Smart Management and Maintenance Solution for Power Generation Transformers

Ⅰ. Background and Pain Points
As power generation enterprises scale up and grid intelligence advances, traditional periodic maintenance models struggle to meet the O&M demands of large power transformers:
• Delayed Fault Response: Sudden insulation aging or overheating cannot be detected in real time
• High Maintenance Costs: Over-maintenance wastes resources, while insufficient maintenance causes unplanned downtime
• Fragmented Data Analysis: Isolated data from DGA (Dissolved Gas Analysis), partial discharge tests, etc., lack intelligent cross-diagnosis

II. System Architecture and Core Technologies
(1) Intelligent Sensing Layer
Deploys multi-dimensional IoT terminals:

graph LR 

A[Winding Fiber Optic Temp] --> D[Central Analytics Platform] 

B[DGA Sensor] --> D 

C[Vibration/Noise Monitor] --> D 

E[Core Grounding Current Detector] --> D 

(2) AI Analytics Engine

(3) Predictive Maintenance Platform
3D Dashboard: Real-time display of transformer load rates, hotspot temps, and risk levels
Maintenance Decision Tree: Auto-generates work orders based on risk ratings
(e.g., C₂H₂>5μL/L & CO/CO₂>0.3→Triggers bushing looseness inspection)

III. Core Functional Matrix

IV. Technical Highlights

1. Multi-physics Coupling Analysis:
   EM-thermal-stress simulation data fed into AI models for early winding deformation alerts (±0.5mm precision)

2. Blockchain Certification:
   O&M records and test data stored on-chain for ISO 55000 compliance

3. AR-assisted Repair:
   Hololens overlays 3D fault-point positioning →40% faster critical repairs

V. Application Results (1,000MW Plant Case)