A principal component pursuit (PCP)-based interface is proposed between raw synchrophasor data and the algorithms used for wide-area monitoring application to provide resilience against malicious data corruption. The PCP method-based preprocessor recovers a low rank matrix from the data matrix despite gross sparse errors originating from cyber-attacks by solving a convex program. The low-rank matrix consists of the basis vectors obtained from the system response and the sparse matrix represents corruption in each position of the data matrix. An augmented Lagrangian multiplier-based algorithm is applied to solve the PCP problem. The low rank matrix obtained after solving PCP represents the reconstructed data and can be used for estimation of poorly damped modes. A recursive oscillation monitoring algorithm is tested to validate the effectiveness of the proposed approach under both ambient and transient conditions.
All Science Journal Classification (ASJC) codes
- Computer Science(all)