Team Name: TechTitans

Automated Fault Detection and Safety System for Overhead Power Grids

Objective

This project addresses fault detection and automated safety in overhead power grids, particularly during severe weather events and environmental hazards. It provides real-time monitoring and automated fault isolation to reduce power outage risks and enhance public safety.

Concept and Approach

The system integrates voltage sensors, a bus serial servo-controlled AB switch, and cloud-based monitoring for fault detection in the overhead power grid.

Key Innovations:

• Automated AB Switch in Transformers: Uses a bus serial servo to instantly isolate faulty sections, preventing further damage and reducing downtime.
• AI-Driven Fault Detection: Analyzes voltage anomalies and triggers alerts via a mobile app.
• Real-Time Updates & Control: Uses ESP8266 to transmit live data to Firebase, allowing linemen to diagnose and restore power remotely.
• Visual & Digital Indications: Defective poles are indicated with LEDs, while real-time updates are displayed on an app.

Impact

• Faster Fault Identification & Response: Reduces downtime and operational inefficiencies.
• Enhanced Safety: Minimizes electrocution risks during adverse weather conditions.
• Cost Savings: Reduces manual inspections and unnecessary maintenance.
• Scalability: Can be implemented in urban and rural power grids with minimal modifications.

Feasibility

Resources Needed:

• Hardware: Arduino, ESP8266, voltage sensors (ZMPT101B), bus serial servo for AB switch automation, power supply, LoRa modules.
• Infrastructure: Reliable internet connectivity for cloud-based monitoring and deployment setup for field testing.

Implementation Plan:

1. Prototype Development: Integrate sensors and microcontrollers to collect voltage data.
2. Automation & Communication: Implement bus serial servo-controlled AB switch and wireless data transmission via LoRa and Firebase.
3. Mobile App & AI Integration: Display real-time data and enable remote fault resolution.
4. Testing & Validation: Simulate various fault conditions and ensure system reliability.

Tech Stack

• Software: Arduino IDE, Firebase, FastAPI, Flask, Google Cloud, Flutter, TensorFlow Lite.
• Communication:
  • Serial (Arduino ↔ ESP, Bus Serial Servo ↔ Arduino)
  • Analog (Voltage Sensors ↔ Arduino)
  • LoRa (Arduino ↔ LoRa Transmitter, LoRa Receiver ↔ ESP)
  • Wi-Fi (ESP ↔ Firebase)

Sustainability

• Modular & Expandable: Can be adapted for single- and three-phase overhead power grids with enhanced hardware, including customized PCBs and LoRa communication.
• Smart Grid Integration: Future scope includes AI-driven predictive maintenance.
• Self-Sustaining Model: Potential for large-scale deployment with government and utility partnerships.

Differentiation

• Automated AB Switch with Bus Serial Servo: Unlike existing solutions that rely on manual intervention, our system isolates faults autonomously.
• AI-Powered Fault Detection: Enhances accuracy compared to traditional threshold-based methods.
• Mobile-Based Monitoring: Provides real-time fault alerts and allows linemen to update defect resolution.

Project Structure

|-- frontend
|-- hardware
    |-- transmitter
    |-- receiver
        |-- arduino
        |-- esp8266

Contributors

• Hariram V (22MIS1176)
• Anbarasan K (22MIS1173)
• Naresh S (22MIS1175)
• Balaji K (22MIS1198)