Identification of internal faults in indirect symmetrical phase shift transformers using ensemble learning

Pallav Kumar Bera, Rajesh Kumar, Can Isik

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

Abstract

This paper proposes methods to identify 40 different types of internal faults in an Indirect Symmetrical Phase Shift Transformer (ISPST). The ISPST was modeled using Power System Computer Aided Design (PSCAD)/ Electromagnetic Transients including DC (EMTDC). The internal faults were simulated by varying the transformer tapping, backward and forward phase shifts, loading, and percentage of winding faulted. Data for 960 cases of each type of fault was recorded. A series of features were extracted for a, b, and c phases from time, frequency, time-frequency, and information theory domains. The importance of the extracted features was evaluated through univariate tests which helped to reduce the number of features. The selected features were then used for training five state-of-the-art machine learning classifiers. Extremely Random Trees and Random Forest, the ensemble-based learners, achieved the accuracy of 98.76% and 97.54% respectively outperforming Multilayer Perceptron (96.13%), Logistic Regression (93.54%), and Support Vector Machines (92.60%).

Original languageEnglish (US)
Title of host publication2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538675687
DOIs
StatePublished - Jul 2 2018
Event2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018 - Louisville, United States
Duration: Dec 6 2018Dec 8 2018

Publication series

Name2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018
Volume2019-January

Conference

Conference2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018
CountryUnited States
CityLouisville
Period12/6/1812/8/18

Keywords

  • Ensemble Learning
  • Fault Identification
  • Phase Shift Transformer

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science Applications
  • Computer Networks and Communications

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