Output voltage estimation of a power transformer integrated with three-phase T-type inverter
Year 2023,
Volume: 7 Issue: 2, 199 - 211, 30.06.2023
Seda Kul
,
Selami Balcı
,
Suleyman Sungur Tezcan
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.
Supporting Institution
Karamanoglu Mehmetbey University Scientific Research Projects Coordination Unit
References
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Year 2023,
Volume: 7 Issue: 2, 199 - 211, 30.06.2023
Seda Kul
,
Selami Balcı
,
Suleyman Sungur Tezcan
References
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- [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] 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.
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- [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.
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- [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] 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] 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] 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] 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] 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