6533b831fe1ef96bd1298737

RESEARCH PRODUCT

Event signal characterization for disturbance interpretation in power grid

Kjetil UhlenNand KishorRicha NegiSanjay Singh Negi

subject

Signal processingWaveletStationary processComputer sciencebusiness.industryKurtosisPattern recognitionMel-frequency cepstrumArtificial intelligencebusinessSignalHilbert–Huang transformEvent (probability theory)

description

This paper presents the signal processing approach to detect and characterize the physical events that occur in power system using PMUs signals. A small window is applied so that the extracted spectral features belong to a stationary signal. This is based on applying empirical mode decomposition, followed by square root of spectral kurtosis (SRSK) for computation of statistical indices to indicate the event occurrence. Subsequently, features from these events are extracted using mel frequency cepstral coefficients on SRSK. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

10.23919/smagrimet.2018.8369844https://hdl.handle.net/11250/2598553