Adaptive Particle Swarm Optimization Agorithm for Maximum Power Point Tracking of Vertical Sxis Wind Turbines

This paper investigates the optimization problem of a vertical axis wind turbine (VAWT) energy system aiming to maximise the output power of a permanent magnet synchronous generator (PMSG) based system. To this end, a maximum power point tracking (MPPT) method based on rotational speed control is proposed and combined with an adaptive particle swarm optimization (APSO) algorithm to optimally regulate the rotational speed. Simulation results show that the wind turbine with this method can effectively operate at the maximum power point and achieve the speed regulation and output power optimization under wind speed variation.

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Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/aiim64537.2024.10934567
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.00

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article

Adaptive Particle Swarm Optimization Agorithm for Maximum Power Point Tracking of Vertical Sxis Wind Turbines

Xiuxia Zhang, Shuyi Wei, Kewang Li
Power Systems and Renewable Energy
article

Adaptive Particle Swarm Optimization Agorithm for Maximum Power Point Tracking of Vertical Sxis Wind Turbines

Xiuxia Zhang, Shuyi Wei, Kewang Li
article en

Abstract

This paper investigates the optimization problem of a vertical axis wind turbine (VAWT) energy system aiming to maximise the output power of a permanent magnet synchronous generator (PMSG) based system. To this end, a maximum power point tracking (MPPT) method based on rotational speed control is proposed and combined with an adaptive particle swarm optimization (APSO) algorithm to optimally regulate the rotational speed. Simulation results show that the wind turbine with this method can effectively operate at the maximum power point and achieve the speed regulation and output power optimization under wind speed variation.

North Minzu University (CN)
Natural Science Foundation of Ningxia Province
Affordable and clean energy
Openalex Percentile: Top 65%
Power Systems and Renewable Energy
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