{"title":"Harmonics Elimination in Multilevel Inverter Using Linear Fuzzy Regression","authors":"A. K. Al-Othman, H. A. Al-Mekhaizim","volume":38,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":257,"pagesEnd":261,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/3192","abstract":"Multilevel inverters supplied from equal and constant\r\ndc sources almost don-t exist in practical applications. The variation\r\nof the dc sources affects the values of the switching angles required\r\nfor each specific harmonic profile, as well as increases the difficulty\r\nof the harmonic elimination-s equations. This paper presents an\r\nextremely fast optimal solution of harmonic elimination of multilevel\r\ninverters with non-equal dc sources using Tanaka's fuzzy linear\r\nregression formulation. A set of mathematical equations describing\r\nthe general output waveform of the multilevel inverter with nonequal\r\ndc sources is formulated. Fuzzy linear regression is then\r\nemployed to compute the optimal solution set of switching angles.","references":"[1] A. Bardossy, \"Note on fuzzy regression,\" Fuzzy Sets and Systems, vol.\r\n37, pp. 65-75, 1990\/8\/15 1990.\r\n[2] J. K. Kim and H.-R. Chen, \"A Comparison of fuzzy and nonparametric\r\nlinear regression,\" Computers Ops Res, vol. 24, pp. 505-519, 1997.\r\n[3] G. Peters, \"Fuzzy linear regression with fuzzy intervals,\" Fuzzy Sets and\r\nSystems, vol. 63, pp. 45-55, 1994\/4\/11 1994.\r\n[4] R.Dieck, \"Measurement Uncertainty Methods and Applications,\"\r\nInstrument Society of America 1995.\r\n[5] A. Abur and M. K. Celik, \"Least absolute value state estimation with\r\nequality and inequality constraints,\" Power Systems, IEEE Transactions\r\non, vol. 8, pp. 680 - 686, May 1993.\r\n[6] H. Singh, F. L. Alvarado, and W.-H. E. Liu, \"Constrained LAV state\r\nestimation using penalty functions,\" Power Systems, IEEE Transactions\r\non, vol. 12, pp. 383 - 388, Feb. 1997.\r\n[7] K. A. Clements, P. W. Davis, and K. D. Frey, \"Treatment of inequality\r\nconstraints in power system state estimation,\" Power Systems, IEEE\r\nTransactions on, vol. 10, pp. 567 - 574, May 1995.\r\n[8] F. C. Schweppe, Uncertain dynamic systems. Englewood Cliffs, N.J.:\r\nPrentice-Hall, 1973.\r\n[9] F. Shabani, N. R. Prasad, and H. A. Smolleck, \"A fuzzy-logic-supported\r\nweighted least squares state estimation,\" Electric Power Systems\r\nResearch, vol. 39, pp. 55-60, 1996\/10 1996.\r\n[10] H. Tanaka, S. Uejima, and K. Asai, \"Fuzzy linear regression model,\"\r\nInt. Congr. on Applied Systems Research and CyberneticsAcapulco,\r\nMexico, 1980, pp. 2933-2938.\r\n[11] T. Ross, Fuzzy Logic with Engineering Applications, 2nd ed.: John\r\nWiley & Sons, Ltd, April 2005.\r\n[12] H. Moskowitz and K. Kim, \"On assessing the H value in fuzzy linear\r\nregression,\" Fuzzy Sets and Systems, vol. 58, pp. 303-327, 1993.\r\n[13] H. Tanaka, S. Uejima, and K. Asai, \"Linear regression analysis with\r\nfuzzy model,\" IEEE Transactions on Systems, Man and Cybernetics,\r\nvol. SMC-12, pp. 903-907, 1982\/11\/ 1982.\r\n[14] J. J. Grainger and W. D. Stevenson, Power system analysis. New York:\r\nMcGraw-Hill, 1994.\r\n[15] D. T. Redden and W. H. Woodall, \"Further examination of fuzzy linear\r\nregression,\" Fuzzy Sets and Systems, vol. 79, pp. 203-211, 1996\/4\/22\r\n1996.\r\n[16] \"Optimization Toolbox for use with Matlab user's guide,\" 2 ed: The\r\nMath works inc.\r\n[17] M. M. Adibi and D. K. Thorne, \"Remote measurement calibration,\"\r\nPower Systems, IEEE Transactions on, vol. PWRS-1, pp. 194-202, May\r\n1986.\r\n[18] M. M. Adibi, K. A. Clements, R. J. Kafka, and J. P. Stovall, \"Remote\r\nmeasurement calibration,\" IEEE Computer Applications in Power, vol.\r\n3, pp. 37 - 42, Oct. 1990.\r\n[19] T. J. Ross, Fuzzy logic with engineering applications. Chichester: Wiley,\r\n2004.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 38, 2010"}