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首页> 外文期刊>International Journal of Power Electronics and Drive Systems >Adaptive Neuro-Fuzzy Inference System-based Improvement of Perturb and Observe Maximum Power Point Tracking Method for Photovoltaic Systems
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Adaptive Neuro-Fuzzy Inference System-based Improvement of Perturb and Observe Maximum Power Point Tracking Method for Photovoltaic Systems

机译：基于自适应神经模糊推理系统的扰动改进并遵守光伏系统最大功率点跟踪方法

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摘要

This paper presents a maximum power point (MPP) tracking method based on a hybrid combination between the fuzzy logic controller (FLC) and the conventional Perturb-and-Observe (P&O) method. The proposed algorithm utilizes the FLC to initialize P&O algorithm with an initial duty cycle. MATLAB/Simulink models consisting of, the photovoltaic system, boost converter and controllers, are built to evaluate the performance of the proposed algorithm. To accurately illustrate the performance of the proposed algorithm, comparisons with standalone FLC and P&O are carried out. The performance of the proposed algorithm is investigated difficult weather conditions including sudden changes and partial shading. The results showed that the proposed algorithm successfully reaches MPP in all scenarios.
机译：本文提出了一种基于模糊逻辑控制器（FLC）与常规扰动和观察（P＆O）方法的混合组合的最大功率点（MPP）跟踪方法。所提出的算法利用FLC初始化具有初始占空比的P＆O算法。建立了由光伏系统，升压转换器和控制器组成的MATLAB / Simulink模型，以评估所提出算法的性能。为了准确地说明所提出算法的性能，与独立的FLC和P＆O进行了比较。提出的算法的性能研究了恶劣天气条件下的突然变化和局部阴影。结果表明，该算法在所有场景下均成功达到了MPP。

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