”

Section: Hybrid Methods (Traditional and Intelligent-based Mppt)mentioning

confidence: 99%

Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

Umar,

Alkawsi,

Jailani

et al. 2023

Processes

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As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques comprising two different maximum power point tracking (MPPT) algorithms were further proposed to eliminate the shortages. Furtherly, Artificial Intelligence (AI)-based MPPT algorithms were proposed for the WEHS as either standalone or integrated with the traditional MPPT methods. Therefore, this research focused on the review of the AI-based MPPT and their performances as applied to WEHS. Traditional MPPT methods that are studied in the previous articles were discussed briefly. In addition, AI-based MPPT and different hybrid methods were also discussed in detail. Our study highlights the effectiveness of AI-based MPPT techniques in WEHS using an artificial neural network (ANN), fuzzy logic controller (FLC), and particle swarm optimization (PSO). These techniques were applied either as standalone methods or in various hybrid combinations, resulting in a significant increase in the system’s power extraction performance. Our findings suggest that utilizing AI-based MPPT techniques can improve the efficiency and overall performance of WEHS, providing a promising solution for enhancing renewable energy systems.

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Section: Hybrid Methods (Traditional and Intelligent-based Mppt)mentioning

confidence: 99%

Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

Umar,

Alkawsi,

Jailani

et al. 2023

Processes

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“…Modified P&O merged with PSF is presented in [151] where MPP is first tracked using MPO and employs PSF after that. MPPT based on ANN employing the P&O rules is discussed in [152], and the important benefits of using this technique are that it eliminates the need for the information about the values of λ and C p and also provides better accuracy. Figure 19…”

Section: Pando Based Hybrid Mppt Algorithmsmentioning

confidence: 99%

A Review of Maximum Power Point Tracking Algorithms for Wind Energy Conversion Systems

Pande

Nasikkar

Kotecha

et al. 2021

JMSE

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Renewable energy resources are gaining a lot of popularity. Several researchers have worked on the tracking and extraction of energy from these sources. In the past few decades, among the available green energy resources, wind energy has been the most attractive option among the resources available. It is imperative to use the maximum power available in the wind to achieve the wind turbine (WT) operation at maximum power. The maximum power point tracking (MPPT) algorithms are a pioneer in this context. Many research papers are contributed in this domain which necessitates a thorough review while choosing an appropriate technique. This paper comprehensively focuses on reviewing different algorithms in the past and present for tracking maximum power point, and capturing maximized output power from the wind energy conversion system (WECS). In this paper, the algorithms are classified based on the direct and indirect power measurement, hybrid and smart algorithms for tracking maximum power point, and they are compared, considering the parameters like complexity, convergence speed, use of sensors, memory requirement, need for knowledge of system parameters, etc. The immense popularity of the different versions of perturb and observe (P&O) based algorithms due to their various features is evident from the literature. The review reveals that the hybrid maximum power point tracking algorithms can use the advantages of the conventional methods and eliminate their drawbacks.

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“…Thus, developing the railway's digital tiny generation network is crucial to accomplishing a decrease in pollutants, power saving, and increased electricity stability for the station. [6][7] It is crucial for tracking the highest point in the grid-connected turbine architecture to optimize the productivity of solar power. Trains are one of the greatest energy-intensive forms of transportation.…”

Section: Introductionmentioning

confidence: 99%

Study and Analysis of RBFN based MPPT controller for wind energy integrated with Traction Power Supply System

Mebratu Delelegn

2024

jes

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The need of energy for railway is rising due to the increased speed at which trains must operate to maintain safety and dependability. This situation implies more electricity consumption and more challenges to railway operation stability. Therefore, this research suggests configuring wind energy integration with the current railway traction substations built on the current traction substation. The suggested setup consists of a converter that converts DC to DC with RBFN-based MPPT regulator wind energy source linked to a permanent magnet synchronous generator (PMSG), a rectifier, and a conventional traction system. The proposed system is modelled and analyzed by utilizing a radial basis function network (RBFN) in this study. Three traction substations: Aysha, Adegla and Dewanle:-are the subject of the case study, which integrates wind energy with the railway electric power system working at 25 kV AC. For the Aysha wind farm, a MATLAB/Simulink simulation is run in order to confirm the suggested technology. The setup with or without MPPT was used in order to evaluate the results. The proposed system produces an average output of 260.3kW without MPPT and 289.3kW with MPPT controller at wind speed of 20m/sec. The simulation findings suggest that the RBFN-based control method performs better under diverse wind conditions and is more suited for wind energy integration with traction system.

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A Novel Hybrid MPPT Controller Using (P&O)-neural Networks for Variable Speed Wind Turbine Based on DFIG

Cited by 7 publications

References 22 publications

Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

A Review of Maximum Power Point Tracking Algorithms for Wind Energy Conversion Systems

Study and Analysis of RBFN based MPPT controller for wind energy integrated with Traction Power Supply System

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