Spatial-temporal characterization of synchrophasor measurement systems — A big data approach for smart grid system situational awareness

An approach for fully characterizing a synchrophasor measurement system is proposed in this paper, which aims to provide substantial data volume reduction while keep comprehensive information from synchrophasor measurements in time and spatial domains. Specifically, the optimal synchrophasor sensor placement (OSSP) problem with the effect of zero-injection buses (ZIB) is modeled and solved to ensure the minimum number of installed sensors and also the full observability of the power system. After the sensors are optimally placed, the matching pursuit decomposition algorithm is used to extract the time-frequency features for full description of the time-domain synchrophasor measurements. To demonstrate the effectiveness of the proposed characterization approach, power system situational awareness is investigated on Hidden Markov Model (HMM) based fault detection and identification using the spatial-temporal characteristics generated from the proposed approach. Several IEEE standard systems such as the IEEE 14 bus system, IEEE 30 bus system and IEEE 39 bus system are employed to validate and evaluate the proposed approach.