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Edge Intelligence Powers AIS CTs' Digital Transformation: Compatible with Traditional & Smart Grids for New Power Systems

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

I. Project Background
With the New Power System's escalating demands for dynamic monitoring accuracy, equipment compatibility, and data intelligence, traditional Air Insulated Switchgear (AIS) Current Transformers (CTs) urgently require digital transformation to achieve the following breakthroughs:

  • Compatibility Conflict:​ Need to simultaneously support both traditional analog substations and digital substations.
  • Accuracy Bottleneck:​ Insufficient PMU (Phasor Measurement Unit) synchronization accuracy restricting dynamic process analysis.
  • Data Deluge:​ Transmission of raw sampled values occupies over 90% of channel bandwidth.

II. Core Technical Solution

1. Dual-Mode Output Interface Architecture

Output Mode

Technical Parameters

Application Scenario

Traditional Analog

5A/1A, Accuracy Class 0.2S

Protection devices, mechanical meter access

Digital Output

IEC 61850-9-2 LE Sampled Values (SV), 4000 Hz

Merging Unit (MU), PMU centralized analysis

  • Innovation:​ Employs magnetically isolated dual-winding design, avoiding digital/analog signal interference, achieving full-scale error < ±0.1%.

2. Microsecond (μs) Level Time Synchronization System

3. Edge Computing Intelligent Terminal
* ​Hardware Configuration:
* Dual-core ARM Cortex-M7 processor @ 480MHz
* 128KB SRAM + 4MB Flash storage
* ​Local Analysis Functions:
* Harmonic Distortion Ratio (THD) calculation (±0.2% accuracy when THD ≤ 1.5%)
* Three-phase imbalance analysis (Response time < 20ms)
* Load waveform feature extraction (Compression ratio 7:1)
* ​Data Transmission Optimization:​ Only uploads feature data, reducing bandwidth usage by 70%.

III. Transformation Implementation Pathway

Three-Phase Implementation Plan for Digital Transformation

Phase

Activity

Timeline, Effort (Quarter-Person)

Device Layer Refit

Traditional CT Replacement

2025 Q1, 6qp

 

Optical Fiber Network Deployment

2025 Q2, 4qp

System Layer Upgrade

MU Data Access

2025 Q3, 3qp

 

Edge Computing Configuration

2025 Q4, 2qp

Advanced Applications

PMU Dynamic Monitoring

2026 Q1, 4qp

 

AI Load Forecasting

2026 Q2, 6qp

(qp = Quarter-Person effort unit)

IV. Technical and Economic Benefits

Indicator

Before Refit

After Refit

Improvement

Data Acquisition Dimensions

6 parameters

27+ features

350% Increase

PMU Synchronization Accuracy

10 μs

0.8 μs

12.5x Improvement

Data Transmission Volume

12 Mbps/unit

3.6 Mbps/unit

70% Reduction

Fault Diagnosis Response Time

300 ms

45 ms

85% Improvement

Investment Payback Calculation:

  • Single Bay Refit Cost:​ ¥48,000
  • Annual Maintenance Cost Savings:​ ¥18,000
  • Avoided Capacity Expansion Savings:​ ¥50,000 (3-year period)
  • Static Payback Period:​ 2.7 years < Target 3 years

V. Typical Application Scenarios

  1. Renewable Energy Plant Grid Connection Point
    • Captures second-level fluctuations in wind/solar power output.
    • Automatically triggers SVG (Static VAR Generator) upon harmonic violation.
  2. Urban Load Center Substation
    • Dynamic capacity expansion based on edge computing.
    • Identifies impulsive loads within 0.1 seconds.
  3. Traditional Substation Intelligent Refit
    • Preserves existing protection circuits.
    • Adds new optical fiber digital channels.
07/19/2025
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