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Due to the increasing load of the power grid and the rapid expansion of cable line construction, monitoring cable insulation performance has become very important. To address this challenge, this paper proposes an online monitoring system for cable insulation in distribution networks based on line conduction characteristics. The system combines advanced signal acquisition technology, data processing methods, and deep learning (DL) algorithms to enable real-time monitoring and accurate evaluation of cable insulation status. The results demonstrate that the system excels in real-time monitoring of cable insulation status. Compared to traditional methods, the system proposed in this paper shows significant advantages in key indicators such as missed detection rate, fault location accuracy, recognition speed, and recall rate. Specifically, the system effectively reduces the missed detection rate, improves the accuracy of fault location, accelerates identification speed, and enhances the recall rate, enabling more comprehensive detection of insulation faults.
Li et al. (2024) studied this question.
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