Advanced Fault Detection, Classification, and Analysis Framework for HV Transmission Lines using RT Synchronized Monitoring and Control Systems

Abstract

The emergence of new technologies such as IoT, along with the merger of renewable energies, AI, smart grids, and non linear loads is enhancing the complexity of modern power systems and detecting fault as well as its correction much harder. Traditional methods suffer from inadequate speed, accuracy, less coverage, and latency that renders them highly ineffective in varying conditions. Reliable power transmission is vital for modern infrastructure, as faults on transmission lines can disrupt supply, damage equipment, and create safety risks. This paper presents a Fault Detection and Analysis System (FDAS) designed to enhance power system reliability and efficiency by enabling early fault detection, classification, and precise fault location. The FDAS system unites sensor inputs from voltage and current transformers alongside superior analysis methods for continuous fault surveillance. Proper detection of faults enables rapid identification of faulty sections thus minimizing the duration of outages and protecting equipment from damage. Fault classification defines fault categories which allows technicians to apply suitable solutions and accurate fault location enhances repair operations to decrease operational interruptions and maintenance expenses. Moreover, the FDAS system outperforms impedance-based and wave-based traditional methods through its combination of real-time acquisition and analytical algorithms and wireless monitoring which accelerates fault detection while enabling accurate corrective actions. Identifying specific fault types through fault classification provides suitable corrective solutions while exact location determination helps minimize repair time and spending related to maintenance costs.