A Novel Maximum Power Point Inference Method for Distributed Marine Photovoltaic Monitoring

Yujie Chen, Jianan Wang, Lele Peng () and Jiachen Qiao
Additional contact information
Yujie Chen: College of Mechanical Engineering, Donghua University, Shanghai 201620, China
Jianan Wang: College of Mechanical Engineering, Donghua University, Shanghai 201620, China
Lele Peng: College of Mechanical Engineering, Donghua University, Shanghai 201620, China
Jiachen Qiao: School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China

Energies, 2025, vol. 18, issue 11, 1-23

Abstract: In actual operation, the output power of distributed marine photovoltaic monitoring faces challenges from wind, waves, and other dynamic motion factors. To address these challenges, this paper proposes a novel maximum power point inference method for distributed marine photovoltaic monitoring. First, a digital fusion model has been constructed to obtain a comprehensive dataset of the distributed marine photovoltaic monitoring system. Second, Multilayer Convolutional Neural Networks (CNN) are constructed to extract the local high-frequency motion characteristics, Squeeze and Excitation Attention (SE-Attention) is employed to capture the global low-frequency motion characteristics, and Long Short-Term Memory (LSTM) is utilized to perform temporal modeling of the motion characteristics. Subsequently, the Crested Porcupine Optimizer (CPO) algorithm is used to achieve high-precision recognition of the maximum power point in distributed marine photovoltaic monitoring. Finally, the effectiveness of the method is verified through experiments and simulations. The results indicate that the maximum power point of distributed marine photovoltaic monitoring exhibits multi-spectral motion characteristics, with the highest frequency at 335.2 Hz and the lowest frequency at 12.9 Hz. The proposed method enables efficient inference of the maximum power point for distributed marine photovoltaic monitoring under motion conditions, with an accuracy of 98.63%.

Keywords: distributed marine photovoltaic monitoring; maximum power point inference; Multilayer Convolutional Neural Networks; Squeeze and Excitation Attention; long short-term memory; Crested Porcupine Optimizer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/11/2760/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/11/2760/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:11:p:2760-:d:1664689

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().