Power cable monitoring method based on UHF‐RFID and deep learning in edge computing environment

This research addresses the challenge faced by most existing prediction methods in handling nonlinear data of cables. Furthermore, it proposes a novel power cable monitoring method utilizing UHF‐RFID and deep learning within an edge computing environment, specifically targeting the currently suboptimal wireless monitoring of cables. First, based on edge computing, a power cable monitoring system is designed to migrate the analysis of massive data to the edge of the network to improve the monitoring efficiency. Then, the temperature sensing chip and RFID chip were integrated to design a UHF‐RFID temperature tag, which was fixed at the cable temperature measurement point to achieve passive wireless monitoring of the cable. Finally, the parameters of the GRNN model are optimized using the beetle antennae search algorithm, and the EEMD decomposed data is input into the BAS‐GRNN model for learning to output temperature prediction results. Based on the establishment of an experimental platform, the method was demonstrated, and results showed that the maximum error between the UHF‐RFID temperature tag temperature measurement results and the thermocouple was within 0.3°C, and the average relative error of the proposed method was only 0.01, which can meet the accuracy requirements of actual monitoring of power cables.