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

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%.