Abstract
Large-scale solar photovoltaic (PV) plants play an essential role in providing the increasing demand for energy in recent time. Therefore, in the purpose of achieving the highest harvested power under the partial shading conditions as well as protecting the PV array from the hot-spot calamity, the PV reconfiguration strategy is established as an efficient procedure. This is performed by redistribution of PV modules according to their levels of shading. Motivated by this, the authors in this article have introduced a novel population-based algorithm that is known as marine predators algorithm (MPA) to restructure the PV array dynamically. Moreover, a novel objective function is introduced to enhance the algorithm performance rather than utilizing the regular weighted objective function in the literature. The effectiveness of the proposed algorithms based on the novel objective function is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the obtained results are compared with a regular total-cross-tied (TCT) connection, manta ray foraging optimization (MRFO), harris hawk optimizer (HHO) and particle swarm optimizer (PSO) based reconfiguration techniques. Furthermore, to demonstrate the suitability of the proposed methods, large scale PV arrays of $16\times16$ and $25\times25$ are considered and evaluated. The results reveal that MPA enhanced the PV array power by percentage of 28.6 %, 2.7 % and 5.7 % in cases of $9\times9$ , $16\times16$ and $25\times25$ PV arrays, respectively. The comprehensive comparisons endorse that MPA shows a successful shade dispersion; hence the number of multiple peaks in the PV characteristics has reduced, and high values of power have been harvested with least mean execution time in comparison with PSO, HHO and MRFO. Moreover, the Wilcoxon signed-rank test has been accomplished to confirm the reliability and applicability of the proposed approach for the PV large scale arrays as well.
Highlights
In recent years, research on extraction of maximum power from a photovoltaic (PV) system has been focused on dynamic change of irradiation and temperature conditions [1], [2]
SIMULATIONS AND RESULTS The framework of this section is divided into two stages; 1) The first one is a comparison among the results provided by merging the weighted objective function as well as the novel one with the proposed algorithms
It is presented to clarify the applicability of the proposed objective function and its impact on the algorithms
Summary
Research on extraction of maximum power from a photovoltaic (PV) system has been focused on dynamic change of irradiation and temperature conditions [1], [2]. The major drawback in TCT is that, the output current generated by the PV array is limited, when the maximum number of PV modules in row are shaded [20] With this motivation, the authors proposed various reconfiguration techniques, such as adaptive, static, and dynamic reconfiguration, to diffuse the shade over the entire PV array. The authors proposed various reconfiguration techniques, such as adaptive, static, and dynamic reconfiguration, to diffuse the shade over the entire PV array This enhances power generation and reduces the mismatch losses. An optimal fixed reconfiguration technique is proposed by reducing row spacing between arrays in [30] Another method based on an odd-even structure for TCT-configured systems is presented in [31].
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