Minimizing Power Loss of Hybrid Quadra Tied Solar PV Arrays Using Cuckoo Search MPPT Algorithm During Shading Scenarios

Abhinav Bhattacharjee;Suresh Mikkili
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Abstract

This study assists in selecting the appropriate solar photovoltaic (SPV) array configuration and metaheuristic maximum power point tracking (MPPT) technique to minimise power loss in rooftop SPV systems resulting from partial shading conditions (PSCs) caused by tall adjacent buildings in urban environments. A hybrid SPV array configuration, termed alternate - quadratied - cross tied (A-QT-CT), that integrates quadra tied (QT) and total cross tied (TCT) configurations is proposed. This configuration is designed to provide maximum power extraction comparable to the best performing TCT configuration, while incorporating a reduced number of cross-links. Simulations were conducted utilising MATLAB to evaluate the performance of these configurations in the context of typical PSCs found in urban environments. This evaluation includes a comparative analysis with the established TCT, series-parallel (SP), bridge linked - TCT (BL-TCT), and SP-TCT configurations. The proposed configurations are integrated with Perturb & Observe (P&O), Cuckoo Search (CS), and Particle Swarm Optimisation (PSO) MPPT techniques. These algorithms were evaluated under PSCs using MAT LAB simulations, as well as a hardware model implemented with the Texas Instruments TMS320F28379D microcontroller. The time required to track the MPP and the steady-state MPPT efficiency are assessed. The combination of the CS MPPT method with A-QT-CT and TCT configurations has been identified as the optimal solution for minimising power loss in this application.
在遮阳场景下,使用杜鹃搜索MPPT算法最小化混合四边形太阳能光伏阵列的功率损失
本研究有助于选择合适的太阳能光伏(SPV)阵列配置和元启发式最大功率点跟踪(MPPT)技术,以最大限度地减少城市环境中相邻高层建筑造成的部分遮阳条件(PSCs)导致的屋顶SPV系统的功率损失。提出了一种混合SPV阵列结构,称为交替-二次-交叉捆绑(A-QT- ct),它集成了二次捆绑(QT)和总交叉捆绑(TCT)结构。这种配置可以提供与最佳TCT配置相媲美的最大功率提取,同时减少了交联的数量。利用MATLAB进行了模拟,以评估这些配置在城市环境中发现的典型psc的性能。该评估包括与现有TCT、串并联(SP)、桥接-TCT (BL-TCT)和SP-TCT配置的比较分析。所提出的配置集成了扰动与观察(P&O),布谷鸟搜索(CS)和粒子群优化(PSO) MPPT技术。这些算法在psc下使用MAT LAB模拟进行了评估,并使用德州仪器TMS320F28379D微控制器实现了硬件模型。评估了跟踪MPP所需的时间和稳态MPPT效率。CS MPPT方法与A-QT-CT和TCT配置的结合已被确定为在该应用中最小化功率损耗的最佳解决方案。
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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CiteScore
8.80
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