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AI Used to Detect Anomalies in Power Grid Failures

Finding a fault in the power grid of a country can be like attempting to locate a needle in a huge haystack. ....

United States , Jie Chen , Enyan Dai , Megan Craig , International Conference On Learning Representations , Watson Ai Lab , Pennsylvania State University , Research Staff Member , Us Department Of Energy , Intelligence Used , Detect Anomalies , Power Grid , Senior Study Author , Pennsylvania State , International Conference , Graph Augmented Normalizing Flows , Anomaly Detection , Multiple Time Series ,

"AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstat" by Michael Bertolacci, Ori Rosen et al.

We present the AdaptSPEC-X method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incorporate multiple time series, with mixture components parameterized by a time-varying mean and log spectrum. The mixture components are based on AdaptSPEC, a nonparametric model which adaptively divides the time series into an unknown number of segments and estimates the local log spectra by smoothing splines. AdaptSPEC-X extends AdaptSPEC in three ways. First, through the infinite mixture, it applies to multiple time series linked by covariates. Second, it can handle missing values, a common feature of time series which can cause difficulties for nonparametric spectral methods. Third, it allows for a time-varying mean. Through these extensions, AdaptSPEC-X can estimate time-varying means and spectra at observed and unobserved covariate values, allowing for predictive inference. Estimation is per ....

United States , Monte Carlo , Hamiltonian Monte Carlo , Locally Stationary Time Series , Multiple Time Series , Eversible Jump Markov Chain Monte Carlo , Hittle Likelihood ,