Customized power solutions for industries such as electric utilities, new energy, and industrial manufacturing.
Intelligent Operation and Maintenance Solution for UHV Transformers Based on Digital Twin
Traditional transformer operation and maintenance (O&M) relies on periodic inspections and corrective repairs, suffering from three core pain points: blind maintenance, high costs, and slow fault response. It fails to capture real-time degradation trends of internal multi-physical fields, leading to sudden faults that trigger large-scale power outages, while keeping labor and spare parts costs at a persistently high level. With the deployment of UHV DC interconnection and the high-density urban load growth, power grids are imposing new requirements for transformer O&M, including full-status visibility, advance warning, precise fault location, and cost control.
Centered on digital twin technology, this solution builds a closed-loop system integrating physical entity, digital mirroring, and service application. Combined with edge intelligence and cloud-based big data analytics, it realizes the leap from passive emergency repair to active prevention and control, supporting the safe and economical operation of global UHV and urban core power grids.
1. Three-Tier Digital Twin Architecture (Compatible with Global Standards)
| Tier | Components | Functions & Roles | International Standard Compliance |
|---|---|---|---|
| Physical Entity Layer | Built-in multi-physical field sensor array (temperature, vibration, partial discharge, dissolved gas in oil, electric field distribution, deformation and strain) + 5G/TSN industrial communication module | Real-time acquisition of key status parameters, covering all monitoring indicators specified in IEC 60076-7 | IEC 61850-7-420 (sensor modeling), IEEE C37.91 (protection communication) |
| Digital Mirroring Layer | High-fidelity multi-physical field simulation engine (electromagnetic-thermal-mechanical-chemical coupling) + historical & real-time data fusion model + knowledge graph reasoning library | Replicates internal operating status of transformers; predicts insulation aging, hot spot migration, and mechanical fatigue trends | IEC 61970/61968 (power grid information model), ISO 13374 (machine condition monitoring) |
| Service Application Layer | Fault early warning center, service life assessment & remaining useful life (RUL) calculation, intelligent work order dispatching, AR remote collaborative maintenance | Provides predictive maintenance recommendations, pinpoints fault locations accurately, and optimizes maintenance schedules | IEC 62351 (information security), IEC 62443 (industrial network security) |
2. Core Technologies
- Edge Intelligent Diagnosis: Deploy compact AI diagnostic modules on transformer units to conduct local analysis of key data including temperature, partial discharge, and dissolved gas in oil. It enables millisecond-level anomaly detection and early warning without waiting for cloud data transmission, ensuring reliability in areas with unstable network connectivity.
- High-Speed and Reliable Data Transmission: Adopt a converged network integrating 5G and industrial internet to ensure real-time, stable upload of sensor data. It maintains low latency and zero packet loss even in high-density sensor scenarios, delivering timely and accurate "field intelligence" for digital mirroring.
- Full-Lifecycle Health Profile: Unifies and records all device data from factory shipment, commissioning to O&M, forming a continuous health trend curve. It helps O&M personnel visualize aging processes and risk points intuitively, eliminating experience-reliant "blind maintenance".
- Cloud-Field Collaborative Optimization: On-site devices perform real-time monitoring and emergency judgment, while the cloud centrally analyzes global data and updates prediction models. This forms a continuously evolving O&M strategy, making the system increasingly intelligent and the early warning more accurate over time.
3. Functional Implementation (Performance Metrics)
- Advance Warning 72–168 Hours in Advance: Detects decreasing degree of polymerization of insulation paper, abnormal phase characteristics of partial discharge, and rising trends of CO/CO₂ in oil, with an early warning accuracy rate of >95%.
- Fault Location Accuracy ≤ 1m: Achieves component-level positioning of windings, bushings, and tap changers based on multi-sensor spatio-temporal correlation and electromagnetic inversion algorithms.
- O&M Cost Reduction >40%: Cuts unplanned downtime by 85%, reduces inspection frequency by 60%, and doubles the turnover rate of spare parts inventory.
- Service Life Assessment Error ≤ ±5%: Integrates thermal aging kinetic models and mechanical fatigue cumulative damage models to output RUL curves.
4. Application Scenarios and Innovative Cases
Scenario 1: UHV Converter Station Transformer (A Cross-Border Interconnection Project at ±800kV)
Challenges: Operation across climate zones (desert-plateau-coastal), where sensors are vulnerable to temperature differences and sand dust interference.
Solution Highlights:
- Adopts wide-temperature industrial-grade sensors (-40℃~+85℃) with self-cleaning dust covers;
- Edge AI chips identify sand dust interference signals locally, and the cloud automatically calibrates data drift.
Results:
- Issued an early warning 5 days in advance for abnormal dielectric loss of bushings, avoiding a DC blocking incident caused by pollution flashover;
- Reduced annual O&M costs by 42% and increased availability to 99.992%.
Scenario 2: Renovation of a 220kV Smart Substation in Urban Core Area (A Major International Metropolis)
Challenges: Space constraints, large load fluctuations, and limited power outage windows.
Solution Highlights:
- Installs non-intrusive optical sensors on existing CT/PT circuits without power outage for retrofitting;
- AR remote collaboration platform guides on-site personnel to perform precise maintenance based on the digital twin model.
Results:
- Achieved a fault location accuracy of 0.8m, shortening repair time from an average of 6h to 1.5h;
- Reduced unplanned power outages to zero within three years and improved user satisfaction by 28%.
5. Benefits and Global Value
| Dimension | Traditional O&M | This Solution | Improvement Effect |
|---|---|---|---|
| Fault Warning Lead Time | Post-fault detection | 72–168h | Shift from passive to active response |
| O&M Cost | Baseline value | ↓40%+ | Dual reduction in labor and spare parts costs |
| Availability | 97–98% | >99.99% | Enhanced power grid resilience |
| Carbon Emissions | Frequent dispatch of inspection vehicles | ↓35% | Green O&M practices |
Standard Leadership: Fully compliant with IEC 61850, IEC 62443, and IEEE C37 series standards, supporting rapid deployment in cross-border projects.
Conclusion
With digital twin as the brain, multi-physical field sensing as the eyes, edge intelligence as the hands, and cloud-edge collaboration as the cycle, this solution transforms transformers from "black-box devices" to "transparent assets". It upgrades the O&M model into a predictive, precise, and closed-loop self-healing intelligent system, providing a verifiable and replicable smart O&M paradigm for global UHV and urban core power grids.
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