GIS Voltage Transformer Intelligent Operation and Maintenance Solution: IoT-Based Predictive Maintenance System Haɓeji Don Yawan Gwaji Da Turanci A Ƙaikai Na GIS: Tattalin Yawan Gwaji Da Turanci A Ƙaikai Na IoT

1.Ayọ:
Voltage transformers (VTs) tuntun ni ẹka GIS ofin ni o nira ẹni lati ṣe iṣiro ti ọdun, eyi le mu awọn ẹnu agbara mẹ́ta pataki:

Ojoo si ẹ̀kọ́ àbẹ̀rò: Ẹka GIS ti o fi ẹ̀ka gas ṣe ati ẹ̀kọ́ àbẹ̀rò kankan (PD), ififọ́ SF6 gas ti o jẹ́ aláìlẹ̀, ati ipa ọ̀nà ti o dara si ẹ̀kọ́ àbẹ̀rò ló dàbí pé ó ní ẹ̀kọ́ àbẹ̀rò láti ràn mọ̀ ìwà tí ó ní àwọn ọ̀nà tó ṣe àtúnṣe tàbí tó ṣe àtúnṣe.
Ìwà ọ̀daràn: Àwọn ẹni ti o ní ẹ̀kọ́ àbẹ̀rò ló ní ọ̀nà tó ní ẹ̀kọ́ àbẹ̀rò dáadáa (ọdún/ọ̀wọ́rọ̀) èyí túmọ̀ sí pé àwọn ẹ̀kọ́ àbẹ̀rò bíi ìwà tó ṣe àtúnṣe tàbí tó ṣe àtúnṣe fún gas ló ń ṣẹlẹ̀ gan-an lórí àwọn ẹ̀kọ́ àbẹ̀rò, èyí túmọ̀ sí ìṣẹ̀lẹ̀ tí ó ní ẹ̀kọ́ àbẹ̀rò.
Ọrọ̀-ọ̀rọ̀ O&M: Àtúnṣe ẹ̀kọ́ àbẹ̀rò ati ìwádìí tuntun ló ní àwọn eniyan pataki ati àwọn ohun tí ó ní ẹ̀kọ́ àbẹ̀rò, nítorí pé ó ní ẹ̀kọ́ àbẹ̀rò tó ní àwọn ẹ̀kọ́ àbẹ̀rò tó ní ẹ̀kọ́ àbẹ̀rò tàbí tó ní ẹ̀kọ́ àbẹ̀rò.

2. Ìwádìí: IoT-Based Predictive Maintenance System
Láti mú ìrànlọ́wọ́ wọ̀nyí, ìwádìí yìí ń ṣe àwọn ẹ̀kọ́ àbẹ̀rò tó ní ẹ̀kọ́ àbẹ̀rò GIS-VTs:

(1) Comprehensive Sensing Layer:

Precision Deployment: Fí àwọn ẹ̀kọ́ àbẹ̀rò tó ní ẹ̀kọ́ àbẹ̀rò pàtàkì (ẹni bíi 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

Indicator	Improvement	Realized Value
Equipment Reliability	≥40% reduction in sudden failure rate	Prevents major outages, ensures grid backbone stability
O&M Efficiency	35% reduction in unplanned repair orders	Staff focus on critical areas, efficiency multiplied
O&M Costs	≥25% reduction in overall O&M costs	Reduces ineffective inspections & over-maintenance, optimizes spare parts inventory
Equipment Availability	≥99.9% annual comprehensive availability	Supports grid's high power supply reliability targets
Decision Making	Data-driven precision decisions	Transitions from "scheduled maintenance" to "precision maintenance", extends equipment life

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.

07/11/2025

Tambayar Da Yawanci