Output voltage estimation of a power transformer integrated with three-phase T-type inverter

Abstract

The issues related to integrating these systems into the grids continue to gain importance with the increasing use and importance of renewable energy sources. Therefore, the importance of power distribution transformers is increasing. Besides, these power distribution transformers are connected to the grid with power electronics circuits and inverters. Considering the modular inverter structures, ease of maintenance, and connection, three-level T-type inverters are chosen for this study. The secondary output voltage of the power transformer is estimated by using circuit parameters such as the dead time of the inverter circuit, PWM switching frequency, and modulation rate. Based on the finite element analysis analysis according to the selected parameters, 810 data are obtained with time-dependent parametric analysis. The adaptive neuro-fuzzy inference system model is constructed by considering the simulation data to estimate the secondary output of the power transformer of these parameters. In the training phase of the model, 648 randomly selected data from 810 data obtained by ANSYS-Electronics/Simplorer are used. The remaining 162 data are used in the testing process to measure system performance. As a result of the analysis made by ANFIS, the Root Mean Square Error (RMSE) error is found as 2.475%. Since the values obtained in the estimation process of the study are very close to the simulation values, the ANFIS method can be used as an estimation method that will give accurate results during the design phase.

Keywords

• ANFIS
• T-type inverter
• Parametric analysis
• PWM excitation
• Transformer

References

1. [1] Gencer, A. Comparison of t-type converter and NPC for the wind turbine based on doubly-fed induction generator. Balkan Journal of Electrical and Computer Engineering 2021; 9: 123-128. DOI: 10.17694/bajece.826624.
2. [2] Schweizer, M., Kolar, J. W. High efficiency drive system with 3-level T-type inverter. In 14. European conference on power electronics and applications; 30 August-1 September 2011: Institute of Electrical and Electronics Engineers (IEEE), pp. 1-10.
3. [3] Salem, A, Abido, M. A. T-type multilevel converter topologies: A comprehensive review. Arabian Journal for Science and Engineering 2019; 44: 1713-1735. DOI: 10.1007/s13369-018-3506-6
4. [4] Pires, V. F, Foito, D, Martins, J. F. Multilevel power converter with a dual T-type three level inverter for energy storage. In: OPTIM 2014 International Conference on Optimization of Electrical and Electronic Equipment; 22-24 May 2014: Institute of Electrical and Electronics Engineers (IEEE), pp. 1091-1096.
5. [5] Pires, V. F, Foito, D, Sousa, D. M. Conversion structure based on a dual T-type three-level inverter for grid connected photovoltaic applications. In: PEDG 2014 5. International Symposium on Power Electronics for Distributed Generation Systems; 24-27 June 2014: Institute of Electrical and Electronics Engineers (IEEE), pp.1-7.
6. [6] Pandit, S, Mishra, R. K, Chauhan, G. Estimation and methods of equalizing leakage reactance for multi-winding transformers. In: NPSC 2018 20. National Power Systems Conference; 14-16 December 2018: Institute of Electrical and Electronics Engineers (IEEE), pp. 1-5.
7. [7] Alvarez, D. L, Rivera, S. R., Mombello, E. E. Transformer thermal capacity estimation and prediction using dynamic rating monitoring. IEEE Transactions on Power Delivery 2019; 34(4): 1695-1705. DOI: 10.1109/TPWRD.2019.2918243
8. [8] Zhou, L, Wang, J, Wang, L, Yuan, S, Huang, L, Wang, D, Guo, L. A method for hot-spot temperature prediction and thermal capacity estimation for traction transformers in high-speed railway based on genetic programming. IEEE Transactions on Transportation Electrification 2019; 5(4): 1319-1328. DOI: 10.1109/TTE.2019.2948039.

9. [9] Lin, W, Miao, X, Xiao, S, Jiang, H, Zhuang, S. Research on Winding Temperature Prediction of UHV Transformer Based on Convolutional Long Short-Term Memory Network. In: Chinese Intelligent Systems Conference; 6-8 April 2021: Springer, pp. 109-120.
10. [10] Işıka, K, Alptekin, S. E. A benchmark comparison of Gaussian process regression, support vector machines, and ANFIS for man-hour prediction in power transformers manufacturing. Procedia Computer Science 2022; 207: 2567-2577. DOI: 10.1016/J.PROCS.2022.09.315.
11. [11] Colak, M, Balci, S. Parameter Estimation of Photovoltaic System Using Marine Predators Optimization Algorithm-Based Multilayer Perceptron. In: ICRERA 2022 11. International Conference on Renewable Energy Research and Application; 18-21 September 2021: Institute of Electrical and Electronics Engineers (IEEE), pp. 540-545,
12. [12] Kul, S, Yıldız, B, Tezcan, S. S. Estimation of Core Losses in Three-Phase Dry-Type Transformers Using Adaptive-Network Based Fuzzy Inference Systems (ANFIS). Electric Power Components and Systems 2022; 50( 16-17); 1006-1013. DOI: 10.1080/15325008.2022.2144550
13. [13] Aslan, B, Balci, S, Kayabasi, A, Yildiz, B. The core loss estimation of a single phase inverter transformer by using adaptive neuro-fuzzy inference system. Measurement 2021; 179: 109427. DOI: 10.1016/j.measurement.2021.109427.
14. [14] Balci, S, Kayabasi, A, Yildiz, B. ANFIS Based Parameter Estimating of a Two-Phase Interleaved Dual Cascaded DC-DC Boost Converter for Fuel Cell Supplied Electric Vehicles. Balkan Journal of Electrical and Computer Engineering 2021; 9(4): 410-416. DOI: 10.17694/bajece.940791.
15. [15] Atar, T, Balci, S, Kayabasi, A. The analysis of three level inverter circuit with regard to current harmonic distortion by using ANFIS. Journal of Energy Systems 2022; 6(2): 143-152, DOI: 10.30521/jes.951487.
16. [16] Balci, S, Kayabasi, A, Yildiz, B. ANFIS based voltage determination for photovoltaic systems according to the specific cell parameters, and a simulation for the non-isolated high gain DC–DC boost converter control regard to voltage fluctuations. Applied Solar Energy 2019; 55: 357-366, DOI: 10.3103/S0003701X19060100.
17. [17] Aslan, B, Balci, S, Kayabasi, A. ANFIS-based Parameter Estimation of a Single Phase Inverter Circuit with Isolation Transformer. Kastamonu University Journal of Engineering and Sciences 2022; 8(2): 135-144, DOI: 10.55385/kastamonujes.1193007
18. [18] Hizarci, H, Pekperlak, U, Arifoglu, U. Conducted emission suppression using an EMI filter for grid-tied three-phase/level T-type solar inverter. IEEE Access 2021; 9: 67417-67431. DOI:10.1109/ACCESS.2021.3077380
19. [19] Schweizer, M, Lizama, I, Friedli, T, Kolar, J. W. Comparison of the chip area usage of 2-level and 3-level voltage source converter topologies. In: IECON 2010 36. Annual Conference on IEEE Industrial Electronics Society; 7-10 November 2010: Institute of Electrical and Electronics Engineers (IEEE), pp. 391-396.
20. [20] Jang, J. S. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 1993; 23(3): 665-685. DOI: 10.1109/21.256541.
21. [21] J.S.R. Jang, Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm. In: AAAI 1991 9. National Conference on Artificial Intelligence; 14-19 July 1991: AAAI Press, pp. 762-767
22. [22] Walia, N, Singh, H, Sharma, A. ANFIS: Adaptive neuro-fuzzy inference system-a survey. International Journal of Computer Applications 2015; 123(13): DOI: 10.5120/ijca2015905635