Why is West Side Transformer Overheating?

Transformer Issue Finder: AI-Driven Grid Asset Management

The Challenge: Aging Infrastructure, Load Growth and Regulatory Compliance

In utility grid operations, transformers are the heart of our substations - and among the most expensive assets to replace. With sizes ranging from 20 MVA distribution units to massive 500 MVA transmission transformers, these critical components face unprecedented challenges today:

• Aging Infrastructure: The average power transformer in North America is now 38 years old, with 30% of units exceeding 40 years of service life

https://www.tdworld.com/utility-business/article/21243198/transformative-times-update-on-the-us-transformer-supply-chain

• Unprecedented Load Growth: Electrification across transportation, heating, and data centers is creating demand patterns these assets weren't designed to handle

https://www.energy.gov/articles/doe-releases-new-report-evaluating-increase-electricity-demand-data-centers

• Global Transformer Shortage: Replacement lead times have extended dramatically, making proper maintenance not just good practice but an operational necessity

https://www.dhirubhai.net/posts/andreasschierenbeck_the-energy-sector-is-undergoing-unprecedented-ugcPost-7305164571390935040-is_y?utm_source=share&utm_medium=member_desktop&rcm=ACoAAANYXyQBN5RDNvCR_1vBiTICHMKG7fqaRHY

• Knowledge Drain: Retiring maintenance experts take decades of troubleshooting expertise with them

https://librestream.com/media/LIB-utilities-whitepaper-workforce-transformation-final.pdf

• Regulatory Pressure: New standards like FERC 881 and FERC 1923 require more sophisticated asset management approaches

https://pr-tech.com/news/ferc-order-881-2025-requirements/

The Real-World Impact

These challenges manifest in very tangible operational problems:

When a SCADA alarm triggers at 2:37 AM showing "TRANSFORMER at WEST SIDE SUB - HIGH OIL TEMPERATURE WARNING (87°C)," operations and maintenance teams face critical decisions with limited information. The traditional approach involves calling on-call personnel, hoping someone remembers similar past incidents, and potentially conducting time-consuming diagnostic processes.

This reactive approach leads to:

• Extended outage durations
• Higher maintenance costs
• Accelerated asset deterioration
• Increased safety risks
• Reduced grid reliability
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Introducing the Transformer Issue Finder

To address these challenges, here is a practical AI-powered application that helps operations and maintenance teams quickly locate relevant historical records about similar transformer issues. This tool bridges the gap between raw maintenance data and actionable intelligence.

How It Works: A Technical Overview

The solution consists of three key components:

1. FastAPI Backend (query_api.py)

• Processes natural language queries from users
• Converts queries into vector embeddings using SentenceTransformers
• Uses FAISS (Facebook AI Similarity Search) to find similar past transformer issues
• Returns relevant maintenance records ranked by similarity

2. Streamlit Frontend (app.py)

• Provides an intuitive web interface accessible to non-technical users
• Takes user queries in plain language
• Communicates with the FastAPI backend
• Displays retrieved maintenance records in a user-friendly format

3. Dependency Management (requirements.txt)

• Manages required Python packages: #FastAPI, #Streamlit, #FAISS, and #SentenceTransformers
• Ensures consistent deployment across environments

The User Experience

Using the application is straightforward:

1. The user navigates to a simple web interface
2. They type a question like "Why is the transformer at West Side overheating?"
3. The system instantly retrieves relevant past issues: "Transformer T-789 experienced overheating due to blocked radiator airflow after autumn storms deposited debris." "Transformer winding overheating in Central Substation due to excessive harmonic distortion from nearby industrial loads." "High ambient temperature caused transformer at East Substation to exceed safe limits during summer heat wave."
4. With these insights, maintenance teams can prioritize the most likely causes first

Real Business Value

This application delivers tangible benefits to utility operations:

• Extended Asset Life: Better maintenance decisions help maximize transformer lifespan
• Reduced Downtime: Faster issue identification means shorter outage durations
• Knowledge Preservation: Institutional expertise becomes accessible even after staff turnover
• Cost Avoidance: Each prevented transformer failure saves $1-5M in replacement costs
• Improved Planning: Pattern identification enables proactive maintenance scheduling
• Enhanced Safety: Historical safety incidents help prevent future occurrences
• Operational Efficiency: Better coordination between operations and maintenance teams

Development Journey

The development process followed these key steps: All help from online resources only ??

1. Problem Definition: Identifying the core challenge of knowledge preservation and rapid diagnosis
2. Component Development: Building the FastAPI backend and Streamlit frontend separately
3. Local Testing: Verifying functionality in a controlled environment
4. Integration: Connecting the frontend and backend components
5. Deployment Planning: Evaluating cloud hosting options. Running in local environment only.

Current Status and Next Steps

The application is:

• Functional: Working in local environment. With limited hard coded data/scenario.
• Tested: Tested locally for poc.
• Cloud deployment: Temporarily paused while evaluating hosting options between Render, Deta Space, and enterprise cloud platforms. I tried but need technical assistance, it seems. Available online resources couldn’t help resolving issues.

The roadmap includes:

1. Pilot Program: Deploy to a certain Region operations and maintenance teams
2. Data Expansion: Incorporate additional historical maintenance records
3. Integration Planning: Connect with existing operational and asset management systems
4. ROI Analysis: Measure time savings and maintenance outcomes

Scaling Beyond Transformers

While transformers are the initial focus, this approach can be readily applied to other critical grid assets:

Substation Assets:

• Circuit Breakers
• Protective Relays
• Battery Systems
• Capacitor Banks

Transmission & Distribution Infrastructure:

• Overhead Conductors
• Structures (poles, towers)
• Underground Cables
• Line Devices (reclosers, sectionalizers)

The Bigger Picture: AI-Powered Grid Resilience

This application represents just the beginning of AI's potential in grid asset management. As utilities face increasing pressure to maintain aging infrastructure with fewer experienced workers while accommodating electrification-driven load growth, AI-powered tools will become essential for comprehensive asset management.

The Transformer Issue Finder demonstrates how utilities can:

• Leverage existing maintenance data to create new operational value
• Preserve institutional knowledge as workforce demographics shift
• Enhance decision-making with AI augmentation
• Deploy practical AI solutions without massive investment

For utilities navigating the complex challenges of grid modernization, energy transition, and workforce evolution, this approach offers a pragmatic starting point for incorporating AI into core operations.

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As utilities continue their digital transformation journey, AI-enhanced asset management represents one of the most promising areas for operational improvement and cost reduction. The Transformer Issue Finder demonstrates how focused AI applications can deliver immediate value while building toward a more resilient and intelligent grid.