Selective harmonic elimination in multi-level inverters by using neural networks

In this study, it is aimed to eliminate the harmonics selected by the selective harmonic elimination (SHE) method in a 7-level cascade multilevel inverter using artificial neural networks (ANNs). A control algorithm has been developed in which the 3rd and 5th harmonics or 5th and 7th harmonics can be eliminated according to the selection while adjusting the output voltage amplitude of the inverter. The required switching angles for SHE are calculated in real time using ANN. These angles were first obtained offline training of ANN using Newton-Raphson method. ANN was trained in MATLAB® environment according to the obtained data. The resulting ANN algorithm and practical implementation using the STM32F429 ARM microcontroller® and inverter switching was provided. Experimental results of the system with RL load were tested.

Keywords:

Artificial Neural Networks Multilevel Inverter, Newton-Raphson method, Selective Harmonic Elimination,

PDF

___

1. Santhi, R., Giridharan, K., Kannabhiran, A., A survey on voltage source multi level inverter topologies. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017. p. 1262-1271.
2. Kar, P. K., Priyadarshi, A., Karanki, S. B., A modified single phase H-bridge multilevel inverter topology for photovoltaic applications. in National Power Electronics Conference (NPEC) 2017: Pune, India. p. 340-345.
3. Sahu, N., and Londhe, N. D., Optimization based selective harmonic elimination in multi-level inverters. in National Power Electronics Conference (NPEC) 2017: Pune, India. p. 325-329.
4. Sharma, A., Singh, D., Pandey, V., Gao, S., Selective harmonic elimination for cascaded H-bridge MLI using GA and NR-Method. in International Conference on Electrical and Electronics Engineering (ICE3) 2020: Gorakhpur, India. p. 89-94.
5. Halim, A. W., Azam, T. N. A. T., Applasamy, K., Jidin, A., Selective harmonic elimination based on newton-raphson method for cascaded H-bridge multilevel inverter. International Journal of Power Electronics and Drive Systems (IJPEDS), 2017. 8(3): p. 1193-1202.
6. Chatterjee, A., Rastogi, A., Rastogi, R., Saini, A., and Sahoo, S. K., Selective harmonic elimination of cascaded H-bridge multilevel inverter using genetic algorithm. in Innovations in Power and Advanced Computing Technologies (i-PACT) 2017: Vellore, India. p. 1-4.
7. Bose, B. K., Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective. IEEE Transactions on Industrial Electronics, 2007. 54(1): p. 14-33.
8. Kumar, J., Gambhir, J., Kumar, A., Control of switching angles for a CMLI using ANN. in Engineering and Computational Sciences (RAECS) 2014, Panjab University Chandigarh: India. p. 1-6.
9. Aravind, P. S., Alexander, S. A., Harmonic minimization of a solar fed cascaded H bridge inverter using artificial neural network. in International Conference on Energy Efficient Technologies for Sustainability 2013: Nagercoil, India. p. 163-167.
10. Filho, F. J. T., Tolbert, L. M., Ozpineci, B., Real time selective harmonic minimization for multilevel inverters using genetic algorithm and artificial neural network angle generation. Proceedings of The 7th International Power Electronics and Motion Control Conference, 2012. p. 895-899.
11. Fakhry, M. G., Massoud, A., Ahmet, S., Quasi seven-level operation of multilevel converters with selective harmonic elimination, in 2014 26th International Conference on Microelectronics (ICM): Doha, Qatar. p. 216-219.
12. Yang, K., Hao, J., and Wang, Y., Switching angles generation for selective harmonic elimination by using artificial neural networks and quasi-newton algorithm, in 2016 IEEE Energy Conversion Congress and Exposition (ECCE): Milwaukee, WI. p. 1-5.
13. Cybenko, G, Approximation by superpositions of a sigmoidal function. Math. Control Signal Systems, 1989. 2(4): p. 303–314.
14. Oluwasegun K. M., Ojo O. A., Ola O. T., Birur A., Cuddy J. and Chan K., Development of artificial neural network models for predicting weld output parameters in Advanced fusion welding of a magnesium alloy. American Journal of Modeling and Optimization. 2018. 6(1): p. 18-34.

International Advanced Researches and Engineering Journal-Cover

Yayın Aralığı: Yılda 3 Sayı
Başlangıç: 2017
Yayıncı: Dr. Ceyhun YILMAZ

Arşiv