Smart Upgrade and Efficient Maintenance Solution for Transmission Transformers

1. Background and Challenges​
Some transmission transformers in current power grid systems face significant challenges. On one hand, aging equipment with extended operational lifespans exhibits gradual degradation in technical performance, reliability, and safety. On the other hand, traditional manual inspections and periodic maintenance are inefficient, lagging in detecting potential faults. Maintenance efforts are plagued by high costs, operational difficulties, and challenges in fault localization. This has become a bottleneck constraining grid efficiency, safety, and stability. Therefore, it is imperative to advance equipment upgrades and deeply integrate intelligent maintenance methods.

​2. Solution: Dual-Driven Strategy for Equipment Upgrade and Smart Maintenance​
This proposal adopts a strategy combining "Hardware Upgrades" and "Software Empowerment" to holistically enhance transmission transformers’ performance, reliability, and maintenance efficiency through systematic deployment of new technologies.

​2.1 Core Equipment Upgrades​

• ​Promote On-Load Tap Changers (OLTC):​​ Gradually replace aging or non-smart fixed-tap transformers. OLTC automatically adjusts voltage ratios in real-time during operation, responding to grid fluctuations. This significantly ​enhances voltage stability and quality, outperforms traditional transformers in handling load variations and renewable energy integration, and reduces risks of equipment damage or load shedding due to voltage instability.
• ​Apply Gas-Insulated Switchgear (GIS):​​ Prioritize GIS over traditional Air-Insulated Switchgear (AIS) in new or retrofit projects. GIS integrates circuit breakers, disconnectors, grounding switches, transformers, and surge arresters into sealed metal enclosures filled with insulating gas. Key advantages include:
• ​Space Savings:​​ Occupies only 10%-30% of AIS footprint, optimizing substation land use—ideal for urban centers, land-constrained areas, or underground facilities.
• ​Environmental Resilience:​​ Sealed construction protects against dust, humidity, salt mist, and pollution, minimizing external-fault risks and adapting to harsh climates.
• ​High Reliability & Safety:​​ Significantly reduces arcing and explosion risks; failure rates are far lower than AIS. Maintenance workloads decrease, enhancing personnel and equipment safety.
• ​Low Noise & EMI:​​ Metal shielding minimizes operational noise and electromagnetic interference, reducing environmental impact.

​2.2 Intelligent Condition Monitoring System​

• ​Dissolved Gas Analysis (DGA) Online Monitoring:​​ Serves as the critical sensing layer. Real-time analyzers installed in oil circuits continuously monitor concentrations and trends of dissolved gases (H₂, CH₄, C₂H₆, C₂H₄, C₂H₂, CO, CO₂).
• ​Value:​​ Gas types, concentrations, and generation rates serve as sensitive "fingerprints" reflecting latent faults (e.g., thermal decomposition, partial/arcing discharge, oil overheating). Using analytical models (e.g., Duval Triangle, Rogers Ratios), the system automatically assesses health, enabling ​early, precise fault warnings​ (e.g., winding overheating, core grounding faults, insulation degradation), shifting from reactive repairs to predictive maintenance to prevent catastrophic failures.

​2.3 AI-Driven Smart Maintenance Management​

• ​Unified Data Platform:​​ Integrates multi-source data (DGA, partial discharge, core current, oil temperature/level, bushing losses), equipment records, maintenance history, and operational data (load, voltage, ambient temperature) to create a transformer digital twin.
• ​Big Data Analytics:​​ Uses data mining to correlate monitoring data with equipment states, establishing baseline models and identifying anomalies (especially in DGA parameters).
• ​AI-Powered Diagnosis & Decision-Making:​​
• ​Fault Diagnosis & Localization:​​ ML algorithms (e.g., DNNs, SVM, Random Forest) learn from historical faults and expert knowledge. Combined with real-time data, models intelligently identify fault types (e.g., thermal vs. electrical faults) and locate origins (e.g., windings, core, tap changers), aiding rapid troubleshooting.
• ​Health Assessment & Lifespan Prediction:​​ AI synthesizes multi-dimensional data to quantify health scores (e.g., Health Index) and predict remaining useful life, guiding replacement decisions.
• ​Risk Alerts & Maintenance Optimization:​​ Systems auto-evaluate risk levels and issue alerts. Optimization algorithms recommend tailored maintenance strategies (e.g., outage planning, task prioritization) based on risk, criticality, and resources. Confirmed faults trigger automated repair protocols.
• ​Expert Knowledge Base:​​ Built-in knowledge graphs and expert systems structure domain expertise and standards, supporting explainable AI decisions and boosting credibility.

​3. Expected Benefits​

1. ​Enhanced Intelligence:​​ Combines smart hardware (OLTC auto-regulation), sensors, and AI to enable "self-perception, self-diagnosis, self-decision, self-optimization."
2. ​Improved Reliability:​​ Higher inherent reliability of GIS/OLTC; AI monitoring reduces unplanned outages by preempting failures.
3. ​Increased Safety:​​ GIS design and smart monitoring lower explosion/fire risks; early fault intervention prevents accidents.
4. ​Lower Maintenance Costs:​​ Reduces manual inspection frequency; condition-based maintenance avoids over-/under-maintenance and optimizes resources/spares; preventive measures cut repair expenses.
5. ​Resource Efficiency:​​ GIS saves land; smart maintenance boosts equipment/personnel utilization.
6. ​Extended Lifespan:​​ Proactive health management slows insulation aging and performance decline, prolonging service life.

​4. Implementation Recommendations​

• ​Phased Rollout:​​ Prioritize aging equipment, critical substations, and urban load centers.
• ​Standardization First:​​ Develop uniform specs for equipment selection, sensor installation, data protocols, platform interfaces, and AI modeling.
• ​Data Integration:​​ Break silos by consolidating monitoring and management data onto a unified platform.
• ​Workforce Transformation:​​ Train staff in smart monitoring, data analytics, and AI diagnostics to shift toward data-driven, human-AI collaboration.
• ​Continuous Improvement:​​ Iteratively refine AI models and strategies using operational feedback.