FELINES — Forecast of the Effects of Lightning IN Electrical Systems

FELINES is a research project aiming to design a preventive protection concept for electrical infrastructures by sensing electromagnetic fields generated during the early phases of lightning inception—in particular the Preliminary Breakdown Pulses (PBP)—and using them to predict whether the upcoming Return Stroke (RS) could be dangerous, enabling timely disconnection of vulnerable equipment.
The work combines PBP/RS modeling, electromagnetic field & coupling simulations, and machine learning for early classification (“dangerous RS” vs “not dangerous”).
More context is available on the project website and public deliverables (links below).

🔧 Installation & Setup

git clone https://github.com/ShayanDodge/FELINES-Lightning-Forecast.git
cd FELINES-Lightning-Forecast

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📑 Table of Contents

• 📦 What the Repository Includes

• 📌 Phase 1 — First Case Study

  • 🌩 Project Vision
  • 📦 Repository Content
  • 📊 Datasets
  • 🧠 Implemented Methods
  • 📈 Performance — New Scenario (MTDM)
  • 🚀 Getting Started

• 🔮 Next Steps — Phase 2

• 👥 Project Team

• 🤝 Acknowledgments

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📦 What the Repository Includes

📍 Phase 1 — Lightning Geolocation and Peak Current Estimation

• Developing deep learning models trained on electromagnetic signals
• Estimating lightning strike location
• Regressing channel-based peak current

⚡ Phase 2 — Early-Stage Classification and Protection

• This phase provides the physical and data-driven foundation for risk assessment and protection strategies
• Machine learning–based classification of dangerous vs non-dangerous events
• PBP and RS electromagnetic modeling
• Field-to-line coupling simulations

⚠️ This repository currently contains the implementation of Phase 1 (Geolocation and Peak Current Estimation). Phase 2 (Early Classification and Protection Strategy) will be integrated in future releases.

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📌 Phase 1 — First Case Study

This release corresponds to Phase 1 of the FELINES project, dedicated to lightning geolocation and peak current estimation from lightning-induced voltages on overhead lines.

The scientific foundations of this work are presented in:

Dodge, S.; Nicora, M.; Barmada, S.; Brignone, M.; Procopio, R.; Tucci, M.
“A deep learning based lightning location system.”
Electric Power Systems Research, 2025, https://doi.org/10.1016/j.epsr.2025.111437.
Read the article

📌 This repository implements, reproduces, and extends the methodology described in the above peer-reviewed publication. If you use this code, dataset, or results in your research, please cite the original article.

🌩 Project Vision

Rather than relying solely on conventional far-field Lightning Location Systems (LLS), FELINES introduces a line-integrated approach that infers lightning characteristics directly from voltage waveforms induced on overhead power lines.

The framework enables the estimation of:

• Lightning strike coordinates (x, y)
• Channel-base peak current (kA)

At its core, the system learns the physical-to-data mapping:

Induced Voltage Waveforms  →  {Strike Location (x, y), Peak Current}

📦 Repository Content

This repository provides the complete implementation of Phase 1, including datasets, models, and reproducible experimentation pipelines.

• Simulation Datasets

  • Old Scenario — A benchmark 10-km single-conductor transmission line used to implement and compare the three proposed Deep Learning approaches (FFDM, FTDM, MTDM) under controlled conditions.
  • New Scenario — A realistic 2-km three-phase medium-voltage distribution system including surge arresters and nonlinear components. In this configuration, only the best-performing model (MTDM) is evaluated, following its superior benchmark results.

• Deep Learning Architectures
  Full implementations of the best regression models:

  • MTDM — Modified Time Domain Method
    with MTDM selected as the reference model for the high-fidelity scenario.

• Preprocessing and Feature Engineering Pipeline
  Signal conditioning, dimensionality reduction, and adaptive time-domain compression routines.

• Training and Evaluation Framework
  10-fold cross-validation, performance metrics, and robustness analysis utilities ensuring full reproducibility of the published results.

📊 Datasets

The repository includes two simulated scenarios used for model development and validation:

Feature	OLD SCENARIO — Benchmark Case	NEW SCENARIO — Realistic Distribution Line
Line Configuration	10 km single-conductor transmission line	2 km three-phase MV distribution line
Voltage Sensors	2 sensors	2 sensors
Lightning Events	2000 simulated events	2000 simulated events
Samples per Waveform	2000	3701
Soil Conductivity	5 mS/m	1 mS/m
Surge Arresters	Not included	Installed every 250 m
Purpose	Model comparison (FFDM, FTDM, MTDM)	High-fidelity validation (MTDM only)

🧠 Implemented Methods

Three Deep Learning approaches are implemented and evaluated:

Method	Domain	Description
FFDM	Frequency Domain	Full-spectrum Fourier representation of voltage waveforms.
FTDM	Time Domain	Direct use of time-domain voltage samples.
MTDM	Modified Time Domain	Compressed time-domain representation with adaptive feature reduction.

⭐ Selected Model — MTDM (Best Performing)

The Modified Time Domain Method (MTDM) demonstrated the best overall accuracy and robustness.

Key characteristics:

• Leading-zero removal (arrival-time normalization)
• Adaptive non-uniform time-domain compression
• Fully connected neural network
• 3 hidden layers (74 neurons each)
• 10-fold cross-validation

📈 Performance — New Scenario (MTDM)

The Modified Time Domain Method (MTDM) achieves the following results on the realistic distribution line scenario:

Metric	Value
Average Location Error (ALE)	67.24 m
Peak Current Mean Absolute Error (MAE)	1.40 kA
Localization Accuracy	~70% of events with error < 50 m

🔍 Robustness Analysis

The model maintains stable performance under realistic perturbations:

✔ Additive White Gaussian Noise (AWGN) — tested down to SNR = 25 dB
✔ Variations in soil conductivity (limited sensitivity observed)

🚀 Getting Started

1. Navigate to:
Phase 1 — Lightning Geolocation and Peak Current Estimation/
2. Choose:
- Old scenario
- New scenario
3. Follow the README inside each folder.

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🔮 Next Steps — Phase 2

• Preliminary Breakdown Pulse (PBP) analysis
• Early classification of dangerous return strokes
• Protection-triggering strategy design
• Experimental validation on real networks

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👥 Project Team

FELINES is developed by a multidisciplinary research team from University of Genoa (DITEN), University of Pisa (DESTEC), and University of Campania “Luigi Vanvitelli”.

🇮🇹 University of Genoa — DITEN

Department of Naval, Electrical, Electronic and Telecommunication Engineering

Name	Role	ORCID
Renato Procopio	Associate Professor	[Profile]
Massimo Brignone	Associate Professor	[Profile]
Daniele Mestriner	Researcher (RTD-A)	[Profile]
Martino Nicora	PhD Candidate	[Profile]

🇮🇹 University of Pisa — DESTEC

Department of Energy, Systems, Territory and Construction Engineering

Name	Role	ORCID
Sami Barmada	Full Professor	[Profile]
Shayan Dodge	Research Fellow	[Profile], [ORCID]
Mauro Tucci	Full Professor	[Profile]

🇮🇹 University of Campania “Luigi Vanvitelli”

Department of Engineering

Name	Role	ORCID
Alessandro Formisano	Full Professor	[Profile]
Ehsan Akbari Sekeh Ravani	Research Fellow	[Profile]

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🤝 Acknowledgments

Supported by: Italian Ministry for Education, University, and Research (PRIN 2022) European Union — NextGenerationEU