Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems
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...
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my.uniten.dspace-341852024-10-14T11:18:20Z Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems Umar D.A. Alkawsi G. Jailani N.L.M. Alomari M.A. Baashar Y. Alkahtani A.A. Capretz L.F. Tiong S.K. 57218304981 57191982354 58297401800 55627877302 56768090200 55646765500 6602660867 15128307800 artificial intelligence MPPT wind energy harvesting system Energy harvesting Fuzzy inference Fuzzy neural networks Kinetic energy Kinetics Maximum power point trackers Particle swarm optimization (PSO) Wind power Wind turbines Energy harvesting systems Intelligent method Maximum power point Maximum Power Point Tracking Maximum Power Point Tracking algorithms Maximum power point tracking techniques Performance Tracking method Wind energy harvesting Wind energy harvesting system Wind 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. � 2023 by the authors. Final 2024-10-14T03:18:20Z 2024-10-14T03:18:20Z 2023 Review 10.3390/pr11051420 2-s2.0-85160767978 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160767978&doi=10.3390%2fpr11051420&partnerID=40&md5=a8a77e3a47689ac0ef8461b68a0f7996 https://irepository.uniten.edu.my/handle/123456789/34185 11 5 1420 All Open Access Gold Open Access Multidisciplinary Digital Publishing Institute (MDPI) Scopus |
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artificial intelligence MPPT wind energy harvesting system Energy harvesting Fuzzy inference Fuzzy neural networks Kinetic energy Kinetics Maximum power point trackers Particle swarm optimization (PSO) Wind power Wind turbines Energy harvesting systems Intelligent method Maximum power point Maximum Power Point Tracking Maximum Power Point Tracking algorithms Maximum power point tracking techniques Performance Tracking method Wind energy harvesting Wind energy harvesting system Wind |
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artificial intelligence MPPT wind energy harvesting system Energy harvesting Fuzzy inference Fuzzy neural networks Kinetic energy Kinetics Maximum power point trackers Particle swarm optimization (PSO) Wind power Wind turbines Energy harvesting systems Intelligent method Maximum power point Maximum Power Point Tracking Maximum Power Point Tracking algorithms Maximum power point tracking techniques Performance Tracking method Wind energy harvesting Wind energy harvesting system Wind Umar D.A. Alkawsi G. Jailani N.L.M. Alomari M.A. Baashar Y. Alkahtani A.A. Capretz L.F. Tiong S.K. Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems |
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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. � 2023 by the authors. |
author2 |
57218304981 |
author_facet |
57218304981 Umar D.A. Alkawsi G. Jailani N.L.M. Alomari M.A. Baashar Y. Alkahtani A.A. Capretz L.F. Tiong S.K. |
format |
Review |
author |
Umar D.A. Alkawsi G. Jailani N.L.M. Alomari M.A. Baashar Y. Alkahtani A.A. Capretz L.F. Tiong S.K. |
author_sort |
Umar D.A. |
title |
Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems |
title_short |
Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems |
title_full |
Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems |
title_fullStr |
Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems |
title_full_unstemmed |
Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems |
title_sort |
evaluating the efficacy of intelligent methods for maximum power point tracking in wind energy harvesting systems |
publisher |
Multidisciplinary Digital Publishing Institute (MDPI) |
publishDate |
2024 |
_version_ |
1814061170110234624 |
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13.209306 |