Simulation and analysis of magnetic fields around High-Voltage power lines using Python for enhanced safety and design insights

Accurate modeling of magnetic fields around high-voltage power lines is essential for public health protection, electromagnetic compatibility (EMC) planning, and infrastructure safety. This study presents a novel, open-source, Python-based simulation framework that rigorously computes magnetic flux density using the Biot-Savart Law, enhanced with ground-air boundary conditions via a modified finite element module. Simulations were conducted for three typical conductor configurations, horizontal, vertical, and triangular (delta) under balanced three-phase loading (132 kV, 100 A per phase), using Aluminium Conductor Steel-Reinforced (ACSR) 'Linnet' conductors mounted 10 m above ground level. The horizontal configuration exhibited the highest peak magnetic flux density, reaching 120 µT directly beneath the conductors and 104.2 µT at 1.5 m height, exceeding the ICNIRP (2020) public exposure limit of 100 µT. In contrast, the triangular layout produced the most uniform field distribution, with a peak of 57.6 µT and a standard deviation of 7.3 µT across the 0-2 m human exposure zone. The vertical arrangement, while exhibiting lower peak intensity, influenced a broader lateral dispersion, indicating potential implications for densely populated environments. Incorporation of ground-air interactions resulted in a 28.3% increase in local field intensity at 1.5 m due to constructive interference, necessitating up to 1.2 m reduction in safety clearance in worst-case exposure scenarios. Field measurements using a precision three-axis gaussmeter (± 0.01 µT) at 5 m, 10 m, and 15 m from the transmission line showed a maximum relative deviation of 0.74%, with absolute error ranging from 6.78 × 10⁻²¹ T to 1.53 × 10⁻⁷ T, validating the model's predictive fidelity. Incorporating boundary effects reduced spatial prediction error by 15-25% compared to boundary-excluded models. The simulation framework, developed using NumPy, SciPy, and Matplotlib, provides a cost-effective, scalable, and regulator-aligned tool for optimizing conductor layouts, mitigating electromagnetic exposure risks, and supporting compliance in transmission routing and urban planning. Future work will integrate conductor non-idealities, dynamic environmental loading, and transient power flow conditions to enhance applicability in smart grid and real-time EMF monitoring scenarios.