Charlene de C. Silva, Maria R. Madruga, Héliton R. Tavares, Terezinha F. de Oliveira, and Augusto e C.F. Saraiva


  1. [1] E.Bompard, T.Huang, Y.Wu, and M.Cremenescu, http://www., Classification and trend analysis of threats origins to the security of power systems, International Journal of Electrical Power & Energy Systems 50, 2013, 50–64.
  2. [2] ABNT, Associação Brasileira de Normas Técnicas. NBR 10576: Óleo mineral isolante de equipamentos elétricos – Diretrizes para supervisão e manutenção, Brazil 2006.
  3. [3] M. Duval and J. Dukarm, Improving the reliability of transformer gas-in-oil diagnosis. IEEE Electrical Insulation Magazine, 21, 2005, 21–27.
  4. [4] M.S. Godinho, A.E. Oliveira, and M.M. Sena, Determination of interfacial tension of insulating oils by using image analysis and multi-way calibration, Microchemical Journal, 96(1), 2010, 42–45.
  5. [5] ABNT, Associação Brasileira de Normas Técnicas NBR 7070 – Guia para amostragem de gases e óleo em transformadores e análise dos gases livres e dissolvidos, Brazil, 2006.
  6. [6] IEC, International Electrotechnical Commission, IEC 60599 – Mineral oil-impregnated electrical equipment in service: guide to the interpretation of dissolved and free gases analysis, 1999.
  7. [7] H. Malik, A.K. Yadav, S. Mishra, and T. Mehto, Application of neuro-fuzzy scheme to investigate the winding insulation paper deterioration in oil-immersed power transformers, International Journal of Electrical Power & Energy Systems, 53, 2013, 256–271.
  8. [8] M.A.A. Wahab, M.M. Hamada, and A. Mohamed, Artificial neural network and non-linear models for prediction of transformer oil residual operating time, Electric Power Systems Research, 81(1), 2011, 219–227.
  9. [9] T.S. Noronha, T.F. Oliveira, A.M. Silveira, R.R. Silva, and A.C.F Saraiva, Knowledge acquisition of vibrations in high-power transformers using statistical analyses and fuzzy approaches – A case study, Electric Power Systems Research (Print), 104, 2013, 110–115.
  10. [10] F. Cribari-Neto and T.C. Souza, Testing inference in variable dispersion beta regressions, Journal of Statistical and Simulation, 82(12), 2012, 1827–1843.
  11. [11] F. Cribari-Neto and A. Zeiles, Beta regression in r, Journal of statistical Software, 34(2), 2010, 1–24.
  12. [12] S.L.P. Ferrari and F. Cribari-Neto, Beta regression for modelling rates and proportions, Journal of Applied Statistics, 31(7), 2004, 799–815.
  13. [13] A.B. Simas, W. Barreto-Souza, and A.V. Rocha, Improved estimators for a general class of beta regression models, Computational Statistics and Data Analysis, 54, 2010, 348–366.
  14. [14] P.L. Espinheira, S.L.P. Ferrari, and F. Cribari-Neto, Influence diagnostics in beta regression, Computational Statistics and Data Analysis, 52(9), 2008, 3, 56, 4417–4431.
  15. [15] P.L. Espinheira, S.L.P. Ferrari, and F. Cribari-Neto, On beta regression residuals, Journal of Applied Statistics, 35(4), 2008, 3, 49, 53, 54, 407–419.
  16. [16] J. Nocedal and S.J. Wright, Numerical optimization, 2nd ed.(New York: Springer Science & Business Media, 2006).
  17. [17] M. Milash, Manuten¸cão de Transformadores em L´ıquido Isolante, Edgard Blücher Ltda, 1984.
  18. [18] W.G. Cochran, Sampling Techniques, 3rd ed. (New York: John Wiley & Sons, Inc, 1977).

Important Links:

Go Back