An Integrated IoT-Edge Computing Framework for Advanced Fault Diagnosis and Self-Healing in 132 kV Transmission Networks

Abstract

The increasing complexity of modern power systems and the growing penetration of renewable energy necessitate autonomous, cyber-resilient fault management to minimise outage durations. Traditional fault detection methods and centralised cloud-centric architectures suffer from high latency, communication bottlenecks, and significant cybersecurity vulnerabilities. To address these challenges, this paper proposes an integrated fault diagnosis and self-healing framework for 132 kV transmission networks utilising Internet of Things (IoT) sensors, edge computing, artificial intelligence (AI), and lightweight cybersecurity protocols. The methodology employs a discrete wavelet packet transform (DWPT) for feature extraction, paired with an 8-bit integer-quantized artificial neural network (ANN) deployed on edge devices for rapid fault classification. A low-latency fault location, isolation, and service restoration (FLISR) mechanism is orchestrated at the network edge using a mixed-integer linear programming (MILP) solver accelerated by McCormick envelopes. Furthermore, a co-optimised cybersecurity layer incorporating the Elliptic Curve Integrated Encryption Scheme (ECIES), Hash-based Message Authentication Code (HMAC), and a random forest intrusion detection system (IDS) ensures data integrity. Experimental validation on a modified IEEE 34-node test system demonstrates that the proposed secure edge framework achieves 97.3% fault classification accuracy and a total fault-to-restoration time of 198 ms, representing a 78% improvement over cloud-based architectures. The integrated security layer successfully detects 94% of false data injection attacks and 91% of replay attacks with a minimal latency overhead of 26 ms, demonstrating that robust cyber-physical protection can be achieved without compromising real-time power system protection.