GIS Voltage Transformer Intelligent Operation and Maintenance Solution: IoT-Based Predictive Maintenance System

1.Challenges:
Traditional voltage transformers (VTs) within GIS equipment often require high-frequency manual inspections, presenting three core pain points:

• Delayed Detection of Potential Failures: The enclosed gas-insulated structure (GIS) makes early fault indicators like internal partial discharge (PD), minor SF6 gas density drops, and abnormal temperature rises difficult to visually detect or find via conventional methods.

• Low Response Efficiency: Long manual inspection cycles (weeks/months) mean sudden failures like insulation breakdown or gas leaks often occur without warning, leading to unplanned outages.

• High O&M Costs: Preventive testing and routine maintenance consume significant manpower and resources, with risks of both over-maintenance and under-maintenance.

2. Solution: IoT-Based Predictive Maintenance System
Addressing these challenges, this solution establishes an intelligent monitoring network covering the entire lifecycle of GIS-VTs:

(1) Comprehensive Sensing Layer:

• Precision Deployment: Embed/attach high-precision sensors to key VT nodes (e.g., high-voltage connections, near spacers, gas compartment body):

• Partial Discharge (PD) Sensors: High-frequency CT or Ultra-High-Frequency (UHF) sensors detect real-time insulation degradation signals.

• Gas Density & Moisture Sensors: Continuously track changes in SF6 gas pressure, density, and moisture content.

• Temperature Sensors: Monitor abnormal temperature rise points at conductor connections and enclosures.

• Reliable Transmission: Sensor data is transmitted in real-time via device-embedded IoT gateways using industrial-grade wireless/fiber optic networks to a cloud monitoring platform, ensuring data timeliness and integrity.

(2) AI-Powered Analytics Platform:

• Big Data Fusion: The platform integrates real-time monitoring data with multi-dimensional information such as historical operation/maintenance records, fault databases of similar equipment, and environmental conditions (load, temperature).

• AI Diagnostic Engine:

• Feature Extraction: Automatically identifies PD patterns (e.g., floating discharges, surface discharges), gas leakage trend curves, and temperature anomaly correlation maps.

• Deep Learning Prediction: Employs algorithms like LSTM and Random Forest to build fault prediction models, quantitatively assessing component health indices (HI) and remaining useful life (RUL).

• Precise Early Warning: Predicts critical failures like "insulator surface discharge degradation" or "gas micro-leakage due to seal ring aging" at least 7 days in advance, with an early warning accuracy rate exceeding 92%.

(3) Visualized O&M Dashboard:

• Panoramic Visualization: Provides multi-level (GIS equipment, bay, individual VT) health status overviews, supporting one-stop management of asset records, real-time data, historical trends, and alarm information.

• Intelligent Work Order Dispatch: Generates and dispatches precise maintenance work orders based on warning levels and prediction results (e.g., "Phase A VT: Recommend PD retesting and seal inspection within 3 days"), optimizing resource allocation.

• Knowledge Accumulation: Automatically generates fault analysis reports, continuously builds an O&M knowledge base, and drives model optimization.

3. Key Benefits

4. Reference Case

• 500kV Hub Substation GIS Equipment Cluster: Following system deployment, successfully provided early warnings for 3 potential VT insulation faults (2 floating discharges, 1 gas compartment seal anomaly), with lead times of 8-14 days, averting significant economic losses. Annual maintenance costs reduced by 28%, and equipment forced outage frequency dropped to zero.