Efficient and adaptive design of RBF neural network for maximum energy harvesting from standalone PV system

IF 3.8 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-17 DOI:10.1016/j.suscom.2025.101083

Mohand Akli Kacimi , Celia Aoughlis , Toufik Bakir , Ouahib Guenounou

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引用次数: 0

Abstract

This paper deals with a topical topic, the maximum energy harvest of standalone PV system under varying conditions. It introduces a new approach based on the use of artificial intelligence and machine learning to overcome the usual weaknesses of conventional Maximum Power Point Tracking (MPPT) techniques and to improve solutions to meet growing energy demand and further promote sustainable development. The proposal consists of using Radial Basis Function Neural Network (RBFNN) tuned by a PSO algorithm as MPPT controller. The main aim of this combination (RBFNN-PSO) is to achieve the best compromise between the control accuracy and complexity, while using a simple optimization algorithm. This aim is motivated by the potential of the neural networks to learn from any tasks and to generalize the acquired knowledge to other situation never seen before. The proposal reaches a high efficiency and high energy harvesting with a yield greater than 99 %. The performed comparative study with other intelligent techniques from literature prove the superiority and the promising potential of the introduced approach. The developed work presented in this paper is developed with MatLab/Simulink environment.

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基于RBF神经网络的高效自适应光伏系统能量收集

本文研究了独立光伏系统在不同工况下的最大能量收获问题。它引入了一种基于使用人工智能和机器学习的新方法，以克服传统最大功率点跟踪（MPPT）技术的常见弱点，并改进解决方案，以满足不断增长的能源需求，进一步促进可持续发展。该方案采用粒子群算法调谐的径向基函数神经网络（RBFNN）作为MPPT控制器。这种组合（RBFNN-PSO）的主要目的是在使用简单的优化算法的同时实现控制精度和复杂性之间的最佳折衷。这一目标的动机是神经网络从任何任务中学习的潜力，并将获得的知识推广到以前从未见过的其他情况。该方案达到了高效率和高能量收集，收率大于99% %。通过与文献中其他智能技术的比较研究，证明了该方法的优越性和发展潜力。本文的开发工作是在MatLab/Simulink环境下进行的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Computing-Informatics & Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTUREC-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

10.70

自引率

4.40%

发文量

142

期刊介绍： Sustainable computing is a rapidly expanding research area spanning the fields of computer science and engineering, electrical engineering as well as other engineering disciplines. The aim of Sustainable Computing: Informatics and Systems (SUSCOM) is to publish the myriad research findings related to energy-aware and thermal-aware management of computing resource. Equally important is a spectrum of related research issues such as applications of computing that can have ecological and societal impacts. SUSCOM publishes original and timely research papers and survey articles in current areas of power, energy, temperature, and environment related research areas of current importance to readers. SUSCOM has an editorial board comprising prominent researchers from around the world and selects competitively evaluated peer-reviewed papers.