Publication: Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems
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Date
2023
Authors
Umar D.A.
Alkawsi G.
Jailani N.L.M.
Alomari M.A.
Baashar Y.
Alkahtani A.A.
Capretz L.F.
Tiong S.K.
Journal Title
Journal ISSN
Volume Title
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Abstract
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.
Description
Keywords
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