Online Identification of Bad Synchrophasor Measurements via Spatio-temporal Correlations Conference Paper

abstract

• 2016 Power Systems Computation Conference. In order to obtain high-quality synchrophasor data prior to further power system applications such as state estimation and dynamic security assessment, this paper proposes an online data-driven algorithm to identify low-quality synchronphasor measurements caused by either physical instrumentation errors or intentional malicious attacks. The algorithm applies density-based local outlier factor (LOF) analysis and identify low-quality synchronphasor measurements which exhibit an outlier pattern of spatio-temporal correlation. The benefits of the proposed algorithm include: 1) it has fast computation performance, which is desirable for online application; 2) it is capable of identifying low-quality synchrophasor measurements during both normal and eventful operating conditions; 3) it is purely data driven, without involving any knowledge on network parameters or topology, which avoids the impact of parameter/topology errors on detection results.

name of conference

• 2016 Power Systems Computation Conference (PSCC)

authors

• Xie, Le

published proceedings

• 2016 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC)

author list (cited authors)

• Wu, M., & Xie, L. e.

citation count

• 9

complete list of authors

• Wu, Meng||Xie, Le

publication date

• June 2016

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Bad Data Detection
• Data Mining
• Data Quality Improvement
• Local Outlier Factor
• Synchrophasor

Digital Object Identifier (DOI)

• 10.1109/pscc.2016.7540950

International Standard Book Number (ISBN) 13

• 9788894105124

start page

• 1

end page

• 7

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fpscc.2016.7540950