Norvento Uses Passivity-Based Control to Balance Stability and Performance
Using Optimization-Based Tuning Workflows, Engineers Balanced Robust Stability and Dynamic Performance Under Uncertain Grid Conditions
“As power systems become increasingly dominated by inverter-based resources, designing controllers that simultaneously deliver robust stability and strong dynamic performance becomes significantly more challenging. MATLAB enabled us to formulate this problem as an optimization task and systematically identify the best tradeoff.”
Key Outcomes
- Automated controller parameter synthesis under uncertain grid conditions using an optimization-based tuning workflow
- Explicitly incorporated passivity-based robust-stability requirements and performance objectives into the controller design process
- Identified the optimal tradeoff between robust stability and dynamic performance for grid-forming converter control systems
Optimization-based controller tuning enabled Norvento to achieve an improved balance between robust stability and dynamic performance compared with classical control design approaches.
As power systems incorporate more inverter-based resources, grid-connected converters must maintain robust stability under uncertain grid conditions without sacrificing dynamic performance. For Norvento TECHnPower, achieving that balance through traditional control design methods involved manual gain tuning, trial-and-error iterations, and difficult tradeoffs between stability and performance.
Instead, Norvento adopted an optimization-based control design approach. Engineers used Control System Toolbox™, Simulink Control Design™, Robust Control Toolbox™, and Simscape Electrical™ to model the system and configure the controller design workflow. Using the systune command, they expressed stability requirements and performance objectives explicitly, enabling optimization algorithms to synthesize controller parameters automatically.
Norvento selected passivity as the robust-stability criterion because it is compatible with grid uncertainty, supports decentralized assessment, and is based on measurable physical quantities. By incorporating passivity directly into the optimization problem, engineers could evaluate robustness and dynamic performance within the same controller design workflow.
The resulting controller satisfied the passivity-based stability constraint and minimized fault current and reference-tracking error. Compared with classical tuning approaches, the workflow provided a systematic way to identify the best tradeoff between robust stability and dynamic performance for grid-forming converters.
