Is it possible for Agentic AI to perform OPAL-RT simulations?
Yes—agentic AI can be used to operate and automate OPAL-RT simulations, but it should act as an orchestration and analysis layer rather than replace the deterministic real-time simulator.
A practical architecture would be:
AI agent interprets the objective
For example: “Run a sweep of load resistance from 5 Ω to 50 Ω and identify the point where the DC-link voltage becomes unstable.”
Deterministic automation layer executes the workflow
A Python, MATLAB, LabVIEW, C++, or TestStand application can:
Load and compile the RT-LAB model
Start, stop, reset, and monitor execution
Set exposed model signals or runtime parameters
Run test sequences and parameter sweeps
Collect logs, scopes, and measured signals
Generate pass/fail results
RT-LAB documentation describes a comprehensive API for custom online applications and dynamic modification of model signals or parameters during execution. https://opal-rt.atlassian.net/wiki/spaces/RD/pages/45880058/RT-LAB+User+Guide+Introduction
The AI agent analyzes the results
The agent can detect violations, compare results against requirements, identify anomalies, propose the next test, and produce a report.
Safety and authorization gates remain in place
For HIL or power-HIL systems, the agent should not be allowed to arbitrarily change hardware-facing signals, protection thresholds, or operating conditions without limits and approval. The execution layer should enforce:
Allowed parameter ranges
Rate limits on changes
Emergency-stop handling
Hardware interlocks
Test approval and audit logging
A defined safe state after communication loss