Computational Modeling of Tetralayer Graphene/h‐BN/MoS ₂ /Phosphorene Heterostructured Sensors for Real‐Time Fault Detection and Predictive Maintenance of High‐Voltage Power Transmission Lines

A multi‐physics computational investigation of a transparent tetralayer van der Waals heterostructured sensor composed of graphene, hexagonal boron nitride (h‐BN), molybdenum disulfide (MoS 2 ), and phosphorene is presented, designed for comprehensive real‐time monitoring of high‐voltage power transmission infrastructures. The proposed architecture is examined entirely through first‐principles (DFT) and finite‐element (FEA) simulations coupled with electromagnetic modeling, providing quantitative insight into mechanical, thermal, electrical, and electromagnetic responses under realistic field conditions (−40 to +80°C, 20–95% RH). The optimized h‐BN interlayer ensures superior dielectric isolation (< 0.1% crosstalk) and efficient thermal conduction (κ hBN = 600 W m −1 K −1 ), enabling stable coupling among active layers. Parametric analysis yields an enhanced gauge factor (GF eff = 125) for graphene‐based strain sensing, a field‐enhancement constant (ξ hBN = 3.2) for MoS 2 partial‐discharge detection, and directional EMI sensitivity governed by the anisotropic conductivity ratio (σ ∥ /σ ⊥ = 150) of phosphorene. Virtual field‐deployment simulations using actual transmission‐line stress profiles indicate fault prediction up to 120 h earlier than benchmark technologies, with modeled accuracy exceeding 99.7%. The system achieves multi‐modal reliability validated through automated signal‐to‐noise analysis (78, 62, and 54 dB for graphene, MoS 2 , and phosphorene, respectively). By integrating complementary material physics within a unified modeling framework, this study establishes a computational baseline for next‐generation van der Waals heterostructured sensors capable of predictive maintenance and autonomous fault diagnostics in smart‐grid environments. All findings derive solely from validated simulations, highlighting the feasibility and scalability of the computational design prior to experimental realization.