Browsing by Author "Hatas, H."

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Fpga Implementation of Spwm for Cascaded Multilevel Inverter by Using Xsg
(Institute of Electrical and Electronics Engineers Inc., 2019) Hatas, H.; Genc, N.; Mamizadeh, A.
This study presents Field Programmable Gate Array (FPGA) implementation of Sinusoidal Pulse Width Modulation (SPWM) technique for 5-level Cascaded H-Bridge Multilevel Inverter (CHB-MLI). The fast and easy generation of switching signals for multilevel inverters (MLI) which has complex switching schemes is an important issue. In addition, because of parallel processing, the use of FPGAs in the field of power electronics has increased. MATLAB-Simulink software and Xilinx System Generator (XSG) are used to analyze CHB-MLI and generate architecture of PWM signals which embedded in FPGA. After implementation on Digilent Genesys II FPGA board, the experimental gate signals waveforms are observed on a digital oscilloscope. © 2019 IEEE.
Open-Circuit Fault Detection in T-Type Mli Using Xgboost: a Machine Learning-Based Approach
(Institute of Electrical and Electronics Engineers Inc., 2025) Karakilic, M.; Hatas, H.; Pacal, I.
Prediction of open circuit (OC) faults in a T-Type multilevel inverter (MLI) with high accuracy using machine learning (ML) models is a critical issue for the reliability of power electronic systems. In this study, OC faults that may occur in any of the nine switches in the inverter circuit are detected and classified using a data-driven approach. K-Nearest Neighbors (KNN) and XGBoost algorithms are applied for machine learning based fault detection and their performances are compared. The data set for fault detection is obtained from randomly generated OC faults in the T-Type MLI circuit modeled in MATLAB/Simulink environment. For each fault class, 255 signal samples are obtained for two cycles (4 ms) at a sampling frequency of 5 kHz and used as input data to the ML models. The XGBoost model shows the best performance with an accuracy of 98.37%. This shows that it provides fast and reliable fault detection in large data sets. The confusion matrix, accuracy and loss plots are used to analyze the performance of the model and the study demonstrates the applicability of ML-based fault detection in inverter systems. © 2025 IEEE.