The Role of 3D Robotic Technology in Ensuring Power Quality in Underground Power Transmission Networks
DOI:
https://doi.org/10.52152/4290Keywords:
3D Robotic Technology, Underground Power Transmission, Power Quality Monitoring, BiLSTM-CNN Model, Chaotic Fennec Fox Optimization (CFFA)Abstract
The increasing demand for reliable underground power transmission networks necessitates innovative solutions to ensure power quality and operational efficiency. This study presents an advanced 3D robotic inspection system integrating a Bidirectional Long Short-Term Memory - Convolutional Neural Network (BiLSTM-CNN) model optimized by the Chaotic Fennec Fox Optimization Algorithm (CFFA) to enhance power quality monitoring and fault detection in underground power systems. The proposed CFFA-optimized model achieves a fault detection accuracy of 96.8%, outperforming Particle Swarm Optimization (92.5%), Genetic Algorithm (90.3%), and Whale Optimization Algorithm (93.7%). The robotic platform exhibits high maneuverability, capable of climbing 30° slopes and navigating complex terrains, supported by a six-wheel drive system and versatile communication modes. The system autonomously generates detailed inspection reports including defect classification, location, and severity, reducing human intervention. Overall, this integrated approach significantly improves fault detection accuracy, reduces maintenance downtime, and enhances the reliability and safety of underground power transmission networks.
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Copyright (c) 2025 Mingcong Xia, Shengfa Tang, Hao Li, Xing Xu, Bairen Chen, Linhui Guo, Chenchuan Liao, Jianing Zhu, Jinpei Lin (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
