Risk Assessment for Complex Networks
The fluctuating power output from a wind farm would induce distinctive magnitudes of resonant frequency fluctuations at different nodes in an AC power grid (Zhang et al., Sci. Adv. 2019). Due to the stochasity of the driving signal, the nodal frequency fluctuations are also stochastic thus the specific time series is hard to predict a priori. However, once the frequency deviation exceeds the safety range, it would lead to the damage of power grid components thus undermine the overall grid stability. Can we quantify such systematic risk, i.e. which nodes would be most vunderable under fluctuating driving signals by exhibiting the strongest responses?
We proposed the dynamic vulnerability index (DVI) to identify which nodes in a complex network would potentially exhibit the largest all-time-high responses to stochastic driving signals with a given power spectrum (Zhang et al., Chaos 2020).