AI & ML
Conflict-Aware Impedance-Sensitivity-Based Tuning for Multi-Mode Damping Enhancement
arXiv cs.SY0 views
arXiv:2611.05935v1 Announce Type: new Abstract: Impedance-based eigenvalue sensitivity provides a practical way to relate converter parameters to poorly damped system modes, but tuning becomes nontrivial when one parameter improves one target mode while degrading another. This paper introduces a conflict-aware tuning strategy for simultaneous damping enhancement of multiple modes. The method converts impedance-derived eigenvalue sensitivities into normalized damping-ratio sensitivities, diagnoses opposing modal tuning directions, and selects either one parameter or a coordinated pair at each iteration. A low-dimensional max--min problem maximizes the predicted damping of the worst target mode subject to parameter limits, protected-mode constraints, and implementation-specific feasibility requirements. Each accepted update is verified on the full system model and the sensitivities are recomputed. Studies on a four-bus system and a modified IEEE 14-bus system demonstrate the value of pairwise coordination, including a case in which single-parameter tuning stops before the predefined damping target is reached.
Source:https://arxiv.org/abs/2611.05935PreviousContinuous-Discrete Dual-Domain Co-Design for Optimized Digital Control of Power ConvertersNextAdaptive Grid-Mode-Aware Hybrid Energy Storage Control for AI Data Center Oscillation Mitigation
Related posts
Uniform Race: Parameter-Free Approximate Rejection Sampling
0arXiv stat.ML
Efficient Robust Learning at the Information-Theoretic Limit
0arXiv stat.ML
Algorithmic Principles For Multiclass Learning Are Hard To Come By: Limits of Regularization and Proper Learning
0arXiv stat.ML
More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations
0arXiv stat.ML
Fr\'echet Regression on the Bures-Wasserstein Manifold
0arXiv stat.ML
Decoupling Distance and Networks: Hybrid Graph Attention-Geostatistical Methods for Spatio-temporal Risk Mapping
0arXiv stat.ML