Artificial Intelligence Applications in the Process of Power Cable Operation and Maintenance: A Review

Abstract

Power cables are the core power transmission carriers for modern urban power supply, and their safe and reliable operation is crucial to the stability of urban power grids. Traditional cable operation and maintenance (O&M) mainly relies on manual experience, suffers from prominent data silos, and struggles to meet the demands of complex power grids and high-reliability power supply. Data-driven artificial intelligence has been increasingly applied in cable O&M, serving as a complement to conventional operation frameworks. We focus on the O&M process oriented towards fault handling. Firstly, attention is paid to the core physical quantities for cable condition monitoring and the current application status of Internet of Things (IoT) sensing technology. Subsequently, the limitations of traditional fault diagnosis methods are systematically analyzed. Furthermore, we highlight the research progress of deep learning models such as attention mechanisms and convolutional neural networks (CNNs) in cable fault diagnosis. Digital twin (DT) technology is also addressed, and its synergistic effect with AI technology in the intelligent O&M framework is systematically analyzed. Finally, we summarize the challenges faced in current technology implementation and look forward to future development directions.