High-dimensional data analysis by exploiting low-dimensional models with applications in synchrophasor data analysis in power systems
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Authors
Gao, Pengzhi
Issue Date
2017-12
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Electrical engineering
Alternative Title
Abstract
We also consider recovering PMU data from quantized and partially corrupted measurements. The data recovery is achieved through solving a constrained maximum likelihood estimation problem that exploits the low-rank property of the actual measurements. The recovery error is proven to be order-optimal and decays in the same order as that of the state-of-the-art method when no corruption exists. The data accuracy is thus maintained while the data privacy is enhanced. A new application of this method for data privacy in power systems is discussed. Experiments on synthetic data and real synchrophasor data in power systems demonstrate the effectiveness of our method.
Description
December 2017
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY