Optimal control strategies-based maximum power point tracking for photovoltaic systems under variable environmental conditions

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS

International Journal of Modelling Identification and Control Pub Date : 2023-01-01 DOI:10.1504/ijmic.2023.128773

Sally Abdulaziz, Galal Atlam, Gomaa Zaki, Essam Nabil

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引用次数: 1

Abstract

To increase the efficiency of photovoltaic (PV) array output under variable environmental conditions, maximum power point tracking (MPPT) of the solar arrays is needed. This paper proposes fuzzy logic controller (FLC)-based MPPT, artificial neural network (ANN)-based MPPT, neuro-fuzzy (NF)-based MPPT, particle swarm optimisation (PSO)-based MPPT, and cuckoo search (CS) algorithm-based MPPT to combine an adaptive controller and an optimisation, to guarantee global stability and a constant settling time for all operation conditions. This combination enables an increase in the power generated in comparison with conventional MPPT techniques. Simulation results show that the proposed photovoltaic/storage generator is able to supply the suggested dynamic loads under different conditions, and achieve good performance. It is also noticed that operating the photovoltaic array based on maximum power point tracking conditions gives about 43% extra power generation than in the case of normal operation.

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基于最优控制策略的变环境下光伏系统最大功率点跟踪

为了提高多变环境条件下光伏阵列的输出效率，需要对太阳能电池阵列进行最大功率点跟踪。本文提出了基于模糊逻辑控制器(FLC)的MPPT、基于人工神经网络(ANN)的MPPT、基于神经模糊(NF)的MPPT、基于粒子群优化(PSO)的MPPT和基于布谷鸟搜索(CS)算法的MPPT，将自适应控制器和优化相结合，保证了所有运行条件下的全局稳定性和恒定的沉降时间。与传统的MPPT技术相比，这种组合可以增加产生的功率。仿真结果表明，所设计的光伏/储能发电机能够在不同工况下提供所建议的动态负荷，并取得良好的性能。还可以注意到，在最大功率点跟踪条件下运行光伏阵列，比正常运行时多出约43%的发电量。

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

International Journal of Modelling Identification and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.70

自引率

57.10%

发文量

期刊介绍： Most of the research and experiments in the fields of science, engineering, and social studies have spent significant efforts to find rules from various complicated phenomena by observations, recorded data, logic derivations, and so on. The rules are normally summarised as concise and quantitative expressions or “models". “Identification" provides mechanisms to establish the models and “control" provides mechanisms to improve the system (represented by its model) performance. IJMIC is set up to reflect the relevant generic studies in this area.