title: >-
Eigenvalue early warning for power grid stability — the physical basis AI
transfer-learning models learn to detect
emoji: 📚
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sdk: static
pinned: false
license: mit
short_description: Eigenvalue early warning for power grid stability — the phys
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
TaiScience CSD-EWS is an educational simulator for critical slowing down (CSD) as an early-warning signal in power grid stability. It visualizes how system eigenvalues (poles) approach the stability boundary, using a Jacobian eigenvalue proxy and damping-ratio thresholds.
This work is part of the TokaMind transfer-learning framework, which adapts a tokamak-plasma foundation model to power-grid dynamics. While Microsoft's GridSFM addresses static AC-OPF feasibility, this system targets dynamic CSD precursors — complementary directions for grid security analysis, relevant to cascading-failure events such as the 2025 Iberian blackout and the 2016 South Australia blackout.
What It Shows
- Complex s-plane map — conjugate pole pair (s = σ ± jω) with real-time position relative to the stability boundary (Re = 0)
- Damping ratio — ζ = −σ / √(σ² + ω²), with a 5% threshold separating adequate damping from warning and danger zones
- Time-domain response — illustrative y(t) = e^(σt)·cos(ωt) showing how a near-critical mode decays or diverges
Core Concept
Critical slowing down is a generic precursor to instability across dynamical systems: as a control parameter pushes a system toward a bifurcation, its dominant eigenvalue's real part approaches zero, recovery from perturbations slows, and damping collapses. In power systems, detecting this slowing before a mode crosses into the right-half plane provides an early-warning window for operators.
The full CSD detection pipeline (time-domain Jacobian proxy + spectral LFPR engine) is released as open source: https://github.com/chihsingwu/CSD-dual-engine
Use Case
Teaching and intuition-building for power-system stability monitoring, transient stability assessment (TSA), and PMU-based early-warning research. This page is a manual teaching demo — not a live PMU or Toeplitz identification system. The waveform shows e^(σt)·cos(ωt) only, without modal amplitude or phase.
Related Work
- TaiScience Research Group (Fu Jen Catholic University): https://taiscience.org
- StromaPath platform: https://stromapath.com
- TokaMind PMU transfer-learning preprint: https://arxiv.org/abs/2605.11033
- CSD dual-engine (open source): https://github.com/chihsingwu/CSD-dual-engine
Keywords
critical slowing down · power grid early warning system · Jacobian eigenvalue proxy · damping ratio monitoring · transient stability assessment · PMU · TokaMind transfer learning · s-plane pole map