Yazar "Güney, Ezgi" seçeneğine göre listele

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    A Comparative Real-Time Speed Control of PMSM with Fuzzy Logic and ANN Based Vector Controller
    (Sirnak Üniversitesi, 2019) Güney, Ezgi; Karagöl, Serap; Demir, Memnun
    This paper presents, analyzed real-time speed control ofPermanent Magnet Synchronous Motor (PMSM) under constant load by using FuzzyLogic (FL) controller and recurrent Artificial Neural Network (ANN) controller.A closed loop PMSM drive system is improved using the mathematical model of thePMSM in Matlab / Simulink. Two types of controllers are used; the firstcontroller is the real-time FL controller and the second controller is areal-time recurrent ANN controller in terms of smoother speed response. Wholedrive systems is simulated in Matlab/Simulink program. The simulation resultsshow that the focused ANN controller produce considerable control performancecompare to the FL controller on controlling speed reference variations
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    Classification of Stockwell Transform Based Power Quality Disturbance with Support Vector Machine and Artificial Neural Networks
    (2022) Güney, Ezgi; Çakmak, Ozan; Kocaman, Cagri
    The detection and classification of power quality events that disturb the voltage and/or current waveforms in the electrical power distribution networks is very important to generate electrical energy and to deliver this energy to the end-user equipment at an acceptable voltage. Various property extraction methods are used to determine the type of disturbances in the electrical signal. In this study, seven power distortions including voltage sag, voltage swell, voltage harmonics, voltage sag with harmonics, voltage swell with harmonics, flicker, transient signals and pure sine as a reference signal is used. Synthetic data are produced in MATLAB using parametric equations based on TS EN 50160 standard. Four kinds of feature extraction as frequency-amplitude, time-amplitude, geometric mean and standard deviation is made with Stockwell Transform (ST), which is one of the methods used for the feature extraction of the determined GKB. Detection of voltage distortions is interpreted through these properties. 640 simulation data is entered into the classifier by using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) and their classification performance is compared.
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    Evaluating Core Loss Effects in Toroidal Current Transformers:A Simulation-Based Comparative Study
    (Institute of Electrical and Electronics Engineers Inc., 2025) Güney, Ezgi
    This study investigates the impact of core losses on the performance and design optimization of toroidal current transformers (TCTs) by comparing iron loss and ideal lossless models in both time and frequency domains. A toroidal current transformer with a 600 A/5 A current ratio and a 120-turn secondary winding of 1.2 mm diameter wire was analysed using COMSOL Multiphysics. A 2D finite element model was developed, integrating Magnetic Fields (mf) and Electrical Circuit (cir) physics interfaces to simulate the electromagnetic behaviour of the transformer. Two models were evaluated: one incorporating magnetic core losses and another assuming a lossless core. Comparative simulations were performed to quantify the influence of core losses on magnetic flux distribution, power dissipation, and coil impedance. The results highlight significant performance deviations in the presence of core losses, particularly under high-frequency operating conditions. This study provides a structured methodology for evaluating TCT performance using Multiphysics Simulation, enabling realistic analysis of energy losses and efficiency. The findings offer valuable insights for optimizing core material selection, transformer geometry, and winding design to enhance accuracy and operational reliability in power distribution and measurement systems. © 2025 IEEE.
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    Feature Extraction and Classification of Power Quality Events Based on Fast Fourier Transformation and Artificial Neural Network
    (Ugur SEN, 2021) Güney, Ezgi; Kocaman, Çagri
    This paper presents an effective method for detection and classification of Power Quality Events (PQE), based on Fast Fourier Transformation (FFT) for event identification and Artificial Neural Network (ANN) technique for classifying of these events. Firstly, synthetic data such as pure sine as a reference, voltage sag, voltage swell, flicker, transient, voltage with harmonics are created in MATLAB based on TS EN 50160 standard. Database with 480 PQE waveforms is generated with 80 samples for each of the 6 types of the waveform with randomly different event amplitude, beginning occurrence time, time duration, frequency component and angle according to a type of event. FFT is used to extract features of the events by decomposing the signal. Then, 16384×480 data are reduced to 480×480 data by applying Principal Component Analysis (PCA) that is prevent over-learning, obtain less runtime using less computing power and reduce data and storage space. Finally, a total of 480 PQE are classified by using ANN. 336 of these PQE are used for training cluster, 72 of PQE are used for verification and the remaining 72 are used for testing. Firstly, the ANN has been trained correctly. The classification performance of the ANN in PQE has been examined by inserting the test into ANN. The performance of ANN is 99.8% for these PQE. The purpose of this research is to provide an artificial intelligence assistant that can fast and accurately advise the power system operators for the networks, and the results also show that the goal has been achieved.
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    The Role of Hybrid Transformers in Sustainable Energy Automation
    (IGI Global, 2025) Güney, Ezgi
    This chapter provides a comprehensive examination of Hybrid Transformers (HTs) as key components in the transition to sustainable and intelligent energy systems. It begins with an overview of the background and importance of HTs, emphasizing their role in improving grid efficiency, power quality, and renewable energy integration. Structural design and operating principles are analyzed, including series, parallel, and multilevel configurations. The chapter also explores enabling technologies such as wide bandgap semiconductors, modular power converters, and AI- based control algorithms. Performance and efficiency analyses address core, conductor, and switching loss reduction strategies. Furthermore, the chapter highlights the contributions of HTs to smart grids, energy management systems, and renewable energy- storage integration. It concludes by discussing key technical challenges, offering potential solutions, and identifying future research directions to support the development and deployment of HTs in advanced power infrastructures. © 2026 by IGI Global Scientific Publishing. All rights reserved.