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基于元启发式技术(粒子群优化算法和遗传算法)的最大功率点跟踪(MPPT)算法,致力于提高风能抽水系统的性能。

MPPT algorithm based on metaheuristic techniques (PSO & GA) dedicated to improve wind energy water pumping system performance.

作者信息

Bouchakour Abdelhak, Zarour Laid, Bessous Noureddine, Bechouat Mohcene, Borni Abdelhalim, Zaghba Layachi, Rabehi Abdelaziz, Alwabli Abdullah, El-Abd Mohammed, Ghoneim Sherif S M

机构信息

Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria.

Electrotechnical Laboratory Faculty of Technical Sciences, University of Constantine 1, Constantine, Algeria.

出版信息

Sci Rep. 2024 Aug 2;14(1):17891. doi: 10.1038/s41598-024-68584-4.

Abstract

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work.

摘要

本文对最近用于改进风力发电系统(WECS)以应用于抽水系统的四种技术进行了比较研究。WECS是一种近年来发展迅速的可再生能源。与使用电网供电相比,在抽水领域使用WECS在经济上是一种免费的解决方案。配备永磁同步发电机的WECS的控制目标是精心实现发电最大化。本文介绍了使用遗传算法和粒子群优化算法进行优化的模糊逻辑控制与传统的扰动观察最大功率点跟踪(MPPT)方法在Matlab/Simulink环境下的比较研究。所提系统的性能已根据产生的输出电压、电流和功率波形、中间电路电压波形以及发电机速度进行了验证。给出的结果证明了本工作中应用的控制策略的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/204d/11297299/dcb8bfcde5ea/41598_2024_68584_Fig1_HTML.jpg

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