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.
Direct stability analysis of electric power systems using energy functions: theory, applications, and perspective,
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A diffusion-based generative ML paradigm is introduced to proactively generate and rank high-risk contingencies for voltage stability using physical information from operating points, with experiments on IEEE-6 to IEEE-118 systems.
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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 Diffusion-based Generative Machine Learning Paradigm for Dynamic Contingency Screening
A diffusion-based generative ML paradigm is introduced to proactively generate and rank high-risk contingencies for voltage stability using physical information from operating points, with experiments on IEEE-6 to IEEE-118 systems.