GRID PATROL COPILOT: An AI-Powered Patrol Intelligence Framework for Transmission Line Fault Localization and Field Response Optimization

Abstract

Rapid fault identification and effective field patrolling are essential for minimizing outage duration and restoring transmission system availability following line faults. In conventional practice, patrol planning requires engineers to manually collect and correlate information from multiple sources, including relay fault records, transmission line profiles, tower databases, terrain information, and historical maintenance records. This process is often time-consuming and heavily dependent on individual experience, resulting in delays in patrol deployment and fault restoration. This paper presents GRID PATROL COPILOT, an Artificial Intelligence (AI)-powered patrol intelligence framework developed to transform fault-related information into actionable field recommendations through a conversational interface. The proposed framework integrates fault location analysis, transmission line databases, tower intelligence, terrain information, and patrol planning logic using Microsoft Copilot Studio, Power Automate, SharePoint, and Generative AI technologies. Upon receiving fault details, the system automatically identifies the probable fault span, determines vulnerable towers, evaluates access conditions, and generates optimized patrol deployment recommendations. A practical implementation on extra-high-voltage transmission lines is presented to demonstrate the framework\'s capability in supporting fault localization and patrol decision-making. The results indicate significant reduction in manual analysis effort, improved standardization of patrol planning, faster field deployment, and enhanced restoration readiness. The proposed framework demonstrates a practical and scalable approach for integrating AI-driven decision support into transmission line operation and maintenance activities.

Introduction

The text presents GRID PATROL COPILOT, an AI-powered framework designed to improve the response to transmission-line faults by converting fault-location information and utility data into practical patrol and inspection recommendations.

Transmission-line faults require rapid identification and field inspection because delays can increase outage duration and affect power-system reliability. Traditional fault-response methods depend on relay/fault-locator outputs, tower databases, GIS systems, terrain information, and engineering expertise. Although these tools provide useful information, the data is often distributed across different systems, requiring manual interpretation and coordination.

The proposed GRID PATROL COPILOT addresses this gap by combining receives and validates fault information. It then identifies the likely tower span, retrieves relevant engineering and geographical information, evaluates the affected corridor Artificial Intelligence, Generative AI, transmission-line databases, workflow automation, and conversational interfaces. It is implemented using technologies such as Microsoft Copilot Studio, Power Automate, SharePoint, Excel Online, and Generative AI.

Main contributions

• Develops an AI-enabled transmission-line fault-response system.

• Integrates fault information with tower, line, terrain, village, and operational data.

• Automatically identifies probable fault spans and priority inspection locations.

• Provides patrol boundaries, access information, and deployment recommendations.

• Uses existing digital infrastructure, reducing the need for specialized hardware or separate analytical platforms.

Framework architecture

The system consists of five main layers:

1. User Interaction Layer – Engineers enter information such as line name, fault distance, fault type, and reference terminal through a conversational interface.

2. Fault Localization Layer – The reported fault distance is matched with cumulative tower distances to identify the probable fault span and inspection corridor.

3. Data Intelligence Layer – Relevant tower, route, village, accessibility, and other engineering records are retrieved from utility repositories.

4. AI Reasoning Layer – Generative AI interprets the combined information and identifies important inspection locations, access constraints, and investigation strategies.

5. Patrol Intelligence Layer – The system converts the analysis into actionable field recommendations, including inspection priorities, patrol limits, starting points, and deployment strategies.

AI-driven methodology

The proposed workflow follows six stages:

Fault Information Acquisition → Fault Span Identification → Engineering Data Correlation → Patrol Intelligence Analysis → Deployment Strategy → Patrol Intelligence Report

The system first receives and validates fault information. It then identifies the likely tower span, retrieves relevant engineering and geographical information, evaluates the affected corridor, determines inspection priorities, and creates a deployment strategy. The final output is a structured patrol intelligence report that field personnel can use directly.

Conclusion

This paper presented GRID PATROL COPILOT, an AI-powered Patrol Intelligence Framework developed to support transmission line fault localization and field-response planning. The framework combines fault-location analysis, engineering databases, workflow automation, and conversational AI technologies within a unified operational environment. Using information available immediately after a fault event, the system generates inspection-oriented guidance that can assist personnel during restoration activities. A practical implementation was demonstrated using transmission line data, tower records, and patrol-planning information. The case study showed the ability of the framework to determine probable fault spans, retrieve corridor-specific information, identify inspection priorities, and generate deployment recommendations through a structured workflow. Experimental evaluation further confirmed the successful operation of the major functional components and demonstrated the applicability of the proposed approach for transmission line field-support activities. The introduction of the Patrol Response Index (PRI) provided a conceptual mechanism for evaluating improvements in preparedness between fault occurrence and commencement of field investigation. The results indicated that automation of key preparatory activities can contribute to faster mobilization and more efficient utilization of available resources. The proposed framework demonstrates how existing utility datasets, automation technologies, and artificial intelligence can be combined to enhance operational workflows without requiring specialized infrastructure. As utilities continue their digital transformation journey, solutions such as GRID PATROL COPILOT can play an important role in improving information accessibility, supporting engineering decision-making, and strengthening transmission system operations.

References

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Copyright

Copyright © 2026 Chaganti Chandra Mouli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.