Online bad data outlier detection in PMU measurements using PCA feature-driven ANN classifier

Kaveri Mahapatra, Nilanjan Ray Chaudhuri, Rajesh Kavasseri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper presents a method for online bad data outlier detection in PMU measurements. High resemblance of bad data in the form of outliers with that of system disturbances can cause control centers to take false decisions. The proposed method utilizes artificial neural network (ANN) in association with principal component analysis (PCA)-based feature extraction technique in order to classify bad data outliers and outliers caused by disturbances like faults. The extracted features using PCA from the reduced dimensional representation of the data is given as input to ANN. Bayesian regularization back-propagation-based learning algorithm is used for training ANN. The test results demonstrate online classification/detection of bad data of different amplitudes injected in measurements, both before and after disturbance, in two example power systems.

Original languageEnglish (US)
Title of host publication2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PublisherIEEE Computer Society
Pages1-5
Number of pages5
ISBN (Electronic)9781538622124
DOIs
StatePublished - Jan 29 2018
Event2017 IEEE Power and Energy Society General Meeting, PESGM 2017 - Chicago, United States
Duration: Jul 16 2017Jul 20 2017

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2018-January
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Other

Other2017 IEEE Power and Energy Society General Meeting, PESGM 2017
CountryUnited States
CityChicago
Period7/16/177/20/17

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All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

Cite this

Mahapatra, K., Chaudhuri, N. R., & Kavasseri, R. (2018). Online bad data outlier detection in PMU measurements using PCA feature-driven ANN classifier. In 2017 IEEE Power and Energy Society General Meeting, PESGM 2017 (pp. 1-5). (IEEE Power and Energy Society General Meeting; Vol. 2018-January). IEEE Computer Society. https://doi.org/10.1109/PESGM.2017.8273997