A data-driven LMI criterion extracted from input-state trajectories computes optimal output differential passivity indices to certify distributed stability in power systems via convex SDP.
Foundations and challenges of low-inertia systems
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Introduces DeePConverters that apply data-enabled predictive control to achieve data-driven, optimal, robust, and adaptive operation of grid-connected power converters without relying on explicit grid models.
Neural networks learn dissipativity matrices from data to create a model-free controller that improves transient stability in all-VSG power systems.
Synchronous condensers can improve angular, frequency and voltage stability in inverter-dominated grids, but their location is critical to prevent destabilizing interactions.
citing papers explorer
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Data-Driven Distributed Stability Certification for Power Systems via Input-State Trajectories
A data-driven LMI criterion extracted from input-state trajectories computes optimal output differential passivity indices to certify distributed stability in power systems via convex SDP.
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A Data-Driven Optimal Control Architecture for Grid-Connected Power Converters
Introduces DeePConverters that apply data-enabled predictive control to achieve data-driven, optimal, robust, and adaptive operation of grid-connected power converters without relying on explicit grid models.
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Model-Free Power System Stability Enhancement with Dissipativity-Based Neural Control
Neural networks learn dissipativity matrices from data to create a model-free controller that improves transient stability in all-VSG power systems.
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Synchronous Condensers: Enhancing Stability in Power Systems with Grid-Following Inverters
Synchronous condensers can improve angular, frequency and voltage stability in inverter-dominated grids, but their location is critical to prevent destabilizing interactions.