IoT and Edge Computing Integration for Intelligent Fault Diagnosis and Self-Healing in 132 kV Transmission Networks
Inyene U. Robert *
Electrical and Electronics Engineering Department, University of Uyo, Nigeria.
Nseobong I. Okpura
Electrical and Electronics Engineering Department, University of Uyo, Nigeria.
Kufre M. Udofia
Electrical and Electronics Engineering Department, University of Uyo, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Traditional SCADA and relay-based protection, with typical latencies of 2–10 seconds, are inadequate for the resilience required in modern 132kV transmission networks. This paper reviews the integration of Internet of Things (IoT) sensor fabrics, including Phasor Measurement Units (PMUs) and distributed sensors, with a hierarchical Edge Computing infrastructure to enable autonomous fault diagnosis and self-healing. The authors analysed the deployment of computational intelligence across device, substation, and fog layers, emphasising how local processing mitigates cloud latency. The review examined optimised AI/ML models (such as wavelet-based Support Vector Machines and pruned 1D-CNNs) for real-time fault detection, classification, and location at the network edge. Furthermore, the study explored the role of IEC 61850 GOOSE protocols, with < 4ms latency, in enabling closed-loop actuation for autonomous isolation. This synthesis demonstrates a viable architecture for sub-second self-healing. This paper's primary contribution is its holistic synthesis of these technologies into a single, cohesive framework, highlighting critical research challenges in cybersecurity, interoperability, and data integrity that must be addressed for industrial applications.
Keywords: Convolutional neural networks (CNN), edge intelligence, fault detection isolation and restoration (FDIR), phasor measurement unit (PMU), SCADA, wavelet transform