Robust Subspace Approaches for Analyzing Incomplete Synchrophasor Measurements

Synchrophasor measurements can significantly enhance the monitorability of the power grid by revealing the dynamics of grid operation. However, due to high-rate samples collected in large volume, big data challenges emerge to efficiently process the data. The present work advocates robust subspace approaches including robust principal component analysis and subspace clustering, to identify low-dimensional structures in the synchrophasor data, even when portions of measurements are missing due to sensor or network issues, and outliers are present in the data. The outliers can model abnormal dynamics or cyber-attacks in the grid. Numerical tests using simulated synchrophasor data illustrate the utility of the approaches.