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Lifecycle Cost Reduction for AIS CTs: State-Monitoring-Driven Intelligent O&M Strategy

I. Pain Point Analysis
Traditional Air Insulated Switchgear (AIS) Current Transformer (CT) operations face three key challenges:

  1. Latent Failures Undetectable:​ 73% of CT failures originate from terminal overheating or partial discharge, which conventional inspections fail to capture in real-time.
  2. Excessive Maintenance Costs:​ Fixed planned maintenance cycles (every 5 years) result in over 30% of unnecessary maintenance expenditure.
  3. Delayed Spare Part Response:​ Spare part procurement lead times reach 30 days, causing average outage losses of $18,000/hour.

II. Core Technical Solutions
​**▶ Solution 1: Wireless Temperature Monitoring + Partial Discharge (PD) Integrated Diagnostic System**​

Component

Implementation Points

Passive RFID Sensors

High-temperature resistant (150°C) sensors embedded at CT terminals, transmitting temperature data every 10s (±0.5°C accuracy).

Intelligent Diagnosis Link

Automatic IR thermography scanning triggered when temperature >85°C, with AI identifying PD hotspots (sensitivity ≤2pC).

Data Transmission

LoRaWAN wireless network + edge computing gateways, ensuring data return latency <200ms.

​**▶ Solution 2: LSTM Life Prediction Model
Training Data:​ 10-year historical O&M data (12 dimensions including temperature, PD, load rate).
Prediction Accuracy:​ Validation set MAE=6.8 days (95% CI ±7 days).
Maintenance Decision:​**​ Auto-triggered alert when lifespan degradation exceeds 80%.

​**▶ Solution 3: Modular 3D-Printed Spare Parts Library**​

III. Cost-Benefit Quantification

Metric

Traditional Approach

Our Solution

Optimization

Annual O&M Cost

$42,000/unit

$23,100/unit

↓45%

Unplanned Outage Frequency

2.3 times/year

0.46 times/year

↓80%

Maintenance Cycle

60 months

96 months

↑60% (Extension)

Avg. Failure Recovery Time

720 hours

76 hours

↓89%

IV. Implementation Roadmap

  1. Phase 1 (0-6 Months):
    o Deploy 500 CT temperature monitoring nodes (100% coverage).
    o Establish historical database (ISO 55000 standardized data).
  2. Phase 2 (7-12 Months):
    o LSTM model training & validation (accuracy >92%).
    o Commission regional 3D printing center (200km coverage radius).
  3. Phase 3 (13+ Months):
    o Implement predictive maintenance closed-loop system (AI-powered work order automation).
    o Reduce spare parts inventory by 70% (safety stock ≤5 units).

V. Risk Control
• ​EMC:​ Sensors certified per IEC 60255-22 for electromagnetic compatibility.
• ​Model Drift:​ Quarterly incremental training (with data decay compensation algorithm).
• ​Material Strength:​ 3D-printed components type-tested per DL/T 725-2023.

Conclusion:​ This solution establishes a "Condition Awareness → Intelligent Prediction → Rapid Response" closed-loop system, transforming AIS CT O&M from a cost center to a value creation center. Achieves 267% lifecycle ROI.

Note:​ Validated for AIS substations at 110kV or higher voltage levels. Increases overall availability rate to 99.998%.

07/19/2025
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