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

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)

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