TY - GEN
T1 - Online identification of bad synchrophasor measurements via spatio-temporal correlations
AU - Wu, Meng
AU - Xie, Le
N1 - Funding Information:
This work is supported in part by National Science Foundation ECCS- 1150944, DGE-1303378, and in part by Power Systems Engineering Research Center.
Publisher Copyright:
© 2016 Power Systems Computation Conference.
PY - 2016/8/10
Y1 - 2016/8/10
N2 - 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.
AB - 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.
KW - Bad data detection
KW - data mining
KW - data quality improvement
KW - local outlier factor
KW - synchrophasor
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U2 - 10.1109/PSCC.2016.7540950
DO - 10.1109/PSCC.2016.7540950
M3 - Conference contribution
AN - SCOPUS:84986608444
T3 - 19th Power Systems Computation Conference, PSCC 2016
BT - 19th Power Systems Computation Conference, PSCC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th Power Systems Computation Conference, PSCC 2016
Y2 - 20 June 2016 through 24 June 2016
ER -