Elektrik-Elektronik Mühendisliği Bölümü Makale Koleksiyonu

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https://hdl.handle.net/20.500.12418/593

Listeleniyor 1 - 19 / 19

The Electromagnetic Field Intensity Map of Sivas Cumhuriyet University Campus at Different Frequencies
(2024) Demirkazık, Ayşe; Türk, Tarık; Türkay, Yavuz; Sarı, Vekil; Birdal, Anılcan; Onocak, Gülistan; Yıldırım, Furkan
The speedy progress of the global system for mobile communication services and the ensuing increased electromagnetic field (EMF) exposure to the human body have generated debate on the potential hazard with attentiveness to human health. The aim of this study is to provide a homogeneous distribution of electromagnetic field sources on our university campus and determine possible risk zones via the electromagnetic intensity maps drawn using the data to be obtained from the measurement results. It was determined how much electromagnetic intensity of the campus was under 4 different high frequencies (900, 1800, 2400, and 2600 MHz), which were determined using the geographic information system. Electromagnetic field measurements were performed at points where the topographic structure of the land allowed. These measurements were taken at intervals of about 50 meters in the parts with building density, while at intervals ranging between 100 and 120 meters in the open areas where there were no buildings. The geographic location information about the points where these measurements were made was specified with an accuracy of cm level. As a result, our measurement values are also below the limit values of the limit electromagnetic values determined by ICNIRP.
Design and Implementation of a Wireless Power Transfer System for Electric Vehicles
(2024) Sarı, Vekil
Wireless power transfer (WPT) systems, which have been around for decades, have recently become very popular with the widespread use of electric vehicles (EVs). In this study, an inductive coupling WPT system with a series–series compensation topology was designed and implemented for use in EVs. Initially, a 3D Maxwell (ANSYS Electromagnetics Suite 18) model of the system was generated. The impact of individual parameters on the coupling coefficient was analyzed through systematic variations in each parameter’s values. As a result, a system with a higher coupling coefficient was obtained. Using this system, three distinct load cases were investigated for their efficiency in the Simplorer (ANSYS Electromagnetics Suite 18) circuit. Subsequently, a prototype of the system was constructed, and the experimental results were compared with the model’s results. This study shows that both the output power and the efficiency of the system increase as the load resistance increases. The results obtained in this study are anticipated to offer valuable insights for the enhancement of WPT system design.
Tap Staggering Analysis and Effects on the Adaptive Protection System in Networks With Renewable Energy Sources
(IEEE, 05.12.2023) Kasap, Hakan; Pürlü, Mikail; Emre Türkay, Belgin; Ganjavi, Reza
Increasing the penetration of renewable energy sources as distributed generation in modern power grids presents several challenges, including voltage level issues and protection system failures resulting from bidirectional power flow and changing network dynamics. Voltage limit violations could potentially damage the utility equipment and lead to power quality problems. Innovative Volt/VAr control methods, such as tap staggering, can be used to overcome these problems. Tap staggering utilizes circulating current between parallel transformers to provide reactive power absorption from the network. However, the absorption of reactive power by the primary substation using tap staggering poses a potential risk to the protection system, particularly the Directional Overcurrent protection scheme with Load Blinder. This could lead to failures or nuisance trips during normal load conditions. In this study, a real power system is modeled using real data in PSS CAPE with 120 scenarios generated through tap staggering application, all based on 24-hour demand/wind generation data. The Tap Staggering Macro and Adaptation Protection Macro were developed for the purpose of analyzing these scenarios. The study demonstrates that tap staggering effectively mitigates overvoltage issues on the transmission system by absorbing reactive power. Although there is an increase in active power losses when tap staggering level is increased on the parallel transformers, the power loss remains within reasonable limits. Despite its benefits, tap staggering has been found to affect the Directional Overcurrent with Load Blinder protection scheme, limiting power transfer generated through wind turbines in the distribution network. This results in changes to the protection scheme’s Directional Overcurrent Pickup, Load Blinder Resistance, and Load Blinder Alpha parameters, requiring adaptation in all scenarios. After adaptation, the protection system operates reliably, guaranteeing efficient and unrestricted transmission of distributed generation power to the grid.
Next-Generation Application-Based Artificial Intelligence in Modeling and Estimation for Ni/n-GaAs/In Schottky Barrier Diode
(Wiley - V C H Verlag GmbbH & Co., 14.01.2023) Doğan, Hülya; Koçkanat,Serdar
Herein, for Ni/n-GaAs/In Schottky barrier diode, experimental measurement, modeling, data generation from the model, and parameter estimation processes are simultaneously carried out. In the experimental step, Ni/n-GaAs/In Schottky barrier diodes are fabricated and annealed from the temperature of 200 °C up to 600 °C with 100 °C steps. Current values are recorded by applying voltage to the diode contacts from −1 V up to 0.5 V. In the modeling step, 1503 experimental current–voltage data are used for 19 different regression models. For Adaptive Neuro Fuzzy System (ANFIS), when root mean square error, mean square error, mean absolute error, and coefficient of determination are calculated 6.0341e-07, 3.6410e-13, 2.3873e-07, and 0.9999 for training, they are obtained 5.8904e-07, 3.4697e-13, 2.3083e-07, and 0.9999 for testing. In the estimation step, the values of electrical parameters are estimated by using Mayfly algorithm. Estimations are performed for all annealing temperatures. In addition, current–voltage data for the annealing temperature of 350 °C are produced by the ANFIS model. Thus, a new-generation artificial intelligence application, that includes measurement, modeling, and estimation for the Ni/n-GaAs/In Schottky barrier diode with varying annealing temperatures, is realized and a new perspective is provided to researchers and practitioners.
FDTD-based SAR calculation of a wearable antenna for wireless body area network devices
(INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES, 2023) Kaburcuk, Fatih; Savcı, Hüseyin Şerif
Wireless-connected wearable electronics are finding extensive usage for diagnostic and therapeutic purposes after the globally spread pandemic disease of COVID-19. Although they are undoubtedly helpful for keeping physical distance, their health effects are still under investigation from different aspects and are still a concern for the end-users. In this study, a custom M-shaped wearable antenna covering the wireless body area network and wireless local area network frequencies is designed, built, and measured. A beret cap made from a 2 mm thick textile is used as a substrate. The specific absorption rate (SAR) in a realistic human-head model due to electromagnetic energy produced by the antenna is evaluated using the finite-difference time-domain method. The SAR distributions for 1-g and 10-g tissues are calculated at 2.4 and 5.8 GHz. It is shown that the obtained maximum SAR values for 1-g and 10-g tissues at each frequency of interest were less than the limits determined by IEEE RF exposure guidelines and standards.
Effect of Change of Reluctance Launcher Parameters on Projectile Velocity
(2023) Sari, Vekil
In this study, it is aimed to increase the projectile velocity by changing some parameters of the reluctance launcher. Studied parameters are the initial position of the projectile, the coating of the coil with ferrite, the coil length and the barrel outer diameter. A 3D Maxwell model of the reluctance launcher was generated to examine the effect of the change of these parameters on the projectile velocity. After the 3D model of the launcher was generated, analysis was done for each parameter. During the analysis for a parameter, the other parameters were kept constant. As a result of the analysis of the projectile position, it was determined that the projectile velocity is the highest when the projectile position is −2. It has been determined that the velocity of the projectile increases if the coil is coated with ferrite. It has been determined that the projectile velocity is the highest when the coil length is 7 cm. It has been determined that the highest projectile velocity is obtained when the barrel outer diameter is 18 mm. Using these results, the Maxwell model of the improved launcher was generated. The projectile velocity in the Maxwell model of the initial launcher is 19.24 m/sec. The projectile velocity of the improved launcher in the Maxwell model is 25.8 m/sec. By improving the launcher, a velocity increase of 34.09% was achieved. Later, this launcher was built and the parameters were investigated experimentally. In the experimental work, the projectile velocity of the initial launcher was measured as 19.11 m/sec, and the projectile velocity of the improved launcher was measured as 24.9 m/sec. As a result of the experimental work, a velocity increase of 30.29% was obtained by improving the launcher.
Long term electricity load forecasting based on regional load model using optimization techniques: A case study
(Taylor&Francis, 2022) Şeker, Mustafa
Long-term load forecasting is a significant and complex topic in electric distribution systems. Forecasters is need to proper forecasting methodologies and smart solutions to minimize complexity. In this study, regional longterm load forecasting is presented, for Sivas province of Turkey, taking into account the development plan of the municipality, and subscriber profiles. Firstly, the municipality development plan is divided into regions of similar load characteristics. The load demand values of each region are defined mathematically using the S curve. The optimal parameter values of the S curve are calculated using meta-heuristic methods such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Harris Hawk Optimization (HHO). The obtained results are compared with the results of the econometrics-based (top-to-bottom) approach and actual consumer projection. The consumption values between 2004 and 2014 are used for parameter estimation of S curves. The consumption values obtained as the result of analysis the period between 2015 and 2018 were selected as test data. The result is shown that S curve-based regional demand forecast demonstrated more convenient results using the HHO algorithm with statistical values of RE = 1.3362, MAE = 1.5145, RMSE = 1.80385 and STD = 2.122 can be applied to the forecast regional electricity consumption. The proposed method is simple and can be easily applied to forecast the total consumption of the power load for a province any load forecasting region. The presented approach can be used to define the future projections of electricity distribution systems and determine the correct investment strategies.
Parameter estimation of positive lightning impulse using curve fitting-based optimization techniques and least squares algorithm
(Elsevier B.V., 2022) Şeker, Mustafa
Lightning is leads to various adversary effects on energy systems and living beings. These negative effects can be reduced only through a better understanding of their characteristic behaviours of lightning. The present study makes a comprehensive evaluation on the calculation of the function parameters of different mathematical models in the literature for the modelling of lightning impulses using the optimization-based curve-fitting method. For the lightning current waveform, artificial lightning current waveforms at different magnitudes were used at 10/350 μs, experimentally measured from Dresden High Voltage Test Laboratory using current impulse generator. With these waveforms, Pulse, Double Exponential, and Heidler function parameters were calculated using Genetic Algorithm(GA), Particle Swarm Optimization(PSO), and Grey Wolf-Cuckoo Search(GWO–CS) optimization algorithms for modelling lightning impulses. The results were statistically evaluated. The simulation findings reveal that the optimization-based curve fitting approach is simple, solid, and efficient tool for accurately extracting the peak value, current derivative, charge, specific energy and the front and tail times of lightning impulse forms using Pulse, Double Exponential, and Heidler functions. The Pulse function parameters calculated by GWO–CS have a higher accuracy for describing the artificial lightning current waveform at standard deviation-5.134e-2, relative error-2.11e-10, mean absolute error-6.14e-12 and root mean square error- 2.47e-6.
The application of different optimization techniques and Artificial Neural Networks (ANN) for coal-consumption forecasting: a case study
(Mineral and Energy Economy Research Institute Polish Academy of Science, 2022) Şeker, Mustafa
The demand for energy on a global scale increases day by day. Unlike renewable energy sources, fossil fuels have limited reserves and meet most of the world's energy needs despite their adverse environmental effects. This study presents a new forecast strategy, including an optimization-based S curve approach for coal consumption in Turkey. For this approach, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) are among the meta-heuristic optimization techniques used to determine the optimum parameters of the S curve. In addition, these algorithms and Artificial Neural Network (ANN) have also been used to estimate coal consumption. In evaluating coal consumption with ANN, energy and economic parameters such as installed capacity, gross generation, net electric consumption, import, export, and population energy are used for input parameters. In ANN modeling, the Feed Forward Multilayer Perceptron Network structure was used, and Levenberg-Marquardt Back Propagation has used to perform network training. S curves have been calculated using optimization, and their performance in predicting coal consumption has been evaluated statistically. The findings reveal that the optimization-based S-curve approach gives higher accuracy than ANN in solving the presented problem. The statistical results calculated by the GWO have higher accuracy than the PSO, WOA, and GA with R2=0.9881, RE=0.011, RMSE=1.079, MAE=1.3584, and STD=1.5187. The novelty of this study, the presented methodology does not need more input parameters for analysis. Therefore, it can be easily used with high accuracy to estimate coal consumption within other countries with an increasing trend in coal consumption, such as Turkey.
The effects of electron irradiation on the current-voltage and capacitance-voltage measurements of Sn/p-GaAs/Au diodes
(25/01/2022) Duman, Songül; Kaya, Fikriye Şeyma; Doğan, Hülya; Turgut, Güven; Şahin, Yılmaz
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Neural network estimations of annealed and non-annealed Schottky diode characteristics at wide temperatures range
(15/06/2022) Doğan, Hülya; Duman, Songül; Torun, Yunis; Akkoyun, Serkan; Doğan, Seydi; Atici, Uğur
n this study, Artificial Neural Network (ANN) model has been proposed to characterize the annealed and the non-annealed Schottky diode from experimental data. The experimental current values of Ni/n-type 6H–SiC Schottky diode for the voltages applied to the diode terminal starting from 80 K with 20 K steps up to 500 K temperature were measured for both non-annealed and annealed Schottky diodes. The applied voltage has been varied starting from -2 V with 10 mV steps up to +2 V for each temperature value. The modeling performance has been assessed according to the varying number of neurons in the hidden layer, starting from 5 to 50 neurons, thereafter the optimum number of neurons has been obtained for both annealed and non-annealed ANN models. The minimum Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) indices values for both annealed and non-annealed diodes have been obtained with 40 neurons for both the training and test phase.
Parameter Estimation of AI/p-Si Schottky Barrier Diode Using Different Meta-Heuristic Optimization Techniques
(2022) Doğan, Hülya
: Schottky barrier diodes (SBD) are crucial in the electronics sector. The electronic properties of SBD are characterized by three basic electrical parameters as the ideality factor (n), barrier height (ΦSB) and series resistance (RS). These parameters are significant in designing and producing SBD. This paper presents a comprehensive review of metaheuristic optimization techniques used to de termine the fundamental electrical parameters of SBD using experimental forward current–voltage (I-V) characteristics. In the study, popular meta-heuristic optimization techniques, such as GA, PSO, ALO, EO, DA, HHO, GWO, WOA, MFO, MVO, and SCA algorithms, are employed for the parameter estimation of SBD. Among these chosen algorithms, meta-heuristic optimization techniques, such as GWO, WOA, HHO and AHA, have been used for the first time in the literature for parameter estimation of SBD. Firstly, parameter values have been calculated using experimental (I-V) character istics. Following that, the findings were compared to the values that had been estimated utilizing optimization techniques. Moreover, the performance of meta-heuristic optimization algorithms in determining the basic parameters of SBD was evaluated statistically. Results show that AHA has higher and symmetrical estimation performance than other presented algorithms in determining the basic parameters of SBD with R 2 = 0.999925806, MAE = 2.79065 × 10−7 , RMSE = 7.49521 × 10−7 , RE = 0.422088668, and STD = 7.68031 × 10−7 statistical values
A Novel Approach for Polyphase Filter Bank Design Using ABC Algorithm
(21.12.2022) Ahmet Loğoğlu; Serdar Koçkanat; Nurhan Karaboğa
—Polyphase filter banks (PFBs) are the most preferred multirate structures for subband coding in Digital Signal Processing (DSP) and communication. For PFB design, there are many important design parameters such as filter length and frequency selectivity. Also, to realize the desired frequency response in designs, stopband and passband attenuation are of considerable importance. In PFB design, researchers and practitioners frequently use iterative and meta-heuristic optimization methods. Heuristic techniques have a significant problem-solving ability in continuous and discrete solution space. Therefore, they give better results than other suggested methods, and their performance depends on the control parameters. In this study, Artificial Bee Colony (ABC) algorithm was employed for suggested design problem of PFB. In the first stage, the control parameters of the ABC algorithm were examined to improve the performance of the proposed PFB problem. In the second stage, the analysis was carried out by changing filter lengths (8-256) and filter band frequencies (0.3-0.7/0.4-0.6). All results obtained were also compared with the Particle Swarm Optimization algorithm (PSO) and the Genetic algorithm (GA). Finally, a DSP application of PFB was carried out according to best results achieved by the ABC algorithm for filter lengths and frequencies.
Modeling of Schottky diode characteristic by machine learning techniques based on experimental data with wide temperature range
(Science Direct, 2021) Torun, Yunis; Doğan, Hatice
In this study, 4 common machine learning methods have been used to model the I–V characteristic of the Au/Ni/n-GaN/undoped GaN Schottky diode. The current values of previously produced Au/Ni/n-GaN/undoped GaN Schottky diode against the voltages applied to the diode terminal starting from the temperature of 40K up to 400K with 20K steps were measured. Models were created using Adaptive Neuro Fuzzy System, Artificial Neural Network, Support Vector Regression, and Gaussian Process Regression techniques using experimental data containing 5192 samples in total. After determining the combinations and specifications for each one that provide the lowest model error of each model, the performances of the obtained models were compared with each other concerning the various performance indices. The performance of the ANFIS model was found to be much better than the others in both the learning and test phases with RMSE model errors as 6.231e-06 and 6.806e-06, respectively. Therefore, it was proposed as a powerful tool for modeling I–V characteristics at all temperature values between 40K and 400K.
Neuro-fuzzy modeling of deformation parameters for fusion-barriers
(Science Direct, 2021) Torun, Yunis; Akkoyun, Serkan
The fusion-barrier distribution is very sensitive to the structure of the colliding nuclei such as nuclear quadrupole and hexadecapole deformation parameters and their signs. If the nuclei that enter the fusion reaction are deformed, the barrier problem becomes complicated. Therefore the deformation parameters are taken into account in the calculations. In this study, Neuro-Fuzzy approach, ANFIS, method has been used for the estimation of ground-state quadrupole () and hexadecapole () deformation parameters for the nuclei. According to the results, the method is suitable for this task and one can confidently use it to obtain the data that is not available in the literature.
Efficient Electromagnetic Analysis of a Dispersive Head Model Due to Smart Glasses Embedded Antennas at Wi-Fi and 5G Frequencies
(Applied Computational Electromagnetics Society Journal, 07-02-2021) Kaburcuk, Fatih; Elsherbeni, Atef
Numerical study of electromagnetic interaction between an adjacent antenna and a human head model requires long computation time and large computer memory. In this paper, two speeding up techniques for a dispersive algorithm based on finitedifference time-domain method are used to reduce the required computation time and computer memory. In order to evaluate the validity of these two speeding up techniques, specific absorption rate (SAR) and temperature rise distributions in a dispersive human head model due to radiation from an antenna integrated into a pair of smart glasses are investigated. The antenna integrated into the pair of smart glasses have wireless connectivity at 2.4 GHz and 5th generation (5G) cellular connectivity at 4.9 GHz. Two different positions for the antenna integrated into the frame are considered in this investigation. These techniques provide remarkable reduction in computation time and computer memory.
A Dual-Band and Low-Cost Microstrip Patch Antenna for 5G Mobile Communications
(Applied Computational Electromagnetics Society Journal, 07-07-2021) Kalinay, Gürkan; Chen, Yiming; Demir, Veysel; Kaburcuk, Fatih; Elsherbeni, Atef Z.
This paper investigates the numerical and experimental analysis of a low-cost and dual-band microstrip patch antenna for the fifth generation (5G) mobile communications. The numerical analysis of the proposed antenna is performed using the computational electromagnetic simulator (CEMS) software which is based on the finite-difference time-domain (FDTD) and CST software which is based on the finite integration technique (FIT). The performance of the proposed antenna designed and fabricated on a low-cost FR-4 substrate is verified with the simulated and measured results. The antenna operates at dual frequency bands which are 24 and 28 GHz. The antenna maximum gain values are 3.20 dBi and 3.99 dBi in the x-y plane at 24 and 28 GHz, respectively. The proposed antenna provides almost omni-directional patterns suitable for 5G mobile communication devices.
A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings
(Elsevier, 2021) GOREKE Volkan, SARI Vekil, KOCKANAT Serdar
Unfortunately, Coronavirus disease 2019 (COVID-19) is spreading rapidly all over the world. Along with causing many deaths, it has substantially affected the social life, economics, and infrastructure worldwide in a negative manner. Therefore, it is very important to be able to diagnose the COVID-19 quickly and correctly. In this study, a new feature group based on laboratory findings was obtained considering ethnical and genetic differences for interpretation of blood data. Then, using this feature group, a new hybrid classifier architecture based on deep learning was designed and COVID-19 detection was made. Classification performance indicators were obtained as accuracy of 94.95%, F1-score of 94.98%, precision of 94.98%, recall of 94.98% and AUC of 100%. Achieved results were compared with those of the deep learning classifiers suggested in literature. According to these results, proposed method shows superior performance and can provide more convenience and precision to experts for diagnosis of COVID-19 disease. (C) 2021 Elsevier B.V. All rights reserved.
Electronic energy spectra in a multiple quantum well within external electric and tilted magnetic fields
(IOP PUBLISHING LTD, 2000) Amca, R; Ergun, Y; Sokmen, I; Sari, H
The analytical solution of the Schrodinger equation for a multiple-quantum-well system subjected to an externally applied electric field in the growth direction and an externally applied tilted magnetic field are obtained and results are discussed. The dependence of the energy spectrum of the system on the external electric field as a function of the orbit centre is also discussed and the behaviour of the wavefunctions of the system is examined.

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