Robust Recovery of PMU Signals with Outlier Characterization and Stochastic Subspace Selection

This paper proposes an improvement on the standalone robust principal component analysis (R-PCA)-based approach for recovering clean signals from corrupted synchrophasor measurements. The contributions of this paper are twofold. First, a kernel principal component analysis (K-PCA)-based metric is proposed for detecting and differentiating event-induced outliers from spurious outliers in data, which is then used as an indicator to suspend R-PCA in the event window to minimize the overall error in signal recovery. Second, a formal approach based on the recursive Bayesian framework is proposed for selecting the most appropriate subspace from a library of subspaces to be used by R-PCA. The paper combines the ideas of robust signal recovery, corruption-resilient event outlier detection, and stochastic subspace selection into a composite approach for correcting anomalies in synchrophasor data. The effectiveness of the proposed methodology is validated on simulated data from IEEE 16-machine, 5-area test system.

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Work Title Robust Recovery of PMU Signals with Outlier Characterization and Stochastic Subspace Selection
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Open Access
Creators
  1. Kaustav Chatterjee
  2. Kaveri Mahapatra
  3. Nilanjan Ray Chaudhuri
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. IEEE Transactions on Smart Grid
Publication Date July 1, 2020
Publisher Identifier (DOI)
  1. https://doi.org/10.1109/TSG.2019.2961561
Deposited November 16, 2021

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  • Added Creator Kaustav Chatterjee
  • Added Creator Kaveri Mahapatra
  • Added Creator Nilanjan Ray Chaudhuri
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