CNN-LSTM Electrical Fault Classification

A hybrid deep learning pipeline for classifying electrical faults in transmission lines using time-series voltage and current measurements.

📊 Project Overview

Model: Hybrid CNN-LSTM Architecture
Accuracy: 78.01% on test set
Dataset: 7,861 electrical measurements
Classes: 6 fault types (No Fault, LG, LL, LLG, LLL, LLLG)

📦 Data Source

This project uses the Electrical Fault Detection and Classification dataset from Kaggle:

Dataset URL: https://www.kaggle.com/datasets/esathyaprakash/electrical-fault-detection-and-classification
Features: Time-series voltage and current measurements from transmission lines
Total Samples: 7,861 electrical measurements
Format: CSV files with multiclass and binary classification variants

🔗 Reference Project

This implementation is built with reference to a previous Kaggle notebook:

Reference Notebook: Electrical Faults Analysis & Classification
Author: harshsingh2209
Enhancements: Refactored into modular Python scripts, improved architecture, and comprehensive evaluation pipeline

📁 Project Structure

A Hybrid CNN - LSTM Model for fault detection in power distribution system/
├── src/ # Source code
│ ├── README.md # Source code documentation
│ ├── cnn_lstm_preprocessing.py # Data preprocessing & sequence generation
│ ├── cnn_lstm_model.py # Model architecture definitions
│ ├── train_cnn_lstm.py # Training pipeline
│ └── evaluate_model.py # Evaluation & visualization
│
├── models/ # Trained models & artifacts
│ ├── best_cnn_lstm_model.h5 # Trained model weights (1.5 MB)
│ ├── scaler.pkl # Fitted StandardScaler
│ └── label_encoder.pkl # Label encoder
│
├── results/ # Training results
│ ├── README.md # Results documentation
│ ├── visualizations/ # Generated plots
│ │ ├── confusion_matrix.png
│ │ ├── training_history.png
│ │ ├── classwise_f1_scores.png
│ │ └── training_summary.png
│ └── metrics/ # Performance metrics
│ ├── evaluation_metrics.txt
│ ├── training_history.csv
│ └── hyperparameters_log.json
│
├── data/ # Datasets
│ ├── classData.csv # Multiclass fault dataset (655 KB)
│ └── detect_dataset.csv # Binary fault detection dataset (952 KB)
│
├── docs/ # Documentation
│ └── README_CNN_LSTM.md # Detailed technical documentation
│
├── electrical-faults-analysis-classification.ipynb # Jupyter notebook
├── electrical-faults-analysis-classification.py # Converted Python script
├── model_architecture.txt # Model architecture summary
├── REFACTORING_SUMMARY.md # Project refactoring notes
└── README.md # This file

🚀 Quick Start

Prerequisites

pip install tensorflow numpy pandas scikit-learn matplotlib seaborn

Training

cd "c:\Users\HP\Downloads\A Hybrid CNN - LSTM Model for fault detection in power distribution system"
$env:PYTHONIOENCODING='utf-8'
python src/train_cnn_lstm.py

Evaluation

python src/evaluate_model.py

📈 Results

Overall Performance

Accuracy: 78.01%
Precision: 77.53%
Recall: 78.01%
F1-Score: 77.47%

Class-Wise Performance

Fault Type	Accuracy	F1-Score
No Fault (0000)	97.01%	95.18%
LG Fault (1001)	93.33%	88.24%
LLG Fault (1011)	88.50%	88.11%
LL Fault (0110)	80.50%	86.10%
LLL Fault (0111)	55.05%	50.21%
LLLG Fault (1111)	33.04%	38.27%

🏗️ Architecture

Model Flow Diagram

flowchart TD
 A["Raw CSV Data"] --> B["Data Loading & Preparation"]
 B --> C["Feature Extraction"]
 C --> D["Train/Test Split"]
 D --> E["StandardScaler Normalization"]
 E --> F["Sequence Generation"]
 
 F --> G["CNN-LSTM Model"]
 
 G --> H["CNN Block 1"]
 H --> I["CNN Block 2"]
 I --> J["LSTM Layer"]
 J --> K["Dense Layer"]
 K --> L["Output Layer"]
 
 L --> M["Predictions"]
 M --> N["Evaluation Metrics"]
 
 N --> O["Confusion Matrix"]
 N --> P["ROC Curves"]
 N --> Q["PR Curves"]
 N --> R["F1 Scores"]
 
 style G fill:#e1f5ff
 style H fill:#fff3e0
 style I fill:#fff3e0
 style J fill:#f3e5f5
 style K fill:#e8f5e9
 style L fill:#e8f5e9

Layer Architecture

Input (10 timesteps, 6 features)
 ↓
Conv1D (64 filters) → BatchNorm → MaxPool → Dropout
 ↓
Conv1D (128 filters) → BatchNorm → MaxPool → Dropout
 ↓
LSTM (100 units, dropout=0.3)
 ↓
Dense (64, ReLU) → Dropout
 ↓
Dense (6, Softmax)

Parameters: 125,142 (488 KB)

Detailed Architecture: See model_architecture.txt for complete layer-by-layer breakdown

📚 Documentation

For detailed documentation, see docs/README_CNN_LSTM.md

🔬 Key Features

✅ Temporal Awareness: LSTM layer captures time-series patterns
✅ Automatic Feature Learning: CNN extracts relevant features
✅ Production Ready: Includes scaler and encoder for deployment
✅ Comprehensive Evaluation: Confusion matrix, F1 scores, training curves
✅ Reproducible: Fixed random seeds and hyperparameter logging

📊 Visualizations

All visualizations are automatically generated in results/visualizations/:

Confusion Matrix - Prediction accuracy per class with heatmap
Training History - Accuracy & loss curves over epochs
Class-wise F1 Scores - Performance comparison across fault types
ROC Curves - Receiver Operating Characteristic (One-vs-Rest) with AUC scores
PR Curves - Precision-Recall curves with Average Precision scores

Run python src/evaluate_model.py to generate all visualizations.

🎓 Academic Use

This project was developed for a term paper on electrical fault classification. The implementation demonstrates:

Deep learning for time-series classification
Hybrid CNN-LSTM architecture
Complete ML pipeline from preprocessing to evaluation

📝 License

Educational project for academic purposes.

👤 Author

Developed for academic purposes as part of a 400 level project on electrical fault detection and classification.

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