Edge-Computing-Based IoT Architecture and Autonomous Algorithm for Oilfield Power Lines

Abstract

As the energy artery of oilfield production, the stable and efficient operation of oilfield power transmission lines is crucial. The traditional manual inspection and isolated monitoring modes have prominent problems such as poor real-time performance, weak early warning capability, and high operation and maintenance costs. This paper proposes an intelligent monitoring system architecture for oilfield power transmission lines that deeply integrates the Internet of Things (IoT), edge computing, and artificial intelligence technologies. Adopting the design concept of "cloud-edge-end" collaboration, the system constructs a three-layer architecture comprising an intelligent sensor layer, an edge computing layer, and a cloud platform layer. This paper elaborates on the system's network topology, IPv6-based hierarchical address allocation strategy, key device configuration parameters, and secure access control mechanism. Its core contribution lies in proposing a hybrid algorithm for line fault diagnosis (HDA-LFD), with independent intellectual property rights which integrates improved wavelet packet transform and a lightweight convolutional neural network (CNN). Deployed at edge nodes, this algorithm achieves accurate identification and early warning of partial discharge, temperature anomalies, and mechanical faults in power transmission lines. Experimental verification shows that in a simulated oilfield environment, the system achieves a fault recognition accuracy of up to 98.7% with an average early warning time of 45 minutes, significantly enhancing the condition perception capability and intelligent operation and maintenance level of power transmission lines. This research holds important theoretical value and engineering significance for ensuring the safe production of oilfields.