Online identification of bad synchrophasor measurements via spatio-temporal correlations

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.