Real-time update algorithms for digital twin models of distribution network equipment under internet of things and optical imaging technology.

In order to achieve more efficient, accurate and intelligent substation equipment management and overall work efficiency of the substation, improve the work quality of the substation, innovate the data transmission mode and basic algorithm of the distribution network, and improve the traditional shortcomings and defects. With the increasing digitalization of distribution network equipment (DNE), real-time update algorithms for digital twin (DT) models have become a focus of research on digitalization of DNE. However, traditional real-time update algorithms for DT models still have problems such as poor real-time and accuracy, robustness, and scalability. The article first described the problems existing in the traditional DT model of DNE. Then it used IoT sensors and optical devices to collect data related to DNE; then it used the Savitzky-Golay filtering algorithm to denoise the data. This article combined the IoT and optical imaging technology to construct a DT model; by using the recursive least squares method again, key parameters and state parameters were extracted from the constructed DT mechanism model, achieving real-time updates of the DNE DT model. Finally, to verify the application effect of the IoT and optical imaging technology in real-time update algorithms for DT models of DNE, this paper compared them with traditional parameter sensitivity analysis and state estimation. The research results showed that in the real-time and accuracy testing of test case 13, the algorithm used in this paper had a time of 0.014 s and an accuracy of 93.2%. The parameter sensitivity analysis method had a time of 0.045 s and an accuracy of 80.4%. The state estimation method took 0.056 s and had an accuracy of 82.7%. In addition, the robustness and scalability of the real-time update algorithm for the DNE DT model using the method proposed in this article are significantly better than the other two traditional methods. The results show that the real-time update algorithm of the DT model of DNE based on the IoT and optical imaging technology has better real-time performance, higher accuracy, and better robustness and scalability. This study highlights the significant impact of the IoT and optical imaging technology on the accuracy, robustness, and real-time performance of real-time update algorithms for DT models. This provides more solutions for real-time monitoring, prediction, and control of DNE.