# ⚡ Power Fault Prediction System [](https://python.org) [](https://flask.palletsprojects.com/) [](https://tensorflow.org) [](LICENSE) A comprehensive AI-powered web application for predicting electrical power system faults using machine learning. This system analyzes power grid parameters with 4-decimal precision and provides real-time fault predictions with confidence scores and detailed visualizations.  ## 🚀 Features - **🧠 Real-time Fault Prediction**: Input power system parameters and get instant AI-powered fault predictions - **📊 Interactive Dashboard**: Modern, responsive web interface with intuitive controls - **📈 Visual Analytics**: Chart.js-powered visualizations showing probability distributions - **🎯 Confidence Scoring**: Detailed confidence metrics for each prediction - **✅ Parameter Validation**: Real-time input validation with helpful hints - **📱 Responsive Design**: Works seamlessly on desktop, tablet, and mobile devices - **🎨 Modern UI/UX**: Beautiful gradient design with smooth animations - **🔢 4-Decimal Precision**: Support for complex values with high precision - **🔍 Detailed Analysis**: Comprehensive fault analysis with actionable recommendations ## 🏗️ Architecture ### Backend (Flask) - **Model Loading**: Automatically loads the trained Keras model - **RESTful API**: Clean API endpoints for predictions and model information - **Error Handling**: Comprehensive error handling and validation - **CORS Support**: Cross-origin resource sharing enabled for frontend integration - **High Precision**: 4-decimal precision support for complex calculations ### Frontend (HTML/CSS/JavaScript) - **Responsive Design**: Mobile-first approach with CSS Grid and Flexbox - **Interactive Forms**: Real-time validation and user feedback - **Data Visualization**: Chart.js integration for probability charts - **Modern Styling**: CSS animations, gradients, and glass-morphism effects - **Progressive Web App**: PWA capabilities with offline support ## 📊 Model Information - **Input Features**: 522 parameters including voltage, current, power load, temperature, wind speed, and more - **Output Classes**: 3 fault categories based on actual dataset - 🔌 **Line Breakage** (1630 cases) - ⚡ **Transformer Failure** (1671 cases) - 🌡️ **Overheating** (1705 cases) - **Architecture**: Deep neural network with dense layers, batch normalization, and dropout - **Activation**: Softmax output for probability distribution - **Precision**: 4-decimal precision support for accurate predictions ## 🌐 Live Demo **Try the live demo**: [Power Fault Prediction System](https://yourusername.github.io/Indicators-of-Anxiety-or-Depression-Model/) ## 🛠️ Installation & Setup ### Prerequisites - Python 3.8 or higher - pip (Python package installer) - Git (for cloning the repository) ### 1. Clone the Repository ```bash git clone https://github.com/yourusername/power-fault-prediction.git cd power-fault-prediction ``` ### 2. Create Virtual Environment (Recommended) ```bash python -m venv venv # On Windows venv\Scripts\activate # On macOS/Linux source venv/bin/activate ``` ### 3. Install Dependencies ```bash pip install -r requirements.txt ``` ### 4. Verify Model File Ensure `power_faults_best.keras` is in the project root directory. ### 5. Run the Application ```bash python app.py ``` ### 6. Access the Application Open your web browser and navigate to: ``` http://localhost:5000 ``` ## 🚀 GitHub Pages Deployment This project is automatically deployed to GitHub Pages. To deploy your own version: ### 1. Fork and Clone ```bash git clone https://github.com/yourusername/Indicators-of-Anxiety-or-Depression-Model.git cd Indicators-of-Anxiety-or-Depression-Model ``` ### 2. Enable GitHub Pages 1. Go to your repository settings 2. Scroll to "Pages" section 3. Select "GitHub Actions" as source 4. Save the settings ### 3. Deploy Simply push to the main branch - the site will automatically deploy: ```bash git add . git commit -m "Deploy to GitHub Pages" git push origin main ``` ### 4. Access Your Site Your site will be available at: ``` https://yourusername.github.io/Indicators-of-Anxiety-or-Depression-Model/ ``` **Note**: Replace `yourusername` with your actual GitHub username. ## 📱 Usage ### 1. Input Parameters Fill in the power system parameters with 4-decimal precision: - **Voltage (V)**: System voltage (1000-5000V) - e.g., `2156.7892` - **Current (A)**: Current flow (50-1000A) - e.g., `247.3456` - **Power Load (MW)**: Power consumption (5-200MW) - e.g., `48.2345` - **Temperature (°C)**: Operating temperature (-50 to 100°C) - e.g., `35.6789` - **Wind Speed (km/h)**: Environmental conditions (0-200 km/h) - e.g., `23.4567` - **Duration of Fault (hrs)**: How long the fault has been active (0-24 hrs) - **Down Time (hrs)**: System downtime duration (0-24 hrs) - **Weather Condition**: Current weather (Clear, Rainy, Snowy, Windstorm, Thunderstorm) - **Maintenance Status**: Maintenance state (Completed, Scheduled, Pending) - **Component Health**: Equipment condition (Normal, Faulty, Overheated) ### 2. Get Prediction Click "Predict Fault" to analyze the parameters and get: - **Fault Classification**: Line Breakage, Transformer Failure, or Overheating - **Confidence Score**: Prediction reliability percentage - **Probability Distribution**: Visual chart showing class probabilities - **Input Summary**: Review of entered parameters with 4-decimal precision - **Detailed Analysis**: Comprehensive fault analysis with recommendations ## 🔧 API Endpoints ### GET `/api/model-info` Returns model information including input/output shapes and class labels. **Response:** ```json { "input_shape": [null, 522], "output_shape": [null, 3], "num_classes": 3, "class_labels": ["Line Breakage", "Transformer Failure", "Overheating"], "feature_count": 522 } ``` ### POST `/api/predict` Makes a fault prediction based on input parameters. **Request Body:** ```json { "voltage": 2156.7892, "current": 247.3456, "power_load": 48.2345, "temperature": 35.6789, "wind_speed": 23.4567, "duration_of_fault": 2.3456, "down_time": 1.2345, "weather_condition": "clear", "maintenance_status": "completed", "component_health": "normal" } ``` **Response:** ```json { "prediction": "Overheating", "confidence": 0.8567, "probabilities": { "Line Breakage": 0.1234, "Transformer Failure": 0.0199, "Overheating": 0.8567 }, "input_features": { "voltage": 2156.7892, "current": 247.3456, "power_load": 48.2345, "temperature": 35.6789, "wind_speed": 23.4567, "duration_of_fault": 2.3456, "down_time": 1.2345 }, "fault_details": { "fault_type": "Overheating", "severity": "HIGH", "description": "System temperature at 35.6789°C indicates thermal stress...", "recommended_actions": [...], "immediate_steps": [...], "affected_components": [...], "estimated_downtime": "2-6 hours", "risk_level": "HIGH" } } ``` ## 🎨 Customization ### Styling Modify `static/style.css` to customize: - Color schemes and gradients - Typography and fonts - Layout and spacing - Animations and transitions ### Functionality Update `static/script.js` to add: - Additional validation rules - Custom notification styles - Enhanced chart configurations - New interactive features ### Backend Modify `app.py` to: - Add new API endpoints - Implement additional preprocessing - Add authentication/authorization - Integrate with databases ## 🚨 Troubleshooting ### Common Issues 1. **Model Loading Error** - Ensure `power_faults_best.keras` exists in the project root - Check TensorFlow version compatibility - Verify file permissions 2. **Port Already in Use** - Change the port in `app.py`: `app.run(port=5001)` - Or kill the process using port 5000 3. **CORS Errors** - Ensure Flask-CORS is installed - Check browser console for specific error messages 4. **Precision Errors** - Ensure all numeric inputs are properly formatted - Check for string/number conversion issues ### Performance Optimization - **Model Loading**: The model is loaded once at startup for optimal performance - **Caching**: Consider implementing Redis for caching frequent predictions - **Scaling**: Use Gunicorn or similar WSGI server for production deployment ## 📈 Future Enhancements - [ ] User authentication and session management - [ ] Historical prediction tracking - [ ] Batch prediction capabilities - [ ] Advanced data visualization dashboards - [ ] Model retraining interface - [ ] Database integration for data persistence - [ ] Real-time monitoring and alerting - [ ] Mobile app development - [ ] API rate limiting and security - [ ] Automated testing suite - [ ] Docker containerization - [ ] CI/CD pipeline setup ## 🤝 Contributing We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details. 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add some amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 🙏 Acknowledgments - TensorFlow/Keras for the machine learning framework - Flask for the web framework - Chart.js for data visualization - Font Awesome for icons - The power systems engineering community for domain expertise ## 📞 Support For support, please: - Open an issue on GitHub - Check the troubleshooting section - Review the documentation ## 🌟 Show Your Support Give a ⭐️ if this project helped you! --- **Built with ❤️ for the power systems industry** *Empowering electrical grid monitoring with AI-driven fault prediction*# Trigger deployment