AI Wind Farm Optimizer Prototype

A comprehensive prototype for AI-driven wind farm optimization that demonstrates core functionality including wind data analysis, turbine placement optimization, and advanced visualization capabilities.

🚀 Quick Start

Prerequisites

• Python 3.8+
• Git

Installation

1. Clone and navigate to the prototype

   cd wind-farm-prototype

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the prototype

   python main.py

📁 Project Structure

wind-farm-prototype/
├── src/
│   ├── models/           # Physics & ML models
│   │   ├── wake_model.py     # Jensen wake model
│   │   ├── power_model.py    # Power calculation
│   │   └── optimizer.py      # Optimization algorithms
│   ├── data/             # Data management
│   │   ├── wind_data.py      # Wind data processing
│   │   └── data_generator.py # Sample data generation
│   ├── visualization/    # Visualization tools
│   │   ├── basic_plots.py    # Static plots
│   │   ├── interactive_plots.py # Interactive plots
│   │   └── wind_analysis.py  # Wind analysis plots
│   └── utils/            # Shared utilities
│       ├── config_loader.py  # Configuration management
│       └── file_utils.py     # File operations
├── notebooks/            # Jupyter notebooks
├── tests/                # Test suite
├── results/              # Output storage
├── data/                 # Data files
├── models/               # Saved models
├── logs/                 # Log files
├── config.yaml          # Configuration
├── main.py              # Main application
└── requirements.txt     # Dependencies

🎯 Core Features

1. Wind Data Analysis

• Weibull wind speed distribution modeling
• Wind direction analysis with wind roses
• Time series wind data generation
• Statistical analysis of wind patterns

2. Turbine Performance Modeling

• Jensen wake model implementation
• Power curve calculations
• Wake effects and power losses
• Turbine interaction modeling

3. Optimization Algorithms

• Genetic Algorithm for turbine placement
• Grid-based optimization
• Random placement for comparison
• Performance evaluation metrics

4. Advanced Visualization

• Interactive Plotly visualizations
• Wind farm layout plots
• Performance comparison charts
• Wind resource analysis plots
• 3D wind farm visualization

📊 Example Usage

from src.models.optimizer import WindFarmOptimizer
from src.visualization.basic_plots import WindFarmVisualizer
from src.data.wind_data import WindDataProcessor

# Initialize components
wind_processor = WindDataProcessor()
optimizer = WindFarmOptimizer()
visualizer = WindFarmVisualizer()

# Process wind data
wind_speeds, wind_directions = wind_processor.generate_wind_data()

# Optimize turbine placement
optimal_positions = optimizer.optimize_turbine_placement()

# Visualize results
visualizer.plot_turbine_layout(optimal_positions)
visualizer.plot_wind_rose(wind_speeds, wind_directions)

🧪 Running Tests

python -m pytest tests/ -v

📈 Performance Metrics

The prototype includes comprehensive performance evaluation:

• Power Output: Total farm power generation
• Wake Losses: Power losses due to turbine interactions
• Efficiency: Overall farm efficiency
• Capacity Factor: Utilization of installed capacity

🎨 Visualization Examples

Static Plots

• Wind speed distribution
• Wind direction rose
• Turbine layout maps
• Performance comparisons

Interactive Plots

• 3D wind farm visualization
• Interactive wind analysis
• Real-time parameter adjustment
• Dynamic performance monitoring

🔧 Configuration

All parameters are configurable via config.yaml:

• Wind farm dimensions
• Turbine specifications
• Optimization parameters
• Visualization settings
• Machine learning parameters

📚 Documentation

• API Reference: Detailed function documentation
• Development Guide: Coding standards and practices
• Team Guide: Collaboration guidelines
• User Manual: End-user documentation

🤝 Team Collaboration

This prototype supports team development with:

• Modular architecture for parallel development
• Comprehensive testing framework
• Version control integration
• Documentation standards

📄 License

MIT License - see LICENSE file for details.

🆘 Support

For questions or issues:

1. Check the documentation
2. Review existing issues
3. Create a new issue with detailed description

🚀 Future Enhancements

• Reinforcement Learning integration
• Real-time weather data integration
• Advanced wake models
• Multi-objective optimization
• Cloud deployment capabilities