Precise Recovery of Corrupted Synchrophasors Based on Autoregressive Bayesian Low-Rank Factorization and Adaptive K-Medoids Clustering

Phasor measurement unit (PMU) data quality problems, such as data loss and modification caused by communication contingencies and cyber attacks, threaten the reliability and stability of modern power systems. In this paper, autoregressive Bayesian low-rank matrix/Hankel tensor factorization (BLMF/BLTF) algorithms are proposed to recover corrupted synchrophasor measurements by leveraging the latent electrical-temporal correlations between data points. To mitigate the impact of consecutive data anomalies on recovery, adaptive k-Medoids clustering based on dynamic time wrapping (DTW) distance and Canopy clustering is introduced to preprocess raw data. The effectiveness of the proposed algorithms in recovering missing or modified synchrophasor measurements is demonstrated through comprehensive case studies. Compared with previous works, the proposed algorithms rely on simpler prior hypotheses and can achieve higher recovery precision. Rank reduction and low-cost rolling prediction techniques make the proposed algorithms suitable not only for processing offline data blocks but also for online real-time recovery of PMU data streams.

Automated summary

This paper proposes autoregressive Bayesian low-rank matrix/Hankel tensor factorization algorithms to address the problems of PMU data quality. The effectiveness of the algorithms is demonstrated through comprehensive case studies, showing higher recovery precision and suitability for processing offline data blocks and online real-time recovery.