Harmonics Elimination in Multilevel Inverter Using Linear Fuzzy Regression
Multilevel inverters supplied from equal and constant
dc sources almost don-t exist in practical applications. The variation
of the dc sources affects the values of the switching angles required
for each specific harmonic profile, as well as increases the difficulty
of the harmonic elimination-s equations. This paper presents an
extremely fast optimal solution of harmonic elimination of multilevel
inverters with non-equal dc sources using Tanaka's fuzzy linear
regression formulation. A set of mathematical equations describing
the general output waveform of the multilevel inverter with nonequal
dc sources is formulated. Fuzzy linear regression is then
employed to compute the optimal solution set of switching angles.
[1] A. Bardossy, "Note on fuzzy regression," Fuzzy Sets and Systems, vol.
37, pp. 65-75, 1990/8/15 1990.
[2] J. K. Kim and H.-R. Chen, "A Comparison of fuzzy and nonparametric
linear regression," Computers Ops Res, vol. 24, pp. 505-519, 1997.
[3] G. Peters, "Fuzzy linear regression with fuzzy intervals," Fuzzy Sets and
Systems, vol. 63, pp. 45-55, 1994/4/11 1994.
[4] R.Dieck, "Measurement Uncertainty Methods and Applications,"
Instrument Society of America 1995.
[5] A. Abur and M. K. Celik, "Least absolute value state estimation with
equality and inequality constraints," Power Systems, IEEE Transactions
on, vol. 8, pp. 680 - 686, May 1993.
[6] H. Singh, F. L. Alvarado, and W.-H. E. Liu, "Constrained LAV state
estimation using penalty functions," Power Systems, IEEE Transactions
on, vol. 12, pp. 383 - 388, Feb. 1997.
[7] K. A. Clements, P. W. Davis, and K. D. Frey, "Treatment of inequality
constraints in power system state estimation," Power Systems, IEEE
Transactions on, vol. 10, pp. 567 - 574, May 1995.
[8] F. C. Schweppe, Uncertain dynamic systems. Englewood Cliffs, N.J.:
Prentice-Hall, 1973.
[9] F. Shabani, N. R. Prasad, and H. A. Smolleck, "A fuzzy-logic-supported
weighted least squares state estimation," Electric Power Systems
Research, vol. 39, pp. 55-60, 1996/10 1996.
[10] H. Tanaka, S. Uejima, and K. Asai, "Fuzzy linear regression model,"
Int. Congr. on Applied Systems Research and CyberneticsAcapulco,
Mexico, 1980, pp. 2933-2938.
[11] T. Ross, Fuzzy Logic with Engineering Applications, 2nd ed.: John
Wiley & Sons, Ltd, April 2005.
[12] H. Moskowitz and K. Kim, "On assessing the H value in fuzzy linear
regression," Fuzzy Sets and Systems, vol. 58, pp. 303-327, 1993.
[13] H. Tanaka, S. Uejima, and K. Asai, "Linear regression analysis with
fuzzy model," IEEE Transactions on Systems, Man and Cybernetics,
vol. SMC-12, pp. 903-907, 1982/11/ 1982.
[14] J. J. Grainger and W. D. Stevenson, Power system analysis. New York:
McGraw-Hill, 1994.
[15] D. T. Redden and W. H. Woodall, "Further examination of fuzzy linear
regression," Fuzzy Sets and Systems, vol. 79, pp. 203-211, 1996/4/22
1996.
[16] "Optimization Toolbox for use with Matlab user's guide," 2 ed: The
Math works inc.
[17] M. M. Adibi and D. K. Thorne, "Remote measurement calibration,"
Power Systems, IEEE Transactions on, vol. PWRS-1, pp. 194-202, May
1986.
[18] M. M. Adibi, K. A. Clements, R. J. Kafka, and J. P. Stovall, "Remote
measurement calibration," IEEE Computer Applications in Power, vol.
3, pp. 37 - 42, Oct. 1990.
[19] T. J. Ross, Fuzzy logic with engineering applications. Chichester: Wiley,
2004.
[1] A. Bardossy, "Note on fuzzy regression," Fuzzy Sets and Systems, vol.
37, pp. 65-75, 1990/8/15 1990.
[2] J. K. Kim and H.-R. Chen, "A Comparison of fuzzy and nonparametric
linear regression," Computers Ops Res, vol. 24, pp. 505-519, 1997.
[3] G. Peters, "Fuzzy linear regression with fuzzy intervals," Fuzzy Sets and
Systems, vol. 63, pp. 45-55, 1994/4/11 1994.
[4] R.Dieck, "Measurement Uncertainty Methods and Applications,"
Instrument Society of America 1995.
[5] A. Abur and M. K. Celik, "Least absolute value state estimation with
equality and inequality constraints," Power Systems, IEEE Transactions
on, vol. 8, pp. 680 - 686, May 1993.
[6] H. Singh, F. L. Alvarado, and W.-H. E. Liu, "Constrained LAV state
estimation using penalty functions," Power Systems, IEEE Transactions
on, vol. 12, pp. 383 - 388, Feb. 1997.
[7] K. A. Clements, P. W. Davis, and K. D. Frey, "Treatment of inequality
constraints in power system state estimation," Power Systems, IEEE
Transactions on, vol. 10, pp. 567 - 574, May 1995.
[8] F. C. Schweppe, Uncertain dynamic systems. Englewood Cliffs, N.J.:
Prentice-Hall, 1973.
[9] F. Shabani, N. R. Prasad, and H. A. Smolleck, "A fuzzy-logic-supported
weighted least squares state estimation," Electric Power Systems
Research, vol. 39, pp. 55-60, 1996/10 1996.
[10] H. Tanaka, S. Uejima, and K. Asai, "Fuzzy linear regression model,"
Int. Congr. on Applied Systems Research and CyberneticsAcapulco,
Mexico, 1980, pp. 2933-2938.
[11] T. Ross, Fuzzy Logic with Engineering Applications, 2nd ed.: John
Wiley & Sons, Ltd, April 2005.
[12] H. Moskowitz and K. Kim, "On assessing the H value in fuzzy linear
regression," Fuzzy Sets and Systems, vol. 58, pp. 303-327, 1993.
[13] H. Tanaka, S. Uejima, and K. Asai, "Linear regression analysis with
fuzzy model," IEEE Transactions on Systems, Man and Cybernetics,
vol. SMC-12, pp. 903-907, 1982/11/ 1982.
[14] J. J. Grainger and W. D. Stevenson, Power system analysis. New York:
McGraw-Hill, 1994.
[15] D. T. Redden and W. H. Woodall, "Further examination of fuzzy linear
regression," Fuzzy Sets and Systems, vol. 79, pp. 203-211, 1996/4/22
1996.
[16] "Optimization Toolbox for use with Matlab user's guide," 2 ed: The
Math works inc.
[17] M. M. Adibi and D. K. Thorne, "Remote measurement calibration,"
Power Systems, IEEE Transactions on, vol. PWRS-1, pp. 194-202, May
1986.
[18] M. M. Adibi, K. A. Clements, R. J. Kafka, and J. P. Stovall, "Remote
measurement calibration," IEEE Computer Applications in Power, vol.
3, pp. 37 - 42, Oct. 1990.
[19] T. J. Ross, Fuzzy logic with engineering applications. Chichester: Wiley,
2004.
@article{"International Journal of Electrical, Electronic and Communication Sciences:52310", author = "A. K. Al-Othman and H. A. Al-Mekhaizim", title = "Harmonics Elimination in Multilevel Inverter Using Linear Fuzzy Regression", abstract = "Multilevel inverters supplied from equal and constant
dc sources almost don-t exist in practical applications. The variation
of the dc sources affects the values of the switching angles required
for each specific harmonic profile, as well as increases the difficulty
of the harmonic elimination-s equations. This paper presents an
extremely fast optimal solution of harmonic elimination of multilevel
inverters with non-equal dc sources using Tanaka's fuzzy linear
regression formulation. A set of mathematical equations describing
the general output waveform of the multilevel inverter with nonequal
dc sources is formulated. Fuzzy linear regression is then
employed to compute the optimal solution set of switching angles.", keywords = "Multilevel converters, harmonics, pulse widthmodulation (PWM), optimal control.", volume = "4", number = "2", pages = "243-4", }