ABSTRACT
Reconfiguration of PV array has emerged as a promising solution to improve the power under partial shading conditions (PSC). The three main reconfiguration techniques are the physical array relocation method, electrical array reconfiguration (EAR), and the switching matrix generation based on optimization. The physical relocation techniques are complicated as they require hard labor. In the case of EAR, the optimal design of the switching matrix is still challenging. Therefore, to overcome these issues, this paper proposes a recent metaheuristic technique of Adaptive-JAYA optimization for the optimum reconfiguration of the PV array. Adaptive-JAYA is chosen for its simplicity and reliability, which makes it consume less memory, reducing the burden on processors. The proposed approaches are applied on a 9 × 9 PV array under eight shading patterns and also on a 9 × 15 PV array to analyze the competency of the proposed approach on unsymmetrical shading. The MATLAB results are quantitatively analyzed, and a comprehensive comparison is performed with many existing reconfiguration techniques. It is proved that the proposed Adaptive-JAYA approach improves the output power by 26.12%, 22.20%, 7.61%, and 7.68% compared to the TCT for the four shading patterns, i.e. SW, LW, SN, and LN.
Nomenclature
= | Current in row and column | |
= | Irradiation in row and column | |
= | Maximum current in row and column | |
= | Node current | |
= | Terminal voltage | |
= | Terminal voltage of the module | |
= | Fitness function of particle at iteration | |
= | Best possible solutions | |
= | Worst possible solutions | |
and | = | Random numbers that are distributed in the range [0,1] |
and | = | Adaptive coefficients |
= | Total number of iterations | |
= | Current temperature coefficient | |
= | Thermal voltage | |
CS | = | center shading |
DS | = | diagonal shading |
= | Diode ideality factor | |
= | Total cross tied | |
HC | = | Honey comb |
SP | = | Series parallel |
PSO | = | particle swarm optimization |
HHO | = | Harris hawks optimization |
AEO | = | Artificial ecosystem optimization |
GWO | = | Gray wolf optimization |
and | = | Voltage and current in the row respectively |
= | Output power of the panel without considering the bypass diode | |
= | Difference between row current | |
= | Maximum current with bypass diodes | |
= | Maximum voltage | |
= | Maximum current | |
= | Open circuit voltage | |
= | Short circuit current | |
= | Maximum power | |
= | Average power | |
LW | = | Long wide |
SW | = | Short wide |
LN | = | Long narrow |
SN | = | Short narrow |
OS | = | outer shading |
RS | = | random shading |
PSC | = | Partial shading conditions |
GMP | = | Global maximum power |
BL | = | Bridge link |
EAR | = | Electrical array reconfiguration |
GA | = | Genetic algorithm |
BOA | = | Butterfly optimization algorithm |
GOA | = | Grasshopper optimization algorithm |
GSA | = | Gravitational search algorithm |
Acknowledgements
This work was supported by the Science and Engineering Research Board (SERB) under the Department of Science and Technology (DST) under Grant EEQ/2022/000189.
Disclosure statement
No potential conflict of interest was reported by the authors.