Research Article
Modeling of Induction Heating Inverter Using System Identification
@INPROCEEDINGS{10.1007/978-3-030-15357-1_20, author={Mulugeta Debebe and Endalew Ayenew and Beza Neqatibeb and Venkata Komanapalli}, title={Modeling of Induction Heating Inverter Using System Identification}, proceedings={Advances of Science and Technology. 6th EAI International Conference, ICAST 2018, Bahir Dar, Ethiopia, October 5-7, 2018, Proceedings}, proceedings_a={ICAST}, year={2019}, month={3}, keywords={System identification Induction heating inverter PRBS}, doi={10.1007/978-3-030-15357-1_20} }- Mulugeta Debebe
Endalew Ayenew
Beza Neqatibeb
Venkata Komanapalli
Year: 2019
Modeling of Induction Heating Inverter Using System Identification
ICAST
Springer
DOI: 10.1007/978-3-030-15357-1_20
Abstract
In this paper, Auto Regressive eXogenous input (ARX), Auto Regressive Moving Average eXogenous input (ARMAX), Output error and BJ models of class D voltage-source half-bridge series-resonant inverter used for induction heating are identified and studied based on prior knowledge and measured data from PSIM simulation Environment. The output data are generated by applying Pseudo-Random-Binary-sequence (PRBS) as an input through the inverter MOSFET gate in the PSIM software. PRBS signal is generated using standard components such as flip-flops or XOR gates to approximate the white noise in the PSIM software. The generated output and input data are loaded in the MATLAB to identify the unknown system parameters of induction heating inverter by using MATLAB system identification toolbox. Estimation of models with pre-selected structures can be performed using system identification toolbox. To validate the models and their limitations, the fitness properties of the models based on percentage best fit and their resonant frequencies are examined.
