The Output Voltage Estimation of Power Transformer Integrated with a Three Phase T-Type Inverter

Q3 Energy Journal of Energy Systems Pub Date : 2023-04-28 DOI:10.30521/jes.1246150
Seda Kul, S. Balci, S. S. Tezcan
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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.
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与三相t型逆变器集成的电力变压器输出电压估计
随着可再生能源的使用和重要性的增加,将这些系统整合到电网中的问题继续变得越来越重要。因此,配电变压器的重要性日益增加。此外,这些配电变压器与电力电子电路和逆变器连接到电网。考虑到逆变器的模块化结构,便于维护和连接,本研究选择三电平t型逆变器。利用逆变电路的死区时间、PWM开关频率、调制速率等电路参数估计电力变压器的二次输出电压。根据所选参数进行有限元分析,得到810个数据,进行随时间变化的参数分析。考虑仿真数据,构建了自适应神经模糊推理系统模型,对这些参数下的电力变压器二次输出进行估计。在模型的训练阶段,从ANSYS-Electronics/ simplover获得的810个数据中随机选择648个数据。剩下的162个数据在测试过程中用于测量系统性能。通过ANFIS的分析,发现均方根误差(RMSE)误差为2.475%。由于本研究在估计过程中得到的值与仿真值非常接近,因此可以使用ANFIS方法作为在设计阶段给出准确结果的估计方法。
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来源期刊
Journal of Energy Systems
Journal of Energy Systems Environmental Science-Management, Monitoring, Policy and Law
CiteScore
1.60
自引率
0.00%
发文量
29
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