Yazar "Koroglu, Tahsin" seçeneğine göre listele

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    A Comparative Study on the Estimation of Wind Speed and Wind Power Density Using Statistical Distribution Approaches and Artificial Neural Network-Based Hybrid Techniques in Çanakkale, Türkiye
    (Mdpi, 2024) Koroglu, Tahsin; Ekici, Elanur
    In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in canakkale Province, located in the northwest of Turkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network-genetic algorithm (ANN-GA) and ANN-particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model.
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    A Fast Simulation Model for Large Scale Battery Packs Used in Heavy Electric Vehicles
    (Institute of Electrical and Electronics Engineers Inc., 2019) Erdogan, Birand; Savrun, Murat Mustafa; Koroglu, Tahsin; Cuma, Mehmet Ugras; Tumay, Mehmet
    Voltage imbalance occurs in a battery pack due to the differences of internal resistance, temperature, self discharge rate, leakage current etc. reduce the usable energy over time. To overcome voltage imbalances in the battery pack, cell balancing methodologies are used. Although passive balancing methods provide cheap solutions for equalizing imbalances, active balancing methods make it possible to balance faster. This paper presents an active balancing topology for large scale battery packs in a heavy electric vehicle. In order to investigate the performance of the system, a battery pack and bidirectional flyback converter (BiFLC) based active balancing circuit equipped with switch matrix (SwM) has been simulated by using MATLAB/Simulink. A charge transfer bus, which is supplied by the first cell of the battery pack, is used to transfer energy between the source/ destination cells. Thus, the need for the auxiliary circuit/battery is eliminated. A large scale battery pack consist of 176 series cells and 22 BiFLCs is used in the simulation model. Because of using switching models of BiFLCs requires high run time and more complexity, a simple model of BiFLC using current sources is presented. © 2019 IEEE.
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    An improved and fast balancing algorithm for electric heavy commercial vehicles
    (Elsevier, 2021) Erdogan, Birand; Savrun, Murat Mustafa; Koroglu, Tahsin; Cuma, Mehmet Ugras; Tumay, Mehmet
    Nowadays, the tendency towards electric vehicles is increasing and various studies are being carried out in order to eliminate the shortage of battery life. One way to improve battery life is to overcome imbalances between cells in battery packs. The imbalances due to structural differences arising from production and changing operating conditions reduce the utilization of available energy. Passive and active balancing methods are used in order to eliminate these imbalances. Due to the slow equalization speed and energy inefficiency of passive balancing circuits, the trend towards active balancing is increasing. In this paper, an active balancing system with a new balancing algorithm that features fast balancing, multiple charge transfers, and suitable for large battery packs used in electric heavy commercial vehicles is proposed to increase usable energy. To test the algorithm, battery module and bidirectional dc-dc converter based active balancing system simulation study has been performed in Matlab-Simulink. The performance of proposed algorithm is compared with other algorithms and simulation results show that the proposed algorithm has better balancing performance.
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    An Isolated High-Power Bidirectional Five-Level NPC Dual Active Bridge DC-DC Converter with Anti-Windup PI Controller for Electric Vehicle-to-Home Application
    (Institute of Electrical and Electronics Engineers Inc., 2023) Ekici, Elanur; Koroglu, Tahsin; Celik, Dogan
    This paper introduces the design and analysis of an isolated bidirectional five-level (5L) neutral point clamped (NPC) dual active bridge DC-DC converter with an improved anti-windup strategy using proportional-integral (PI) controller for electric vehicle-to-home applications. The proposed converter is more advantageous in that it provides a 5L output voltage at the high-frequency isolation transformer sides thanks to its NPC structure compared to the conventional full-bridge circuit. Reducing switching stresses, providing galvanic isolation, ensuring bidirectional power flow and simple control strategy are the highlights of the proposed structure. A single phase shift (SPS) modulation strategy has been employed to regulate the output voltage and to generate switching signals of the proposed DC-DC converters' switches. To improve the dc-link voltage and to reduce the output voltage tracking errors, the anti-windup PI (PIAW) controller has been utilized instead of the conventional PI controller. To demonstrate the verification of the proposed converter and its controller, a simulation model has been developed in MATLAB/Simulink software. The performance and effectiveness of the proposed converter have been evaluated under steady-state and dynamic variations by the simulation results. © 2023 IEEE.
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    Design and analysis of two-stage bidirectional power converter for vehicle-to-grid technology with fuel cell-battery electric vehicle
    (Elsevier Ltd, 2025) Ekici, Elanur; Koroglu, Tahsin; Çelik, Özgür
    This paper presents the design and analysis of an isolated bidirectional two-stage power converter for vehicle-to-grid (V2G) technology with a fuel cell (FC) battery electric vehicle (FCBEV). In the first stage, the primary side employs a quadratic boost converter to achieve higher voltage levels, while the secondary side uses a voltage balancer circuit to equalize the DC subgrid terminal voltages. In the second stage, a T-type inverter is used for grid connection, effectively minimizing total harmonic distortion (THD). An Artificial Neural Network (ANN)-based maximum power point tracking technique combined with a Genetic Algorithm (GA) has been used to extract the maximum energy from the FC under changing temperature and pressure conditions in the system. The performance of the proposed converter has been evaluated and validated through simulation studies using MATLAB/Simulink software. The results demonstrate that the performance of the proposed converter is satisfactory under varying operating conditions. The grid current THD is maintained below 2.35 % in accordance with IEEE 519 standards. The proposed converter and control strategy facilitate the integration of EVs with renewable energy sources and bipolar hybrid AC/DC microgrids. © 2024 Elsevier Ltd
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    DSP controlled voltage disturbance generator
    (Engineering & Scientific Research Groups, 2018) Savrun, Murat Mustafa; Tan, Adnan; Koroglu, Tahsin; Cuma, Mehmet Ugras; Bayindir, Kamil Cagatay; Tumay, Mehmet
    Voltage disturbance generator (VDG) is a kind of device used to generate voltage disturbances that may occur in the network in order to test and evaluate the performance of the power quality devices such as Dynamic Voltage Restorer (DVR) and Uninterruptible Power Supply (UPS). Some of the voltage disturbance generator systems which are available in the literature and commercially available are complex and expensive while the others are simple, inexpensive but less functional. In this paper, a new transformer based, digital signal processor (DSP) controlled VDG is designed which can generate balanced and unbalanced voltage sag/swell and interruption and can adjust the depth, duration and initiation point of disturbances. The proposed system is composed of bidirectional switches, variable transformers and DSP based controller. 3 phase 20 kVA experimental setup is developed to verify the performance of the designed system with linear and non-linear loads. Moreover, the performance of VDG is also investigated in the compensation tests of DVR and UPS.
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    Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey
    (Yildiz Technical Univ, 2021) Goncu, Onur; Koroglu, Tahsin; Ozdil, Naime Filiz
    Since global solar radiation (GSR) is an important parameter for the design, installation, and operation of solar energy-based systems, it is important to have precise information about it. As the indicating devices are expensive and their requirements such as operation and maintenance should be carried out, the measurement of solar radiation cannot be frequently taken. On the other hand, the measurements of different meteorological parameters such as relative humidity and ground surface temperature are more prevalent in meteorology stations. Therefore, the estimation of solar radiation is a significant parameter for the areas where the measurements could not be performed and to complete the missing information in databases. Many different models, software, and simulation programs are utilized to calculate solar radiation data, provide an economic advantage, and obtain high accuracy. The main purpose of this study is to perform an estimation of solar radiation in Adana, where is on the east of the Mediterranean in Turkey, by using an artificial neural network (ANN) model. The best estimation performance is obtained by optimizing the neuron numbers used in the network's hidden layer with the trial and error method. With this aim, hourly data including wind speed, wind direction, humidity, actual pressure, and average temperature are taken as inputs while solar radiation is taken as a target. All these data, which is for 2018, has taken from the Turkish State Meteorological Service. A linear correlation coefficient value has been obtained to be about 0.87313 with the mean square error (MSE) of 5.8262x10(7) W/m(2) for the testing data set. The ANN's testing/validation results show that it has a low MSE, indicating the accuracy and adequacy of the network model. Besides, the predicted ANN output is evaluated to be remarkably close to the measured target data by considering the linear correlation coefficient.
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    Five-Port Isolated Bidirectional DC-DC Converter for Interfacing a Hybrid Photovoltaic-Fuel Cell-Battery System with Bipolar DC Microgrids
    (Mdpi, 2024) Koroglu, Tahsin; Ekici, Elanur; Savrun, M. Mustafa
    This paper introduces a novel five-port, three-input, dual-output isolated bidirectional dc-dc converter (FPIBC) topology with an effective controller for power-sharing and voltage-balancing in bipolar dc microgrids (BPDCMGs). The proposed converter acts as the interface for the integration of a hybrid generation system comprising a solid oxide fuel cell (SOFC), a photovoltaic (PV) system, and a battery into BPDCMGs. It employs a reduced number of circuit elements compared with similar multiport converter topologies suggested for BPDCMG applications. Symmetrical bipolar output voltages are ensured by a voltage-balancing circuit composed of a fully controlled switch and four diodes. The FPIBC is equipped with different controllers for output voltage regulation and balancing, power sharing, maximum power point tracking of the PV, the optimum operating region of the SOFC, and constant-current, constant-voltage charging of the battery. To verify the viability and effectiveness of the proposed system, a simulation model was developed with a 4.2 kW SOFC, a 3.7 kW PV, and a 140 V 10.8 Ah battery in MATLAB/Simulink. The performance of the FPIBC was evaluated through extensive case studies with different operational modes, including battery charge/discharge states and SOFC and PV parameter changes under varying load conditions. In addition, the proposed system was examined using a daily dynamic load profile. According to the simulation results, a peak efficiency of 97.28% is achieved and the voltage imbalance between the output ports is maintained below 0.5%. It is shown that the FPIBC has advantages over previous converters in terms of the number of ports, number of circuit elements, bipolar output voltage, bidirectional power flow, and efficiency.
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    Implementation of a Novel Hybrid UPQC Topology Endowed With an Isolated Bidirectional DC-DC Converter at DC link
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Koroglu, Tahsin; Tan, Adnan; Savrun, Murat Mustafa; Cuma, Mehmet Ugras; Bayindir, Kamil Cagatay; Tumay, Mehmet
    This paper introduces an original hybrid unified power quality conditioner (HUPQC) topology as an alternative solution to electrical power quality problems. The proposed HUPQC consists of the shunt hybrid active power filter (SHAPF), the dynamic voltage restorer (DVR), and the isolated bidirectional dc-dc converter (BiDC) located at the common dc link. The SHAPF enables reduction in the voltage rating of the dc-link capacitor, helps to reduce the cost and the size of the dc link, and hence reduces switching losses of the voltage source inverter. Besides the novelty of its topology, dynamic reactive power compensation capability is realized for the first time in the literature within HUPQC concept by achieving adaptively controlling dc-link voltage. The BiDC not only provides isolation and bidirectional power flow between the DVR and the SHAPF but also operates to keep the dc-link voltage of DVR constant against adaptively changing dc-link voltage of the SHAPF. In addition to these, a new hybrid voltage sag/swell detection algorithm based on the combination of the improved Clarke transformation and the enhanced phase-locked loop is developed and introduced. In order to verify the viability and effectiveness of the proposed HUPQC topology, experimental studies are carried out.
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    Isolated H-bridge DC-DC converter integrated transformerless DVR for power quality improvement
    (Inst Engineering Technology-Iet, 2020) Savrun, Murat Mustafa; Koroglu, Tahsin; Tan, Adnan; Cuma, Mehmet Ugras; Bayindir, Kamil Cagatay; Tumay, Mehmet
    This study presents a new H-bridge DC-DC converter-based transformerless dynamic voltage restorer topology (DVR). The proposed system can compensate balanced and unbalanced voltage sag/swell that are the most common electrical power quality problems and offers advantages over conventional DVR topologies by providing the isolation with high-frequency transformer (HFT) rather than bulky injection transformers and by employing shunt converter to eliminate the requirement of an energy storage unit. The system is composed of H-bridge DC-DC converter equipped with a HFT with one primary and three secondary windings and transformerless DVR. The single-phase shift modulation method is used for each series converter independently to provide the bidirectional power flow control of DC-DC converter, whereas in-phase compensation method with a hybrid detection algorithm is used to mitigate voltage sag/swell. An LC filter is employed to attenuate the switching ripple harmonics on the output of the DVR. The performance of the proposed system is verified experimentally on a three-phase, three-wire, 380 V, 10 kVA prototype.
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    Modeling and Analysis of Five-Level Reduced Switch Inverter Based Bidirectional Isolated DC-DC Converter
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ekici, Elanur; Koroglu, Tahsin
    This paper addresses the modeling and analysis of a new bidirectional isolated dc-dc converter (BIDC) based on a multilevel inverter topology. The novelty of the study is the utilization of the reduced switch five-level inverter topology instead of the conventional full-bridge dual active bridge. It has the advantage of lower voltage/current stress on the switches at higher voltages as compared to traditional BIDCs. Power transfer between primary and secondary sides is controlled by PI controller-based single phase shift (SPS) modulation technique. The proposed converter stands out for having: i) employing a reduced number of switches compared to other multilevel inverter-based bidirectional dc-dc converter topologies ii) high voltage gain iii) low harmonic level iv) suitable for integrating multiple input multiple output electrical systems and to be used in bipolar dc microgrid applications. In order to validate the performance of the proposed converter, a simulation model has been developed in Matlab/Simulink environment. The performance of the suggested converter and the controller scheme under steady-state and dynamic conditions have been revealed through the simulation results. © 2022 IEEE.
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    Optimizing parameters for additive manufacturing: a study on the vibrational performance of 3D printed cantilever beams using material extrusion
    (Emerald Group Publishing Ltd, 2024) Ekerer, Sabri Can; Boga, Cem; Seyedzavvar, Mirsadegh; Koroglu, Tahsin; Farsadi, Touraj
    PurposeThis study aims to investigate the impact of different printing parameters on the free vibration characteristics of 3D printed cantilever beams. Through a comprehensive analysis of material extrusion (ME) variables such as extrusion rate, printing pattern and layer thickness, the study seeks to enhance the understanding of how these parameters influence the vibrational properties, particularly the natural frequency, of printed components.Design/methodology/approachThe experimental design involves conducting a series of experiments using a central composite design approach to gather data on the vibrational response of ABS cantilever beams under diverse ME parameters. These parameters are systematically varied across different levels, facilitating a thorough exploration of their effects on the vibrational behavior of the printed specimens. The collected data are then used to develop a predictive model leveraging a hybrid artificial neural network (ANN)/ particle swarm optimization (PSO) approach, which combines the strengths of ANN in modeling complex relationships and PSO in optimizing model parameters.FindingsThe developed ANN/PSO hybrid model demonstrates high accuracy in predicting the natural frequency of 3D printed cantilever beams, with a correlation ratio (R) of 0.9846 when tested against experimental data. Through iterative fine-tuning with PSO, the model achieves a low mean square error (MSE) of 1.1353e-5, underscoring its precision in estimating the vibrational characteristics of printed specimens. Furthermore, the model's transformation into a regression model enables the derivation of surface response characteristics governing the vibration properties of 3D printed objects in response to input parameters, facilitating the identification of optimal parameter configurations for maximizing vibration characteristics in 3D printed products.Originality/valueThis study introduces a novel predictive model that combines ANNs with PSO to analyze the vibrational behavior of 3D printed ABS cantilever beams produced under various ME parameters. By integrating these advanced methodologies, the research offers a pioneering approach to precisely estimating the natural frequency of 3D printed objects, contributing to the advancement of predictive modeling in additive manufacturing.
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    Proper estimation of surface roughness using hybrid intelligence based on artificial neural network and genetic algorithm
    (Elsevier Sci Ltd, 2021) Boga, Cem; Koroglu, Tahsin
    The surface roughness is a crucial index that is commonly used in the machining process to evaluate the final product quality. This paper investigates the effect of different machining parameters on the surface roughness of the high-strength carbon fiber composite plate, manufactured by utilizing the vacuum infusion process, under dry end milling conditions. Besides, a hybrid intelligence approach consisting of artificial neural network (ANN) whose parameters are tuned by genetic algorithm (GA) is introduced for accurate estimation of surface rough-ness. To construct a database for the ANN, the experimental milling tests have been carried out according to the Taguchi optimization method with the design of a mixed orthogonal array L-32 (2(1) x 4(2)). The influence of the machining parameters such as cutting tools, feed rate, and spindle speed on surface roughness have been examined by using analysis of variance (ANOVA). The analyses reveal that the cutting tool and the feed rate are the most effective factors in the surface roughness of the composite material. It is also determined that the experiment with A1B2C1 combination (TiAlN coated cutting tool, 5000 rpm spindle speed, and 250 mm/rev feed rate) gives the optimal result. The proposed hybrid ANN-GA algorithm provides a good prediction correlation ratio (R = 0.96177) indicating that the estimated and the measured surface roughness values are remarkably close to each other. The mean square error (MSE) specifying the accuracy and adequacy of the network model is obtained as 0.074 during the 33th iteration of the GA.