The SONYC project is a smart cities initiative focused on developing a cyber-physical system (CPS) for the monitoring, analysis and mitigation of urban noise pollution. Noise pollution is one of the topmost quality of life issues for urban residents in the U.S. with proven effects on health, education, the economy, and the environment.

In this NSF project, the team proposes to innovate a novel printing scheme that can embed piezoelectric transducers (namely, sensor/actuator coupled elements) into layered composites. As the transducers are densely distributed throughout the entire structure, they function like a nerve system embedded into the structure. Such a sensor nerve system, when combined with new control and command systems and advanced data and signal processing capability, can fully unleash the latest computing power to pinpoint the fault location.

The overarching goal is to employ a Wide-Area measurement-aided supervisory layer for correcting maloperation in relays, which can prevent system-wide blackouts. PSU’s task in this project is to ensure distinction between disturbance outlier and anomalous outlier in such Wide-Area measurements. This poster presents a Principal Component Analysis (PCA)-based method for online characterization of outliers in Wide-Area synchrophasor measurements. To that end, a linearized framework is established to analyze dynamical response from a system under nominal and off-nominal (e.g.

ABSTRACT

ABSTRACT