Table 1.
Experimental equipment parameters.
| Category | Device/Component Name | Model/Version | Key Parameters |
|---|---|---|---|
|
Simulation Platform |
Real-Time Digital Simulator |
RTDS Technologies -RSCAD |
Simulation step size: 50 µs; Supports IEC 61,850 GOOSE/SV protocol; Maximum number of nodes: 512 |
| Real Device | IED |
Nari Relay Protection PCS-9611G |
ARM Cortex-A9, 1 GHz, 512 MB RAM; Supports DL/T 860; Response delay: <1 ms |
| IED |
XJ Electric WDH-821 |
PowerPC e500, 800 MHz, 256 MB RAM; Supports Modbus TCP; Response delay: <2 ms |
|
|
Edge Computing Node |
Edge Server |
NVIDIA Jetson AGX Orin |
ARM Cortex-A78AE, 2.2 GHz; 32 GB LPDDR5; Supports TEE (TrustZone) |
|
EEG Data Acquisition Device |
EEG System |
NeuroScan SynAmps2 |
Sampling rate: 1000 Hz; Number of channels: 64; Input impedance: <5 kΩ; Noise: <0.5 µV RMS |
|
Operating System |
Edge Node OS |
Ubuntu 22.04 LTS (Kernel 5.15) |
Real-time kernel patch PREEMPT_RT; eBPF support; SELinux policy enabled |
| IED Embedded OS | VxWorks 6.9 |
Hard real-time; Task switch delay: <10 µs |
|
|
Development Framework |
Machine Learning Framework |
PyTorch 2.1 + Scikit-learn 1.3 |
Number of LSTM layers: 2; Hidden units: 128; SVM kernel: RBF; Training batch size: 64 |
| Reinforcement Learning Framework | Ray RLlib 2.8 |
Algorithm: Double DQN; Experience replay capacity: 100,000; Exploration strategy: Boltzmann (τ = 1.0 → 0.1) |
|
| Data Acquisition | Performance Monitoring Probe |
Custom eBPF probe+Telegraf |
Sampling frequency: 1 kHz; Metrics collected: CPU load, memory usage, function call delay, and negative entropy |