Advance Maximum Power Point Tracking Scheme Based on Solar Panel Reconfiguration and Pollination Algorithms

One of the major challenges associated with solar photovoltaic (PV) power harnessing is the intermittent nature of its output. The situation worsens in partial shading as it leads to greater mismatch losses and reduced efficiency of PV modules. Consequently, this work proposes two novel algorithms designed to overcome the impacts of various patterns and shading levels over PV panels. One algorithm is designed on a puzzle-based reconfiguration (PBR) scheme that suggests the physical reconfiguration of PV modules in an array. Governed by mathematical relations, PBR effectively distributes the impact of shading as a function of reconfiguration of modules within the array. Comparative analysis of the results of PBR has been carried out with existing alternative configurations. Numerous performance parameters such as global maximum power, fill factor, and mismatch losses have been evaluated for different shading patterns. It is found that proposed PBR algorithm results in greater values of maximum power and fill factor with lowest mismatch losses among all configurations for any level and pattern of shading. Similarly, inspired by the flower pollination (FP) technique, a second algorithm is proposed for enhanced tracking speed with reduced oscillations under various levels of shading. The tracking speed of proposed FP algorithm is found to be higher than most preferred PSO approach and better results are obtained with the increase in shading level.

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Publication Details

Journal
Applied Solar Energy
Published
2024-12-01
DOI
https://doi.org/10.3103/s0003701x24603089
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
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article

Advance Maximum Power Point Tracking Scheme Based on Solar Panel Reconfiguration and Pollination Algorithms

Alivarani Mohapatra, Ranjeeta Patel, Md Ehtesham, Khadija Sajda Khanam
Applied Solar Energy
Photovoltaic System Optimization Techniques
article

Advance Maximum Power Point Tracking Scheme Based on Solar Panel Reconfiguration and Pollination Algorithms

Alivarani Mohapatra, Ranjeeta Patel, Md Ehtesham, Khadija Sajda Khanam
article en

Abstract

One of the major challenges associated with solar photovoltaic (PV) power harnessing is the intermittent nature of its output. The situation worsens in partial shading as it leads to greater mismatch losses and reduced efficiency of PV modules. Consequently, this work proposes two novel algorithms designed to overcome the impacts of various patterns and shading levels over PV panels. One algorithm is designed on a puzzle-based reconfiguration (PBR) scheme that suggests the physical reconfiguration of PV modules in an array. Governed by mathematical relations, PBR effectively distributes the impact of shading as a function of reconfiguration of modules within the array. Comparative analysis of the results of PBR has been carried out with existing alternative configurations. Numerous performance parameters such as global maximum power, fill factor, and mismatch losses have been evaluated for different shading patterns. It is found that proposed PBR algorithm results in greater values of maximum power and fill factor with lowest mismatch losses among all configurations for any level and pattern of shading. Similarly, inspired by the flower pollination (FP) technique, a second algorithm is proposed for enhanced tracking speed with reduced oscillations under various levels of shading. The tracking speed of proposed FP algorithm is found to be higher than most preferred PSO approach and better results are obtained with the increase in shading level.

Applied Solar EnergyVol. 60(6)
Maulana Azad National Urdu University (IN), KIIT University (IN)
Affordable and clean energy
Openalex Percentile: Top 83%
Photovoltaic System Optimization Techniques
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Advance Maximum Power Point Tracking Scheme Based on Solar Panel Reconfiguration and Pollination Algorithms — Alivarani Mohapatra, Ranjeeta Patel, et al. · Applied Solar Energy (2024) | TGRS Research Map | TGRS