Prediction and Assessment Algorithm for Service Life of Fire-Safe Operation of Electrical Wiring and Cable Lines

Abstract

The study focuses on the emergence of ignition sources in low-voltage cable lines under uncontrolled neutral overload caused by higher-harmonic currents and asymmetry. The goal of the study is to develop a quantitative assessment methodology to estimate the probability of local inter-conductor insulation breakdown, accounting for its current technical condition and thermal aging dynamics. The study is based on the synthesis of deterministic thermophysical analysis and stochastic modeling. A Markov model of four stages of insulation reliability is proposed, where the Arrhenius equation determines the transition intensities. The instantaneous risk of ignition source emergence is calculated based on the Weibull distribution. A concept of a dynamic thermal strength threshold has been proposed, which is not constant but decreases as a function of the insulation's thermal degradation. A stochastic algorithm that helps transform electrical mode parameters into an integral index of the probability of ignition source generation, considering the thermal history of a particular line, has been developed. Through a numerical experiment, it has been proven that the ignition source generation is physically impossible for new electrical wiring and that it becomes inevitable under a similar thermal impact on the insulation that has lost its properties. Under an identical load in the neutral, a new cable line maintains its dielectric integrity, whereas worn insulation reaches a critical level of fire hazard within the first 50 minutes of impact. Notably, traditional phase-circuit protection means are functionally incapable of detecting this emergency mode. The presented mathematical framework allows for quantitative substantiation of ignition source generation under overloads, demonstrating the shift of critical threshold temperatures into the operating range as insulation ages. The proposed method enables predictive protection of electrical grids by timely detection of critical proximity between the working temperature and the dynamic dielectric strength threshold, preventing inter-conductor faults. The algorithm can be integrated into electric installation intellectual monitoring systems for predictive prevention of fires in grids with a high proportion of nonlinear loads.