Real‐Time Update Algorithm of Optical Fiber Distribution Network Equipment Based on Optical Imaging Technology

Abstract

With the increasing digitalization of distribution network equipment (DNE), real‐time update algorithms for digital twin (DT) models have become a research focus on the digitalization of DNE. However, traditional real‐time update algorithms for DT models still have problems such as poor real‐time, accuracy, robustness, and scalability. To better promote the development of digitalization of DNE, this article aimed to study the real‐time update algorithm of DT models using the Internet of Things (IoT) and optical imaging technology, to achieve real‐time updates of DT models of DNE. The article first described the problems existing in the traditional DT model of DNE. Then, IoT sensors and optical devices were used to collect data related to DNE; the Savitzky–Golay filtering algorithm was used to denoise the data. This article combined the IoT and optical imaging technology to construct a DT model; 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 article 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 article 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 were significantly better than the other two traditional methods. The results showed that the real‐time update algorithm of the DT model of DNE based on the IoT and optical imaging technology had 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.