Learning geotemporal nonstationary failure and recovery of power distribution

Smart energy grid is an emerging area for new applications of machine learning in a nonstationary environment. Such a nonstationary environment emerges when large-scale failures occur at power networks because of external disruptions such as hurricanes and severe storms. Power distribution networks lie at the edge of the grid, and are especially vulnerable to external disruptions. Quantifiable approaches are lacking and needed to learn nonstationary behaviors of large-scale failure and recovery of power distribution. This paper studies such nonstationary behaviors in three aspects. First, a novel formulation is derived for an entire life cycle of large-scale failure and recovery of power distribution. Second, spatial-temporal models of failure and recovery of power distribution are developed as geolocation-based multivariate nonstationary GI(t)/G(t)/∞ queues. Third, the nonstationary spatial-temporal models identify a small number of parameters to be learned. Learning is applied to two real-life examples of large-scale disruptions. One is from Hurricane Ike, where data from an operational network is exact on failures and recoveries. The other is from Hurricane Sandy, where aggregated data is used for inferring failure and recovery processes at one of the impacted areas. Model parameters are learned using real data. Two findings emerge as results of learning: 1) failure rates behave similarly at the two different provider networks for two different hurricanes but differently at the geographical regions and 2) both the rapid and slow-recovery are present for Hurricane Ike but only slow recovery is shown for a regional distribution network from Hurricane Sandy.