A multivariate reconfiguration method for rooftop PV array based on improved northern goshawk optimization algorithm

• Published in: Physica Scripta
• Main Authors: Yi, Lingzhi, Cheng, Siyue, Wang, Yahui, Hu, Yao, Ma, Hao, Luo, Bote
• Other Authors: Natural Science Zhuzhou United Foundation, National Natural Science Foundation of China
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2024
• Subjects: Northern Goshawk
• Online Access: http://dx.doi.org/10.1088/1402-4896/ad2a2b; https://iopscience.iop.org/article/10.1088/1402-4896/ad2a2b; https://iopscience.iop.org/article/10.1088/1402-4896/ad2a2b/pdf

Abstract Photovoltaic (PV) power has become a crucial solution to the escalating energy crisis. Among the various implementations, Rooftop PV power generation systems (RPVPGS) are predominant in PV buildings. However, RPVPGS will face challenges such as reduced output power due to array fault or shading, leading to fluctuations in Building-Integrated PV (BIPV) power generation. This paper attempts to solve this problem by proposing a novel multivariate reconfiguration method based on the improved northern goshawk optimization algorithm (INGO). The aim is to find the optimal state of RPVPGS under various conditions. In this paper, extensive simulations were conducted on the experimental platform to assess the feasibility and effectiveness of the proposed method. It is worth noting that INGO outperforms existing technologies such as Arrow SoDuku and Zig-zag for the evaluation metrics mentioned in the article. Furthermore, rigorous simulation experiments were conducted on the semi-physical platform to validate the proposed approach. The power enhancement percentage deviation was between +0.1% to +0.2%. These results unequivocally demonstrate that the INGO-based multivariate reconfiguration method accurately reconfigures RPVPGS, ensuring the efficiency and stability of BIPV systems.