DUGi

Performance Comparison of Quantitative Methods for PMU Data Event Detection with Noisy Data

http://dugi.udg.edu/item/http:@@@@hdl.handle.net@@10256@@18570

Comunicació de congrés presentada a: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), organitzat per l’IEEE Power & Energy Society (PES) i la Delft University of Technology (Països Baixos), del 26 a 28 d’octubre de 2020. https://ieee-isgt-europe.org/

This article compares distinct signal-based and knowledge-based approaches often applied to process and detect events in vast amounts of data collected by phasor measurement units (PMU). The computation times and the accuracy of correct event detections are tested and evaluated in a 1-hour data file from the UT-Austin Independent Texas Synchrophasor Network with phasor quantities plus an additive noise gathered at different PMU substations. A sliding time window is considered to build a representative model of the system operating conditions on the fly and search for power system phenomena as soon as new data are available

This research was supported by the European Union’s Horizon 2020 research and innovation programme, call LCE-01-2016-2017, under the auspices of the project “Renewable penetration levered by Efficient Low Voltage Distribution grids”, grant agreement number 773715, and University of Girona scholarship

IEEE