A Neural Network based power quality signal classification system using wavelet energy distribution

Praveen Sebastian, Pramod Antony Ds̈a

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

1 Citation (Scopus)

Abstract

This paper presents a method for the classification of common Power Quality(PQ) events. The described system for the characterization of disturbances is based on wavelet based feature extraction. The amount of data to be analyzed and how the data can be interpreted are of crucial importance in power quality analysis. Wavelet Transform(WT) has been widely used in power quality signal analysis. The advantage of wavelet transform is it can provide precise time information of power quality events and has many advantages over traditional signal analysis approaches. In this paper Discrete Wavelet Transform(DWT) is used for obtaining the energy distribution from simulated signals. The system is developed with Neural Network which is an effective tool in classification of signals in power systems.

Original languageEnglish
Title of host publicationProceedings of IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages199-204
Number of pages6
ISBN (Electronic)9781479982806
DOIs
Publication statusPublished - 01-01-2015
Externally publishedYes
EventIEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015 - Kollam, India
Duration: 24-06-201526-06-2015

Conference

ConferenceIEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015
CountryIndia
CityKollam
Period24-06-1526-06-15

Fingerprint

Power quality
Neural networks
Signal analysis
Wavelet transforms
Discrete wavelet transforms
Feature extraction

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Sebastian, P., & Ds̈a, P. A. (2015). A Neural Network based power quality signal classification system using wavelet energy distribution. In Proceedings of IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015 (pp. 199-204). [7229617] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAPENERGY.2015.7229617
Sebastian, Praveen ; Ds̈a, Pramod Antony. / A Neural Network based power quality signal classification system using wavelet energy distribution. Proceedings of IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 199-204
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Sebastian, P & Ds̈a, PA 2015, A Neural Network based power quality signal classification system using wavelet energy distribution. in Proceedings of IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015., 7229617, Institute of Electrical and Electronics Engineers Inc., pp. 199-204, IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015, Kollam, India, 24-06-15. https://doi.org/10.1109/TAPENERGY.2015.7229617

A Neural Network based power quality signal classification system using wavelet energy distribution. / Sebastian, Praveen; Ds̈a, Pramod Antony.

Proceedings of IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 199-204 7229617.

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

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N2 - This paper presents a method for the classification of common Power Quality(PQ) events. The described system for the characterization of disturbances is based on wavelet based feature extraction. The amount of data to be analyzed and how the data can be interpreted are of crucial importance in power quality analysis. Wavelet Transform(WT) has been widely used in power quality signal analysis. The advantage of wavelet transform is it can provide precise time information of power quality events and has many advantages over traditional signal analysis approaches. In this paper Discrete Wavelet Transform(DWT) is used for obtaining the energy distribution from simulated signals. The system is developed with Neural Network which is an effective tool in classification of signals in power systems.

AB - This paper presents a method for the classification of common Power Quality(PQ) events. The described system for the characterization of disturbances is based on wavelet based feature extraction. The amount of data to be analyzed and how the data can be interpreted are of crucial importance in power quality analysis. Wavelet Transform(WT) has been widely used in power quality signal analysis. The advantage of wavelet transform is it can provide precise time information of power quality events and has many advantages over traditional signal analysis approaches. In this paper Discrete Wavelet Transform(DWT) is used for obtaining the energy distribution from simulated signals. The system is developed with Neural Network which is an effective tool in classification of signals in power systems.

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Sebastian P, Ds̈a PA. A Neural Network based power quality signal classification system using wavelet energy distribution. In Proceedings of IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 199-204. 7229617 https://doi.org/10.1109/TAPENERGY.2015.7229617