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

Scientific reports · 2025 · Mixed · Insufficient evidence

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MixedInsufficient evidencePaper 5879

Study snapshot

PublicationScientific reports · 2025

Study typeengineering

Reported directionMixed

Evidence ratingInsufficient

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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AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeengineering

Effect directionmixed

PopulationNot supplied

Sample sizeNot supplied

ExposureELF

Evidence strengthInsufficientConfidence: 78% · Peer reviewed: yes

Main findings

A Python-based simulation framework (Biot–Savart with modified finite element ground-air boundary conditions) modeled magnetic flux density for three conductor configurations (horizontal, vertical, triangular) under balanced three-phase loading (132 kV, 100 A per phase) at 10 m height. The horizontal configuration produced the highest peak field (120 µT directly beneath; 104.2 µT at 1.5 m), exceeding the ICNIRP (2020) public limit of 100 µT, while the triangular layout had a lower peak (57.6 µT) and more uniform distribution across 0–2 m. Ground-air interactions increased local intensity at 1.5 m by 28.3% and boundary effects reduced spatial prediction error by 15–25%; measurements at 5/10/15 m showed maximum relative deviation 0.74% supporting model validity.

Outcomes measured

Magnetic flux density (µT) around high-voltage transmission lines
Model validation accuracy vs field measurements
Comparison to ICNIRP (2020) public exposure limit (100 µT)

View raw extracted JSON

{
  "study_type": "engineering",
  "exposure": {
    "band": "ELF",
    "source": "high-voltage power lines",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": null
  },
  "population": null,
  "sample_size": null,
  "outcomes": [
    "Magnetic flux density (µT) around high-voltage transmission lines",
    "Model validation accuracy vs field measurements",
    "Comparison to ICNIRP (2020) public exposure limit (100 µT)"
  ],
  "main_findings": "A Python-based simulation framework (Biot–Savart with modified finite element ground-air boundary conditions) modeled magnetic flux density for three conductor configurations (horizontal, vertical, triangular) under balanced three-phase loading (132 kV, 100 A per phase) at 10 m height. The horizontal configuration produced the highest peak field (120 µT directly beneath; 104.2 µT at 1.5 m), exceeding the ICNIRP (2020) public limit of 100 µT, while the triangular layout had a lower peak (57.6 µT) and more uniform distribution across 0–2 m. Ground-air interactions increased local intensity at 1.5 m by 28.3% and boundary effects reduced spatial prediction error by 15–25%; measurements at 5/10/15 m showed maximum relative deviation 0.74% supporting model validity.",
  "effect_direction": "mixed",
  "limitations": [],
  "evidence_strength": "insufficient",
  "confidence": 0.78,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "magnetic fields",
    "magnetic flux density",
    "high-voltage power lines",
    "transmission lines",
    "Biot-Savart law",
    "finite element",
    "ground-air boundary conditions",
    "ICNIRP 2020",
    "public exposure limit",
    "EMC planning",
    "Python",
    "NumPy",
    "SciPy",
    "Matplotlib",
    "gaussmeter",
    "conductor configuration",
    "ACSR Linnet",
    "132 kV",
    "100 A"
  ],
  "suggested_hubs": [
    {
      "slug": "who-icnirp",
      "weight": 0.7,
      "reason": "Abstract explicitly compares modeled fields to the ICNIRP (2020) public exposure limit."
    },
    {
      "slug": "occupational-exposure",
      "weight": 0,
      "reason": null
    }
  ]
}

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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.1038/s41598-025-11464-2
PubMed: 41028797

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