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Open Access

A Simple Primary Key Algorithm Based Shade Dispersion Method for Maximizing PV Power Generation under Partial Shading Conditions

Prabhakaran Koothu Kesavan1( )A. Karthikeyan2Manoj Kumar2Sushant Mandal2
Renewable Energy Laboratory, Prince Sultan University, Riyadh 11586, Saudi Arabia
Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Mangalore 575025, India
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Abstract

The output power generation of a photovoltaic (PV) array reduces under partial shading, resulting in multiple local maxima in the PV characteristics and inaccurate tracking of the global maximum power point (GMPP). Various interconnection schemes are available to reduce power losses under partial shading. In this study, a primary key algorithm is proposed for distributing shading across an array. This method is suitable for any n×n PV array configuration and involves fewer calculations and variables, leading to reduced computational complexity. The power generations of a 9×9 PV array under four different shading conditions were compared with the configurations of: total cross-tied (TCT) and Su Du Ku, physical relocation and fixed column position of modules with fixed electrical connection (PRFCPM-FEC), and magic square (MS) and improved-odd-even-prime (IOEP). The advantage of the proposed method is that once the primary key elements are obtained, the remaining array elements are numbered in a simpler manner. The results obtained using the proposed arrangement show that the power is enhanced with reference to the TCT and is comparable to the Su Do Ku, PRFCPM-FEC, MS, and IOEP reconfigurations.

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Chinese Journal of Electrical Engineering
Pages 93-102

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Cite this article:
Koothu Kesavan P, Karthikeyan A, Kumar M, et al. A Simple Primary Key Algorithm Based Shade Dispersion Method for Maximizing PV Power Generation under Partial Shading Conditions. Chinese Journal of Electrical Engineering, 2024, 10(2): 93-102. https://doi.org/10.23919/CJEE.2024.000067

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Received: 10 October 2023
Revised: 12 November 2023
Accepted: 20 November 2023
Published: 30 June 2024
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