Classification of Power Quality Considering Voltage Sags occurred in Feeders

Anderson Roges Teixeira Góes, Maria Teresinha Arns Steiner, Pedro José Steiner Neto

2013

Abstract

In this paper we propose a methodology to classify Power Quality for feeders, based on sags and by the use of KDD technique, establishing a quality level printed in labels. To support the methodology, it was applied to feeders on a substation located in Curitiba, Paraná, Brazil, based on attributes such as sag length, duration and frequency (number of occurrences on a given period of time). In the search for feeders quality classification, on the Data Mining stage, the main stage on KDD process, three different techniques were used in a comparatively way for pattern recognition: Artificial Neural Networks, Support Vector Machines an Genetic Algorithms. Those techniques presented acceptable results in classification feeders with no possible classification using a simplified method based on maximum number of sags. Thus, by printing the label with information and Quality level, utilities companies can get better organized for mitigation procedures, by establishing clear targets.

References

  1. Adepoju, G. A.; Ogunjuyigbe, S. O. A.; Alawode, K. O. Application of Neural Network to Load Forecasting in Nigerian Electrical Power System. The Pacific JouANNl of Science and Technology. Spring. v. 8, p. 68-72, 2007.
  2. ANEEL Agência Nacional de Energia Elétrica. Procedimentos de Distribuição de Energia Elétrica no Sistema Elétrico Nacional - PRODIST: Módulo 8 - Qualidade da Energia Elétrica. 2008.
  3. Burges, C. J. C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, v. 2, p. 121-168, 1998.
  4. Caciotta, M.; Giarnetti, S.; Leccese, F. Hybrid Neural Network System for Electric Load Forecasting of Telecomunication Station. XIX IMEKO World Congress - Fundamental and Applied Metrology. Lisboa, Portugal, p. 657-661, 2009.
  5. Casteren, J. F. L. Van.; Enslin, L. H. R.; Hulshorst, W. T. J.; Kilng, W.L.; Hamoen, M. D.; Cobben, J. F. G. Acustomer oriented approach to the classification of voltage dips. In: The18th International Conference and exhibition on Electricity Distribuion - CIRED, 2005.
  6. Cobben, J. F. G.; Casteren, J. F. L. Classification Methodologies for Power Quality. Electrical Power Quality & Utilization Magazine. v. 2, no 1, p. 11-17, 2006.
  7. Dash, P. K.; Padhee, M.; Barik, S. K. Estimation of power quality indices in distributed generation systems during power islanding conditions. Electrical Power and Energy Systems, v. 36, p. 18-30, 2012.
  8. Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P.; Uthurusamy, R. Advances in Knowledge Discovery & Data Mining. 1 ed. American Association for Artificial Intelligence, Menlo Park, Califórnia, 1996.
  9. Gencer, O.; Ozturk, S.; Erfidan, T. A new approach to voltage sag detection based on wavelet transform. Electrical Power and Energy Systems, v. 32, p. 133- 140, 2010.
  10. Góes, A. R. T. Uma metodologia para a criação de etiqueta de qualidade no contexto de Descoberta de Conhecimento em Bases de Dados: aplicação nas áreas elétrica e educacional. 145 f. Tese (Doutorado em Métodos Numéricos em Engenharia) - Setor Tecnologia e Setor de Ciências Exatas, Universidade Federal do Paraná, Curitiba, 2012.
  11. Goldberg, D. E. Genetic algorithms in search, optmization, and machines learning. Addison-Wesley Publishing Company, Inc. Massachusetts, 1989.
  12. Haykin, S. Neural Networks - A Comprehensive Foundation. 2.nd., Prentice Hall, New Jersey, 1999. Kaewarsa, S.; Attakitmongcol, K.; Kulworawanichpong, T. Recognition of power quality events by using multiwavelet-based neural networks. Electrical Power and Energy Systems, v. 30, p. 245- 260, 2008.
  13. Kappor, R.; Saini, M. K. Hybrid demodulation concept and harmonic analysis for single/multiple power quality events detection and classification. Electrical Power and Energy Systems, v. 33, p. 1608-1622, 2011.
  14. Oleskovicz, M.; Coury, D. V.; Carneiro, A. A. F. M.; Arruda, E. F.; Delmont, O.; Souza, S. A. Estudo comparativo de ferramentas modeANNs de análise aplicadas à qualidade da energia elétrica. Revista Controle & Automação, v. 17, n. 3. Julho, agosto e setembro 2006.
  15. Trindade, R. M. Sistema Digital de Detecção e Classification de Eventos de Qualidade de Energia. 114 f. Dissertação (Mestrado em Engenharia Elétrica) - Faculdade de Engenharia da Universidade Federal de Juiz de Fora, Juiz de Fora, 2005.
  16. Vapnik, V. The nature of statistical learning theory. Springer-Verlag, New Yourk, 1995.
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Paper Citation


in Harvard Style

Teixeira Góes A., Arns Steiner M. and Steiner Neto P. (2013). Classification of Power Quality Considering Voltage Sags occurred in Feeders . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 433-442. DOI: 10.5220/0004511104330442


in Bibtex Style

@conference{ncta13,
author={Anderson Roges Teixeira Góes and Maria Teresinha Arns Steiner and Pedro José Steiner Neto},
title={Classification of Power Quality Considering Voltage Sags occurred in Feeders},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={433-442},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004511104330442},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Classification of Power Quality Considering Voltage Sags occurred in Feeders
SN - 978-989-8565-77-8
AU - Teixeira Góes A.
AU - Arns Steiner M.
AU - Steiner Neto P.
PY - 2013
SP - 433
EP - 442
DO - 10.5220/0004511104330442