Solusyon sa Maingon nga Pagpamahala ug Pagsiguro alang sa Power Generation Transformers
Ⅰ. Background and Pain Points
Tumong ang mga kompanya sa paggawa ng kuryente ug ang pag-usbong sa grid intelligence, ang tradisyonal nga mga modelo sa pagmantain sa regular na panahon mao nang dili na makapugos sa O&M demands sa dako nga mga transformers:
• Delayed Fault Response: Ang biglang paglubak sa insulation o pag-init dili mahatagan og real-time detection
• High Maintenance Costs: Ang over-maintenance nagpapangutana sa resources, samtang ang insufficient maintenance nagresulta sa unplanned downtime
• Fragmented Data Analysis: Ang isolated data gikan sa DGA (Dissolved Gas Analysis), partial discharge tests, etc., walay intelligent cross-diagnosis
II. System Architecture and Core Technologies
(1) Intelligent Sensing Layer
Nagdeploy og 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 |
