Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/411
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Article Citation - Scopus: 4Cost Optimization of Oil Type Distribution Transformer Using Multi-Objective Genetic Algorithm(Erol Kurt, 2024) Iskender, I.; Yükselen, E.; Telli, S.The demand for electrical energy is increasing day by day with the development of technology in the world. Distributing electric energy to all regions that need energy is a principal issue, and this necessitates the use of transformers to convert the voltage to the desired level. Accordingly, the use of transformers, one of the electrical devices converting AC voltage level at a defined frequency has grown significantly. In this study, design parameters of a 25 kVA, 33/0.4 kV, Yzn11, oil-type distribution transformer are optimized using the Multi-Objective Genetic Algorithm (MOGA) technique by decreasing the weight of the significant materials and manufacturing cost. Electromagnetic analysis of the transformer is performed with ANSYS Maxwell based on the design results obtained from the optimization study for validation of the method. The experimental design parameters are also compared with the optimization results. It is observed that optimum results are achieved by using the proposed approach. © 2024 Published by peer-reviewed open access scientific journal, JES at DergiPark (https://dergipark.org.tr/jes)Article Citation - WoS: 4Citation - Scopus: 6Case Study on Thermal Optimization of Oil Immersed Transformer Used in Solar Power Plant Based on Genetic Algorithm and Computational Fluid Dynamics(Vinca inst Nuclear Sci, 2023) Iskender, Ires; Yukselen, EmirTransformers are one of the most capital investments in the solar power generation. Their safe and stable operations in the electrical networks are important. The main failure factor of transformers is the high temperature generated by the losses during operation, which increases the probability of insulation damage that significantly affects the useful life of transformer. Considering the importance of oil temperature and its effects on the life of the transformer, a numerical method is developed in this paper to optimize the cooling system of the transformer. In this regard, genetic algorithm is used as an optimization method to minimize the total cost of the cooling system while maintaining the required thermal conditions of the transformer. A comprehensive parametric study is carried out among the effective cooling geometry parameters using 3-D electromagnetic and thermal models of the photovoltaic transformer to evaluate and analyze the temperature distribution. The accuracy and feasibility of the proposed method is established by comparing the numerical results with those obtained from the experimental test. The results of the proposed method are found to be in a good agreement with the experimental and simulation results.Article Antenna Synthesis by Levin's Method Using Reproducing Kernel Functions(Applied Computational Electromagnetics Soc, 2023) Sener, GokerAn antenna synthesis application is presented by solving a highly oscillatory Fourier integral using a stable and accurate Levin's algorithm. In antenna synthesis, the current distribution is obtained by the inverse Fourier integral of the antenna radiation pattern. Since this integral is highly oscillatory, the Levin method can be used for its solution. However, when the number of nodes or the frequency increases, the Levin method becomes unstable and ineffective due to the large condition number of the interpolation matrix. Thus, an improved scheme of the method is used in an antenna synthesis application in which reproducing kernel functions are used as the basis of the approximation function. The accuracy of the new method is verified by a log-periodic antenna example. The error and stability analysis results show that the new method is more stable and accurate than other well-known kernels, especially for a large number of nodes.Article Citation - WoS: 6Citation - Scopus: 13Sdn-Driven Internet of Health Things: a Novel Adaptive Switching Technique for Hospital Healthcare Monitoring System(Wiley-hindawi, 2022) Alkhayyat, Ahmed; Abedi, Firas; Jawad, Aqeel Mahmood; Abosinnee, Ali S.; Preveze, BarbarosIn the last decent, the number of Internet of Things (IoT) health-based paradigm reached to a huge number of users, services, and applications across different disciplines. Thus, hundreds of wireless devices seem to be distrusted over a limited or small area. To provide a more efficient network, the software-defined network (SDN) thought to be a good candidate to deal with these huge number of wireless users. In this work, after a novel SDN algorithm is proposed for the hospital environment, it is also designed and integrated into an Internet of Health Things (IoHT) paradigm. The novel algorithm called adaptive switching (AS) is proposed as a novel adaptive access strategy based on adaptively hoping among existing Go-Back-N and Selective Repeat techniques. Finally, the throughput performance of the proposed AS method is compared with the performances of traditional Go-Back-N and Selective Repeat ARQ methods using the developed MATLAB simulation. For this, an optimal Perror rate that the network should prefer to switch either from Go-Back-N to Selective Repeat or from Selective Repeat to Go-Back-N method to maximize the network throughput performance is determined. The evaluated results are also confirmed by theoretical calculation results using well-known Mathis throughput formula. It is observed from the simulation results that the best throughput performance can be evaluated, when AS switches to Go-Back-N if the Perror is less than 3.5% and it switches back to Selective Repeat when the Perror is greater than 3.5%. By this way, it is also observed that the throughput always has its best possible results for all Perror rates and up to 37.52% throughput improvement is provided by the use of novel proposed adaptive switching (AS) algorithm.Article Navigation Under GNSS Denied Environments: Zero Velocity and Zero Turning Update(2022) Çifdalöz, OğuzhanThe objective of this paper is to present a method which bounds the error of an inertial navigation system (INS) when Global Navigation Satellite System (GNSS) is not available. Inertial navigation systems utilize gyroscopes and accelerometers, and calculate velocity, position and attitude, essentially by integrating the measurements obtained from these sensors. Due to the nature of integration, INS are notoriously prone to sensor biases and drifts. Typically, GNSS is used to correct the navigation system errors caused by the inertial sensor measurements. However, in GNSS degraded or denied environments, alternative solutions are required. If the platform on which an INS is mounted is known or estimated to be stationary, zero-velocity update (ZUPT) and/or zero turning update (ZTUPT) algorithms can be applied in order to bound the navigation system errors. Under certain assumptions, ZUPT based algorithms can be applied when the platform is not stationary. If a vehicle’s motion is constrained by the design of its kinematics, i.e. if it can be assumed that the vehicle cannot move or rotate along one or more of its body axes, ZUPT assisted Kalman estimators can be used to correct the errors along those axes. Potentially, ZUPT based estimation algorithms can also be utilized when a sufficiently high fidelity vehicle model is available. In this paper, the implementation of zero-velocity update (ZUPT) and zero turning update (ZTUPT) algorithms are analyzed for the purpose of estimating and bounding inertial navigation errors. The basic principle in navigation is based on combining the data obtained from the sensors onboard and the inertial navigation system through an Extended Kalman filter. Although this process requires additional software components, it potentially offers increased system accuracy and reliability. Incorporating the kinematics of the vehicle, along with a ZUPT and/or ZTUPT algorithm, provides additional data to feed into the Kalman filter and increases the efficiency of error estimation. Estimated error is then fed back into the INS algorithm in order to counteract the sources of error.Article Citation - WoS: 1Citation - Scopus: 1Sustainable Management of a Renewable Fishery Resource With Depensation Dynamics From a Control Systems Perspective(Gazi Univ, 2022) Cıfdaloz, OguzhanHuman societies are exploiting natural renewable sources such as fisheries, forests, groundwater basins, rivers, and soil at an increasing intensity. Around the world, these resources are being managed by various institutions or governments. One of the challenges faced by institutions is to develop strategies and policies to effectively manage these renewable resources under social and ecological uncertainties, disturbances, policy implementation difficulties, and measurement errors. In this paper, a fishery is considered as an example and the problem of managing a fishery is approached from a control systems perspective. The justification behind this approach is due to the observation that the problem of managing a renewable resource can be posed as a control systems problem and that the discipline of control systems possesses tools and methods to deal with model uncertainties, external disturbances, measurement errors and implementation issues. For the fishery, a depensation type population dynamics model is considered. Depensatory models are used in social/ecological systems in order to model dynamics of certain species of fish populations. An optimal control strategy based on Pontryagin’s Maximum Principle is derived and its sustainability and robustness properties with respect to parametric uncertainties, measurement errors and disturbances are examined. Finally, a sub-optimal but more robust control strategy is proposed and its robustness properties are provided. The main objective of the paper is to show that a control systems engineering approach can be applied to a social-ecological problem and it can provide easy to implement management strategies, insight, and guidance into the management of renewable resources.Article Üç Fazlı Kuru Tip Transformatör Verimliliği İçin Meta Sezgisel Algoritma Tabanlı Yaklaşımlar(2021) İskender, İres; Kül, Seda; Celtek, Seyit AlperenTransformatörler, elektrik enerjisinin verimli iletimi ve dağıtımına önemli katkı sağlayan unsurlar olarak kabul edilir. Gerilim ve akım seviyelerini ters orantılı olarak değiştirme yeteneği, iletken kayıplarının azaltılmasına yardımcı olur. Bununla birlikte, günümüzün daha önemli verimlilik işaretlerine yönelik katı gereksinimleri, bir güç sistemindeki bireysel bileşenlerin verimliliğine dikkat çekiyor. Bu nedenle, temel işlevlerinden ödün vermeden transformatörlerin verimliliğini en üst düzeye çıkarmak için büyük çaba sarf edilmektedir. Bu karmaşık bir sorundur ve gelişmiş tasarım araçlarının kullanılmasını gerektirir. Son yıllarda geliştirilen meta-sezgisel yöntemler, tasarım süresinde tasarruf ve optimum çözümü bulmada büyük başarı sağladıklarından elektrik mühendisliğinde kullanılmaktadır. Bu çalışmada sırasıyla Parçacık Sürü Optimizasyonu (PSO), Benzetimli Tavlama (SA) ve Ağaç Tohum Algoritması (TSA) yöntemlerini kullandık. Amaç, üç fazlı kuru tip transformatörler için bir tasarım metodolojisi geliştirmek ve verimliliklerini en üst düzeye çıkarmaktır. Üç algoritmanın sonuçları, optimum çözümü doğrulamak için karşılaştırılır. Prosessin gösterimi için üç fazlı 100 kVA kuru tip bir transformatör kullanılır. Transformatörün matematiksel modeli oluşturulduktan sonra transformatör parametreleri, akım yoğunluğu (s) ve transformatör demir kesiti kabul edilebilirliği (C) optimize edilmiştir. Sonuç olarak, transformatörlerin verimlerinin geleneksel tekniklerle elde edilenin üzerinde artırılabileceği gözlemlenmiştir. Verimlilik optimize edilmiş ve 0.975'ten 0.9844'e yükseltilmiştir.Article Citation - WoS: 7Citation - Scopus: 7Entangled Microwave Photons Generation Using Cryogenic Low Noise Amplifier (Transistor Nonlinearity Effects)(Iop Publishing Ltd, 2022) Salmanogli, AhmadThis article mainly focuses on important quantum phenomenon called entanglement arising the nonlinearity property. This study uses a unique approach in which transistor nonlinearity effect (third-order nonlinearity) entangled microwave photons are created in a cryogenic low-noise amplifier (LNA). For entanglement analysis, the Hamiltonian of the designed cryogenic LNA (containing two coupled oscillators) is derived, and then, using the dynamic equation of motion, the oscillator's number of photons and the phase-sensitive cross-correlation factor are calculated in the Fourier domain to calculate the entanglement metric. The oscillators are coupled to each other through the gate-drain capacitor, and nonlinear transconductance is as an important factor strongly manipulating the entanglement. As a main conclusion, the study shows that the designed circuit using transistor third-order nonlinearity has the ability to generate the entangled microwave photons at very low intrinsic transconductance and more importantly when the noise figure (NF) is strongly minimized. As a complementary task, the printed circuit board of the cryogenic LNA is designed and simulated to verify the ability of the circuit to achieve an ultralow NF, by which the probability of the generation of entangled microwave photons is increased.Article Efficient Task Scheduling in Cloud Systems With Adaptive Discrete Chimp Algorithm(2022) Gündüzalp, Emrullah; Yıldırım, Güngör; Tatar, YetkınSuccessful task scheduling is one of the priority actions to increase energy efficiency, commercial earnings, and customer satisfaction in cloud computing. On the other hand, since task scheduling processes are NP-hard problems, it is difficult to talk about an absolute solution, especially in scenarios with large task numbers. For this reason, metaheuristic algorithms are frequently used in solving these problems. This study focuses on the metaheuristic-based solution of optimization of makespan, which is one of the important scheduling problems of cloud computing. The adapted Chimp Optimization Algorithm, with enhanced exploration and exploitation phases, is proposed for the first time to solve these problems. The solutions obtained from this adapted algorithm, which can use different mathematical functions, are discussed comparatively. The proposed solutions are also tested in the CloudSim simulator for different scenarios and they prove their performance in the cloud environment.Article Ear Semantic Segmentation in Natural Images With Tversky Loss Function Supported Deeplabv3+ Convolutional Neural Network(2022) Kacar, Umit; Inan, TolgaSemantic segmentation is a fundamental problem for computer vision. On the other hand, for studies in the field of biometrics, semantic segmentation is gaining more importance. Many successful biometric recognition systems require a high- performance semantic segmentation algorithm. In this study, we present an effective ear segmentation technique in natural images. A convolutional neural network is trained for pixel-based ear segmentation. DeepLab v3+ network structure, with ResNet-18 as the backbone and Tversky lost function layer as the last layer, has been trained with natural and uncontrolled images. We perform the proposed network training using only the 750 images in the Annotated Web Ears (AWE) training set. The corresponding tests are performed on the AWE Test Set, University of Ljubljana Test Set, and the Collection A of In-The-Wild dataset. For the Annotated Web Ears (AWE) dataset, intersection over union (IoU) is measured as 86.3% for the AWE database. To the best of our knowledge, this is the highest performance achieved among the algorithms tested on the AWE test set.