Air-Insulated Switchgear Voltage Transformer (AIS VT) Full Lifecycle Intelligent O&M Solution: O&M-Driven Transformation

Wone Store

2yrs + staff 1000+m² US$300,000,000+ China

Solution Overview:
Addressing the pain points of traditional AIS VT operation and maintenance (O&M) models, this solution leverages a three-tiered technological architecture – "Sensing & IoT - Digital Twin - Predictive Decision-Making" – to create an intelligent O&M closed loop spanning the entire equipment lifecycle. Core Objective: Replace experience-driven approaches with data-driven insights, shifting from reactive repairs to proactive prevention, achieving dual reductions in O&M costs and risks.

I. Confronting Traditional O&M Pain Points

High-Cost Periodic Testing: Relies on scheduled offline tests, consuming significant manpower, resources, and outage windows, resulting in persistently high overall maintenance costs.
Challenge of Sudden Insulation Failures: Traditional monitoring methods are lagging, unable to effectively detect insulation aging (e.g., moisture ingress, degradation) or latent defects (e.g., partial discharge). Difficult failure prediction leads to high risk of unplanned outages.

II. Innovative Intelligent O&M Architecture & Core Technologies

Intelligent Sensing Layer: Embedded IoT Condition Monitoring Module

Real-time Core Parameter Acquisition:

Dielectric Dissipation Factor (tanδ): Accurately monitors insulation aging state and moisture ingress trends – a core indicator of insulation health.
Partial Discharge (PD): High-frequency sensors capture faint discharge signals within or on the insulation surface to identify early-stage insulation defects.
Temperature (T): Real-time monitoring of critical point temperatures (e.g., windings, terminals) reflecting overload, poor contact, or abnormal cooling.

Features: Modular design, live-line installation, strong electromagnetic interference (EMI) immunity, high-frequency data sampling (to capture transient PD signals).

Intelligent Analytics Layer: AIS VT Digital Twin Platform

Multi-source Data Fusion: Integrates real-time sensor data, historical test reports, SCADA operational records, and equipment profile information.
Precise Remaining Useful Life (RUL) Prediction: Utilizes machine learning algorithms (e.g., LSTM, ensemble learning) to train multi-dimensional degradation models, achieving a <10% margin of error, visually quantifying the equipment's "remaining health lifespan."
3D Visualization & Health Assessment: Constructs a virtual replica of the device, dynamically displaying insulation status, hotspot distribution, and risk levels, supporting "one-click" diagnosis.

Intelligent Decision Layer: Predictive Maintenance Strategy Engine

Dynamic Inspection Optimization: Automatically adjusts inspection cycles and tasks based on real-time health scores output by the platform (e.g., extended intervals for healthy devices, targeted enhanced monitoring for sub-healthy devices), reducing ineffective inspections and lowering O&M manpower input by up to 30%.
Precision Maintenance Triggering: Generates maintenance work orders automatically based on RUL predictions and condition thresholds (e.g., tanδ surge alert prompting inspection, PD exceeding limits triggering urgent defect elimination), preventing both over-maintenance and under-maintenance.
Hierarchical Alarms & Decision Support: Defines parameter abnormality levels, pushing differentiated alerts (Warning / Alert / Critical); provides knowledge base support for fault location, root cause analysis, and corrective action recommendations.

III. Ideal Application Scenarios

Metropolitan Core Substations: Ensures extremely high power supply reliability requirements while reducing dependence on manpower-intensive O&M dispatching.
Renewable Energy Plant Step-up Substations (PV/Wind): Addresses challenges of unmanned operation in remote areas, enabling remote, refined equipment condition management.
Critical Transmission Nodes & Key Consumer Substations: Minimizes unplanned outage risks to the greatest extent, enhancing power supply continuity.

IV. Core Value & Advantages (Quantified Results)

Metric	Traditional Mode	This Intelligent O&M Solution	Improvement Effect
Annual O&M Cost	Baseline (100%)	Reduced by 35%	Significant Cost Savings
Mean Time To Repair (MTTR)	> 24 hours (Complex faults)	≤ 4 hours	>80% Efficiency Gain
Unplanned Outage Count	High	Significantly Reduced	Enhanced Reliability
Manpower Dependence	High	Reduced by ~30%	Optimized Resource Allocation
Failure Prediction Capability	Almost None	High Precision (RUL error <10%)	Proactive Risk Prevention & Control