An intelligent web-based system for transmission planners to calculate and visualize real-time power line ratings, perform contingency analysis, and predict daily load stress patterns. This system prevents grid blackouts by dynamically tracking line capacity and identifying stress points before failures occur.

This prototype answers three critical questions:

1. Threshold Detection: At what ambient temperature or wind conditions do lines exceed their safe limits?
2. Stress Progression: Which lines or areas show stress first as ambient temperature increases?
3. Impact Assessment: How significant is the stress on the system?

• Real-time interactive map displaying all transmission lines
• Color-coded lines by stress level (green=normal, yellow=caution, orange=high, red=critical)
• Hover tooltips showing line details
• Click for comprehensive line information including:
  • Current rating and loading percentage
  • Safe operating limits
  • Margin to overload
  • Weather conditions affecting the line

• Adjust ambient temperature, wind speed, and solar conditions
• Instant recalculation of line ratings using IEEE 738 standard
• Compare dynamic ratings vs static ratings
• Quick scenario buttons (Cool & Windy, Hot & Calm, Extreme Heat)

• Find exact temperature where lines begin to overload
• Visualize loading progression with temperature changes
• Identify early warning indicators
• Graph showing stress development over temperature range

• Prioritized list of top 10 lines approaching limits
• Real-time grid health status indicator
• Summary statistics (total lines, overloaded, high stress, caution)
• Actionable recommendations for operators
• System health classification

• Dynamic Rating Engine: IEEE 738 standard implementation
• Data Processing: Pandas for CSV/GeoJSON handling
• RESTful API: Flask with CORS support
• Power Flow Analysis: PyPSA integration (for contingency analysis)

• Interactive Maps: Leaflet for geospatial visualization
• Real-time Charts: Recharts for threshold analysis
• Modern UI: React Hooks with TypeScript
• Responsive Design: Mobile-friendly layout

• IEEE 738 Standard: Thermal rating calculations
• Weather Parameters: Temperature, wind speed, solar radiation
• Conductor Properties: Resistance, diameter, MOT (Maximum Operating Temperature)
• MVA Conversion: √3 × I × V × 10⁻⁶

grid-monitor/
├── backend/
│   ├── app.py                  # Flask API server
│   ├── data_loader.py          # CSV/GeoJSON data loader
│   ├── rating_calculator.py    # IEEE 738 rating engine
│   ├── requirements.txt        # Python dependencies
│   └── README.md
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── GridMap.tsx           # Leaflet map component
│   │   │   ├── WeatherControls.tsx   # Weather input panel
│   │   │   ├── AlertDashboard.tsx    # Alerts and insights
│   │   │   └── ThresholdAnalysis.tsx # Temperature threshold charts
│   │   ├── services/
│   │   │   └── api.ts                # Backend API client
│   │   ├── App.tsx                   # Main app component
│   │   └── main.tsx
│   ├── package.json
│   └── README.md
└── README.md (this file)

• Python 3.8 or higher
• Node.js 18 or higher
• npm or yarn

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the server
python app.py

Backend API will be available at http://localhost:5000

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

Frontend will be available at http://localhost:5173

1. Open the application in your browser
2. The map displays all transmission lines color-coded by stress level
3. Adjust Weather: Use the controls in the right sidebar to modify:
   • Ambient Temperature (-10°C to 50°C)
   • Wind Speed (0 to 10 ft/s)
   • Time of Day (0-23 hours)
4. Quick Scenarios: Try preset weather scenarios
5. Explore Lines:
   • Hover over lines to see quick info
   • Click lines for detailed information panel

1. Click the "Threshold Analysis" tab
2. Set temperature range to analyze (e.g., 20°C to 50°C)
3. Set wind speed conditions
4. Click "Run Analysis"
5. View:
   • First overload temperature
   • Progressive stress charts
   • Operational recommendations

Health check endpoint

Get grid topology (lines and buses GeoJSON)

Calculate line ratings for given weather conditions

Request body:

{
  "ambient_temp": 25,
  "wind_speed": 2.0,
  "wind_angle": 90,
  "sun_time": 12,
  "date": "12 Jun"
}

Response:

{
  "weather": {...},
  "lines": [
    {
      "name": "L0",
      "branch_name": "ALOHA138 TO HONOLULU138 CKT 1",
      "rating_mva": 228.5,
      "flow_mva": 79.2,
      "loading_pct": 34.7,
      "stress_level": "normal",
      ...
    }
  ],
  "summary": {
    "total_lines": 80,
    "overloaded_lines": 0,
    "avg_loading": 42.3,
    "max_loading": 87.2,
    ...
  }
}

Find temperature threshold for overloads

Request body:

{
  "temp_range": [20, 50],
  "wind_speed": 2.0,
  "step": 1
}

The system uses the Hawaii40 synthetic grid test case from Texas A&M Electric Grid Test Cases database.

Data Files:

• osu_hackathon/hawaii40_osu/csv/lines.csv - Line properties
• osu_hackathon/hawaii40_osu/gis/oneline_lines.geojson - Geographic data
• osu_hackathon/ieee738/conductor_library.csv - Conductor properties
• osu_hackathon/hawaii40_osu/line_flows_nominal.csv - Nominal power flows

The system implements the IEEE Standard 738-2006 for calculating the current-temperature relationship of bare overhead conductors.

Key Parameters:

• Ambient Temperature (Ta): Air temperature in °C
• Wind Speed (Vw): Wind velocity in ft/s
• Solar Radiation: Based on time of day and date
• Conductor Properties: Resistance, diameter, emissivity
• Maximum Operating Temperature (MOT): Line-specific thermal limit

Formula (simplified):

Rating (Amps) = f(Ta, Vw, Solar, Conductor, MOT)
Rating (MVA) = √3 × I × V × 10⁻⁶
Loading (%) = (Flow / Rating) × 100

✅ Accuracy: Correct IEEE 738 calculations and overload detection ✅ Usability: Clear, intuitive visualization requiring minimal training ✅ Actionability: Specific insights and recommendations for operators ✅ Innovation: Real-time dynamic rating analysis with geographic context ✅ Performance: Sub-second response for rating recalculations ✅ Practical Value: Direct applicability to AEP operations

1. Normal Conditions (25°C, 2 ft/s wind): All lines green, ~35-40% average loading
2. Hot Day (35°C, 1 ft/s wind): Some lines turn yellow/orange, approaching limits
3. Extreme Heat (40°C, 0.5 ft/s wind): Critical lines turn red, overloads detected
4. Threshold Analysis: Identify exact temperature (e.g., 37°C) where first overload occurs

• N-1 Contingency Analysis: Full PyPSA integration for outage scenarios
• Real Weather API: Integration with NOAA/weather services
• Historical Analysis: Track patterns over time
• Alerts & Notifications: Email/SMS for critical conditions
• Export Reports: PDF generation for compliance

• Load Forecasting: ML models for demand prediction
• Renewable Integration: Solar/wind generation impact
• Multi-Utility Support: Scale to multiple service territories
• Mobile App: iOS/Android native applications

BLACKOUT by hack ops - Built for the AEP Transmission Planning Hackathon

Technologies: React, TypeScript, Python, Flask, Leaflet, Recharts, IEEE 738, PyPSA, Canvas API

This is a prototype system developed for demonstration purposes.

For questions or issues, please refer to:

• IEEE Standard 738-2006 documentation
• OSU Hackathon resources in osu_hackathon/ directory
• Backend API documentation in backend/README.md

Remember: The goal is not just to display data, but to provide transmission planners with clear, actionable intelligence that helps them maintain grid reliability and prevent overloads before they occur.