ArticleScientific reports2025
Simulation and analysis of magnetic fields around High-Voltage power lines using Python for enhanced safety and design insights.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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