AI for fault detection in high-voltage transmission networks uses machine learning models to analyze operational measurements, protection-system records, sensor data and inspection imagery to identify abnormal patterns associated with emerging equipment faults. The approach supports earlier detection of issues on transmission lines and substations, helping operators prioritize maintenance and respond before faults develop into outages or equipment failures.  

Unlike traditional approaches based primarily on scheduled inspections, periodic testing and manual review after alarms or incidents, AI-enabled fault detection supports more continuous and condition-based monitoring. Models can assess large volumes of operational and inspection data, including infrared imagery, to identify deviations from expected asset behavior and improve fault localization and classification.  

This is particularly relevant for large transmission systems and ultra-high-voltage corridors, where assets are geographically dispersed and manual inspection is resource-intensive. By improving visibility of asset condition and targeting maintenance more effectively, AI-based fault detection can reduce avoidable outages, lower inspection effort and support more resilient grid operation. 

Main outcomes of the solution 

• Reliability & Flexibility: Improves security of supply by detecting abnormal conditions early and helping operators locate and classify faults faster, reducing the likelihood and duration of outages and supporting stable operation as the grid becomes more dynamic. 
• System Resilience: Strengthens the ability to withstand and recover from disruptions by enabling faster situational awareness and response, including real-time analytics that can run close to field devices when communications or central systems are constrained. 
• Affordability: Lowers operating cost by shifting from routine, schedule-based inspections to targeted, condition-based work, reducing unnecessary field visits and avoiding expensive unplanned failures through earlier intervention and better asset life management. 

Key implementation and scaling factors 

1. Establish consistent, high-quality operational data streams and asset inspection inputs so models can learn “normal” behavior and detect abnormal patterns reliably. 
2. Integrate AI outputs into existing grid operations and maintenance workflows so alerts translate into clear actions, not parallel dashboards. 
3. Automate inspection and monitoring programs able to handle very large data volumes – especially images – supported by adequate compute capacity and disciplined data management. 
4. Build workforce capability to validate model outputs, maintain performance over time and retain asset knowledge. 
5. Embed cybersecurity, access control and system assurance from the start, given the ingestion of sensitive operational data. 

Case study 

State Grid’s Electric Power Space Technology Company and North China Electric Power University (2023), China 

In China, State Grid’s Electric Power Space Technology Company, working with North China Electric Power University, developed an AI-powered infrared defect-identification system for ultra-high-voltage transmission lines. The solution analyzes infrared video and images to detect abnormal heat patterns that can indicate emerging faults, helping operators identify likely problem locations sooner and prioritize field intervention.  

The implementation was positioned for operational support during summer peak-demand periods, when system stress and the cost of outages are higher. Reported results include monitoring accuracy above 90%, alongside reduced manual review effort through automated frame extraction and defect identification from inspection videos. In one operational example, reviewing infrared video shifted from several hours of manual screening to a shorter automated workflow from upload to analysis completion, reducing dependence on individual inspector attention and experience.  

The case shows how AI-assisted inspection can support condition-based maintenance on long-distance transmission networks. By combining infrared sensing, machine learning and automated image review, the approach can help operators detect emerging defects earlier and focus field resources on higher-risk assets. 

References:

1. Why edge AI is now crucial for powering the global grid | World Economic Forum 
2. IEA Activities on Energy and AI.pdf 
3. State Grid develops AI-assisted system to monitor UHV transmission lines - Global Times 
4. News - AI technology is used in large-scale heat detection of transmission lines