A Surge Impedance Loading(SIL)–Based Frame work for Transmission Line Monitoring and Predictive Fault Detection

Abstract

Reliable operation of modern transmission networks requires not only post fault detection but also predictive identification of in- stability before fault escalation. This paper presents a probabilistic SIL-based frame work for real-time transmission line monitoring and predictive fault detection using receiving-end phasor measurements. Unlike conventional transient-driven methods, the proposed Approach formulates an adaptive SIL stability boundary incorporating voltage deviation and reactive power imbalance and transforms SIL exceed ancient hazard-based probabilistic risk metric. Monte Carlo simulations (N = 200 per loading level) validated ex-potential instability growth beyond the adaptive threshold, yielding a calibrated risk constant, k = 0.1746, with strong regression agreement, R2 = 0.9793. Implementation on a 220kV,200km transmission corridor and dynamic validation on an IEEE9-bus multimachine system demonstrated non-linear escalation off aultprobabilitybeyond110%SIL.Theframeworkachieved94.7% detection accuracy across 570 test disturbance sandidenti 7edhigh-risk conditions up to 50m sear Lier Hanan adaptive wavelet transform benchmark, while maintaining ower computational complexity. A two-dimensional SIL–reactive compensation risk map further enables proactive operator actions such as dynamic VAR support and preventive load shedding. The results establish SIL deviation as a computationally eBcientearly-warning indicator for transmission-line instability, enabling predictive default risk assessment and scalable deployment in wide-are a monitoring, and modern grid protection systems.