Air Pressure Prediction Model Based on Feature Fusion of Laser Induced Plasma

The vacuum switch uses vacuum as the insulation and arc-extinguishing medium. It is the core equipment in high-voltage transmission and distribution power systems. Online detection of vacuum level for vacuum switch is an international challenge that has not been effectively addressed for over seventy years. In the previous work, our team first proposed an online detection method of vacuum level for vacuum switches based on laser-induced plasma (LIP), which holds the potential for achieving safe and reliable vacuum level detection. However, the accuracy of the vacuum level detection based on a single variable is low and difficult to meet the requirements of high-voltage transmission power systems. To address this issue, this paper proposes a vacuum level prediction model, this model fuses plasma images and spectral images to predict air pressure. Experimental results reveal that the fusion of plasma images or spectral images improves the Macro-Precision (MacP) of air pressure prediction, achieving an accuracy of 95.42%. In comparison to utilizing only plasma images or spectral images for air pressure prediction, the MacP increases by approximately 5.5% and 8%, respectively. These findings underscore the effectiveness of the proposed air pressure prediction model that fuses image and spectra. This paper establishes a foundation for the implementation of the online detection of vacuum level based on laser-induced plasma in vacuum switches.