Hakan AÇIKGÖZ, Ökkeş Fatih KEÇECİOĞLU, Mustafa ŞEKKELİ

Real-time implementation of electronic power transformer based on intelligent controller

Along with the rapid advances in power electronics and semiconductor technology, electronic power transformers (EPTs), which are expected to replace conventional power transformers in the future, are being developed anddesigned. This study presents the dynamic performance of the intelligent controller structure in the control of an EPT.The EPT structure, consisting of the input, isolation, and output stages, is designed for experimental and simulationstudies. A three-phase pulse width modulation (PWM) rectifier is used to rectify the grid voltages at the input stageof EPT. The DC-bus voltage of the three-phase PWM rectifier is controlled by a neuro-fuzzy controller (NFC) againstthe disturbances that may occur in the input stage. In the isolation stage of the EPT, a DC-DC converter circuit isused. Thus, DC voltage obtained from the input part is reduced to the appropriate voltage values. In the output stage,the two-level three-phase inverter circuit is used to provide the necessary voltages for users. The control algorithms forinput and output stages of the EPT have been developed using a dSPACE DS1104 controller card, which can work inreal time with MATLAB/Simulink. Experimental and simulation studies are realized to demonstrate the voltage sag,swell, harmonic, and reactive power compensation performances of the EPT controlled by NFC.

PDF

___

[1] Dujic D, Mester A, Steinke JK, Weiss M, Lewdeni-Schmid S, Stefanutti P. Power electronic traction transformermedium voltage prototype. IEEE T Ind Electron 2014; 61: 3257-3268.
[2] Chuanhong Z, Zheng Z, Xu L, Wang K, Li Y. Modeling and control of a multiport power electronic transformer (PET) for electric traction applications. IEEE T Power Electr 2017; 31: 915-927.
[3] Seon-Hwan H, Xiaohu L, Jang-Mok K, Hui L. Distributed digital control of modular-based solid-state transformer using DSP+FPGA. IEEE T Ind Electron 2013; 60: 670-680.
[4] Zilouchian A, Jamshidi M. Intelligent Control Systems Using Soft Computing Methodologies. 2nd ed. Boca Raton, FL, USA: CRC Press, 2001.
[5] Huber JE, Kolar JW. Volume/weight/cost comparison of a 1 MVA 10 kV/400 V solid-state against a conventional low-frequency distribution transformer. In: IEEE 2014 Energy Conversion Congress and Exposition; Pittsburgh, PA, USA; 2014. pp. 4545–4552.
[6] McMurray W. Power Converter Circuits Having a High-Frequency Link. US Patent 3.517.300, 1970.
[7] Falcones S, Mao X, Ayyanar R. Topology comparison for solid state transformer implementation. In: IEEE 2010 Power and Energy Society General Meeting; Providence, RI, USA; 2010. pp. 1-8.
[8] Zhao T, Wang G, Bhattacharya S, Huang AQ. Voltage and power balance control for a cascaded H-bridge converterbased solid-state transformer. IEEE T Power Electr 2013; 28: 1523-1532.
[9] Xu S, Huang AQ, Burgos R. Review of solid-state transformer technologies and their application in power distribution systems. IEEE J Em Sel Top P 2013; 1: 186-198.
[10] Wang X, Liu J, Ouyang S, Xu T, Meng F, Song S. Control and experiment of an H-Bridge-based three-phase three-stage modular power electronic transformer. IEEE T Power Electr 2016; 31: 2002-2011.
[11] Xu S, Xunwei Y, Fei W, Huang AQ. Design and demonstration of a 3.6-kV-120-V/10-kVA solid-state transformer for smart grid application. IEEE T Power Electr 2014; 29: 3982-3996.
[12] Iman-Eini H, Farhangia SH, Schanen JL, Khakbazan-Fard M. A modular power electronic transformer based on a cascaded H-bridge multilevel converter. Electr Pow Syst Res 2009; 79: 1625-1637.
[13] Liu BL, Zha YB, Zhang T. Sliding mode control of solid state transformer using a three-level Hysteresis Function. J Cent South Univ 2016; 23: 2063-2074.
[14] Beiranvand H, Rokrok E. Asymptotically stable controller for SSTs based on Lyapunov direct stability method. IET Power Electron 2017; 10: 2065-207.
[15] Hooshmand RA, Ataei M, Rezaei MH. Improving the dynamic performance of distribution electronic power transformers using sliding mode control. J Power Electron 2012; 12: 145-156.
[16] Liu H, Mao C, Lu J, Wang D. Optimal regulator-based control of electronic power transformer for distribution systems. Electr Pow Syst Res 2009; 79: 863-870
[17] Acikgoz H, Kececioglu OF, Karadol I, Gani A, Sekkeli M. Adaptive control of solid state transformer using type-2 fuzzy neural system. Stud Inform Control 2017; 26: 171-181.
[18] Zadeh LA. Fuzzy sets. Inform Control 1965; 8: 338-353.
[19] Jang JSR, Sun CT, Mizutani E. Neuro-Fuzzy and Soft Computing. Upper Saddle River, NJ, USA: Prentice Hall, 1997.
[20] Tuncer S, Dandil B. Adaptive neuro-fuzzy current control for multilevel inverter fed induction motor. COMPEL 2008; 27: 668-681.
[21] Coteli R, Deniz E, Dandil B, Tuncer S, Ata F. Phase angle control of three level inverter based D-STATCOM using neuro-fuzzy controller. Adv Electr Comput En 2012; 12: 77-84.
[22] Çöteli R, Acıkgöz H, Dandıl B, Tuncer S. Real-time implementation of three-level inverter-based D-STATCOM using neuro-fuzzy controller. Turk J Elec Eng Comp Sci 2018; 26: 2088-2103.
[23] Kılıç E, Özçalık HR, Şit S. Adaptive controller with RBF neural network for induction motor drive. Int J Numer Model 2018; 31: 1-11.
[24] Kececioglu OF, Acikgoz H, Yildiz C, Gani A, Sekkeli M. Power quality improvement using hybrid passive filter configuration for wind energy systems. J Electr Eng Technol 2017; 12: 207-216.