Classification of PQ Disturbances in Induction Motor using Neuro-Fuzzy

Authors

  • Sridhar S Department of Electrical and Electronics Engineering, RNS Institute of Technology, VTU, Bangalore,560098 ,india.
  • Uma Rao K Department of Electrical and Electronics Engineering, R. V. College of Engineering, VTU, Bangalore,560059 India
  • Raksha Umesh Department of Electrical and Electronics Engineering, RNS Institute of Technology, VTU, Bangalore,560098 ,india.
  • Harish K. S Department of Electrical and Electronics Engineering, RNS Institute of Technology, VTU, Bangalore,560098 ,india.

DOI:

https://doi.org/10.37591/ctsp.v6i2.4928

Abstract

This paper presents a methodology of classification of PQ disturbances in the supply to induction motor using ANFIS. Wavelet transform is applied to the stator currents for the extraction of the signature indicating the variations in the supply. These wavelet coefficients are fed as input to ANFIS. This data has been divided into two sets: 37 training data set and 38 testing data set. The training data set has been used to train different adaptive neuro-fuzzy inference system (ANFIS) models and testing data set has been used to validate the models. The simulation is done using MATLAB/SIMULINK. The proposed network has performance efficiency of 93.34%.

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Published

2021-01-11

Issue

Vol. 6 No. 2 (2016)

Section

RESEARCH ARTICLES