Enhanced Atom Search Optimization Based Optimal Control Parameter Tunning of PMSG for MPPT

He, Xin; Wei, Ping; Gong, Xiao-Yan; Meng, Xiangfeng; Dong, Shan; Zhu, Jiawei

doi:10.32604/ee.2022.015910

Cited by 1 publication

(5 citation statements)

References 39 publications

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“…Accordingly, refs. [3,[27][28][29][30][31][32][33] applied the standalone AI-based method to track the MPP of WECS, while references [20,34,35] used the hybrid AI-based method.…”

Section: Intelligent-based Mppt Methodsmentioning

confidence: 99%

“…Figure 5 shows the list of recent AI algorithms that are applied in MPPT controllers for WEHS. Accordingly, [3,[27][28][29][30][31][32][33] applied the standalone AI-based method to track the MPP of WECS, while references [20,34,35] used the hybrid AI-based method. The summary of the standalone AI-based (MPPT) algorithms and their contributions to enhancing MPPT tracking in WEHS are provided in Table 3…”

Section: Intelligent-based Mppt Methodsmentioning

confidence: 99%

“…Recently, researchers have been focusing on implementing AI approaches in MPPT controllers. These approaches have been proposed either in standalone form, such as in references [3,[27][28][29][30][31][32][33] or in hybrid forms, such as in references [20,34,35]. The use of an artificial neural network (ANN) has been employed in some of these approaches, such as in references [3,27,32,33], resulting in enhanced system performance, resilience, power response, and efficiency.…”

Section: Research Backgroundmentioning

confidence: 99%

“…The use of an artificial neural network (ANN) has been employed in some of these approaches, such as in references [3,27,32,33], resulting in enhanced system performance, resilience, power response, and efficiency. Optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), Archimedes optimization (AOA), and grasshopper optimization algorithm (GOA) have also been proposed, resulting in improved tracking speed, energy generation, dynamic performance, and global search capability to track the MPP, as demonstrated in references [28][29][30][31].…”

Section: Research Backgroundmentioning

confidence: 99%

“…Similarly, MPPT algorithms based on neural networks can adapt to changing environmental conditions, resulting in improved system performance and increased energy conversion efficiency. Furthermore, the intelligent-based MPPT method using optimization techniques such as PSO and other metaheuristic algorithms such as ACOA, AOA, CS, GOA, MOGOA, ECPO, and EASO techniques have been used for the MPPT in WEHS, and the results of their performance have shown a very good improvement of the technique in terms of quick searching of the WEHS's optimum operation point [28][29][30][31]43]. Therefore, in terms of accuracy, efficiency, and adaptability, intelligent-based MPPT methods outperform traditional MPPT techniques, resulting in optimal energy extraction from the wind source.…”

Section: Intelligent-based Mppt Modelsmentioning

confidence: 99%

See 4 more Smart Citations

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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“…Accordingly, refs. [3,[27][28][29][30][31][32][33] applied the standalone AI-based method to track the MPP of WECS, while references [20,34,35] used the hybrid AI-based method.…”

Section: Intelligent-based Mppt Methodsmentioning

confidence: 99%

Section: Intelligent-based Mppt Methodsmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Research Backgroundmentioning

confidence: 99%

Section: Intelligent-based Mppt Modelsmentioning

confidence: 99%

See 3 more Smart Citations

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

Umar,

Alkawsi,

Jailani

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

Enhanced Atom Search Optimization Based Optimal Control Parameter Tunning of PMSG for MPPT

Cited by 1 publication

References 39 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

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