Digital Twin Technology on Noise Reduction of Power Communication System with Deep Learning Algorithm

By applying digital twin technology in digital operations Power communication maintenance, a real-time interaction model between the physical world and the digital world can be created to achieve real-time monitoring of communication network status, predictive analysis of faults, and decision support. The power system is evolving rapidly, making the stability of the communication network vital for its safe operation. In this paper, we utilize digital twin technology combined with a deep learning de-noising algorithm to handle the noise problem in power inspection images. We assess the results by comparing noiseless and denoised images using PSNR (ranging from 20 to 30), SSIM (close to 1), and RMSE (with a difference of 10). The findings show that the de-noising method can maintain image quality well. Yet, there's room for improving accuracy. In summary, this research effectively solves the image processing noise reduction issue, enhancing both the operation and maintenance efficiency and the communication network's performance, thus contributing to the smooth running of the power system.