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
|
Module |
Core Tech |
Function |
|
Condition Assessment |
DBN (Deep Belief Network) |
Integrates SCADA/online data to generate health indices |
|
Fault Warning |
LSTM Time-Series Analysis |
Predicts hotspot trends based on temperature/load rates |
|
Life Prediction |
Weibull Distribution |
Quantifies insulation paper degradation curves |
(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
|
Function |
Technical Implementation |
O&M Value |
|
Panoramic Monitoring |
Edge-computing gateways (10ms data acquisition) |
100% device status visualization |
|
Smart Diagnostics |
IEEE C57.104 + AI correction |
92% fault identification accuracy |
|
Predictive Maintenance |
RUL prediction via degradation modeling |
25% lower maintenance costs |
|
Knowledge Retention |
Self-iterating fault case database |
60% faster new staff training |
IV. Technical Highlights
- Multi-physics Coupling Analysis:
EM-thermal-stress simulation data fed into AI models for early winding deformation alerts (±0.5mm precision) - Blockchain Certification:
O&M records and test data stored on-chain for ISO 55000 compliance - AR-assisted Repair:
Hololens overlays 3D fault-point positioning → 40% faster critical repairs
V. Application Results (1,000MW Plant Case)
|
Metric |
Pre-upgrade |
Post-upgrade |
Improvement |
|
Unplanned Outages |
3.2/yr |
0.4/yr |
↓87.5% |
|
Avg. Repair Time |
72 hrs |
45 hrs |
↓37.5% |
|
Life Prediction Error |
±18 months |
±6 months |
↑67% accuracy |
