Ceylan, Oğuzhan
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Name Variants
Oğuzhan Ceylan
CEYLAN, OĞUZHAN
Ceylan, OĞUZHAN
Ceylan, Oguzhan
CEYLAN, Oğuzhan
Ceylan,Oguzhan
Oğuzhan CEYLAN
OĞUZHAN CEYLAN
C., Oguzhan
Ceylan,O.
C.,Oguzhan
Oguzhan, Ceylan
C., Oğuzhan
Ceylan, Oğuzhan
Ceylan O.
Ceylan, O.
O. Ceylan
Ceylan, Oğuzhan
Ceylan, O?uzhan
CEYLAN, OĞUZHAN
Ceylan, OĞUZHAN
Ceylan, Oguzhan
CEYLAN, Oğuzhan
Ceylan,Oguzhan
Oğuzhan CEYLAN
OĞUZHAN CEYLAN
C., Oguzhan
Ceylan,O.
C.,Oguzhan
Oguzhan, Ceylan
C., Oğuzhan
Ceylan, Oğuzhan
Ceylan O.
Ceylan, O.
O. Ceylan
Ceylan, Oğuzhan
Ceylan, O?uzhan
Job Title
Doç. Dr.
Email Address
oguzhan.ceylan@khas.edu.tr
Main Affiliation
Management Information Systems
Status
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals Report Points
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Scholarly Output
72
Articles
20
Citation Count
362
Supervised Theses
3
47 results
Scholarly Output Search Results
Now showing 1 - 10 of 47
Conference Object Citation - WoS: 1Citation - Scopus: 3Active and Reactive Power Load Profiling Using Dimensionality Reduction Techniques and Clustering(Institute of Electrical and Electronics Engineers Inc., 2019) Yetkin, E. Fatih; Ceylan, Oğuzhan; Ceylan, Oğuzhan; Yetkin, Emrullah Fatih; Papadopoulos, Theofilos A.; Kazaki, Anastasia G.; Barzegkar-Ntovom, Georgios A.; Business Administration; Management Information SystemsThis paper proposes a methodology to characterize active and reactive power load profiles. Specifically, the approach makes use of fast Fourier Transform for conversion into frequency domain, principle component analysis to reduce the dimension and K-means++ to determine the representative load profiles. The data set consists of five-year measurements taken from the Democritus University of Thrace Campus. Test days were also classified as working and non-working. From the results it is observed that the proposed methodology determines representative load profiles effectively both regarding active and reactive power.Conference Object Citation - WoS: 0Citation - Scopus: 1Semi-Centralized Control of Distributed Generation in Smart Grids(IEEE, 2018) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Pisica, Ioana; Paudyal, Sumit; Management Information SystemsThis paper proposes a semi-centralized intelligent control approach for voltage regulation in distribution grids based on sensitivity calculations. The model checks the voltage magnitudes of each end of each lateral in the system one by one then if any of these violates the allowed voltage magnitudes each node in a single lateral sends its reactive power capability and sensitivity information to the sensor located at the beginning node of that lateral. This information is sorted at the sensor and required voltage is computed and assigned to the bids one by one. This paper tests this approach on a modified 33 Node Distribution Test system with several renewable energy sources: photovoltaics (PVs) and wind turbines (WTs) and presents the numerical results based on a 15 minute resolution load data PV outputs and WT outputs.Conference Object Citation - Scopus: 5Double Branch Outage Modeling and Its Solution Using Differential Evolution Method(2011) Ceylan, Oğuzhan; Dağ, Hasan; Ozdemir, Aydogan; Ceylan, Oğuzhan; Dağ, Hasan; Management Information SystemsPower system operators need to check the system security by contingency analysis which requires power flow solutions repeatedly. AC power flow is computationally slow even for a moderately sized system. Thus fast and accurate outage models and approximated solutions have been developed. This paper adopts a single branch outage model to a double branch outage one. The final constrained optimization problem resulted from modeling is then solved by using differential evolution method. Simulation results for IEEE 30 and 118 bus test systems are presented and compared to those of full AC load flow in terms of solution accuracy. © 2011 IEEE.Conference Object Citation - WoS: 0Graph Optimized Locality Preserving Projection Via Heuristic Optimization Algorithms(IEEE, 2019) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Taşkın, Gülşen; Management Information SystemsDimensionality reduction has been an active research topic in hyperspectral image analysis due to complexity and non-linearity of the hundreds of the spectral bands. Locality preserving projection (LPP) is a linear extension of the manifold learning and has been very effective in dimensionality reduction compared to linear methods. However, its performance heavily depends on construction of the graph affinity matrix, which has two parameters need to be optimized: k-nearest neighbor parameter and heat kernel parameter. These two parameters might be optimally chosen simply based on a grid search when using only one representative kernel parameter for all the features, but this solution is not feasible when considering a generalized heat kernel in construction the affinity matrix. In this paper, we propose to use heuristic methods, including harmony search (HS) and particle swarm optimization (PSO), in exploring the effects of the heat kernel parameters on embedding quality in terms of classification accuracy. The preliminary results obtained with the experiments on the hyperspectral images showed that HS performs better than PSO, and the heat kernel with multiple parameters achieves better performance than the isotropic kernel with single parameter.Conference Object Citation - WoS: 0Citation - Scopus: 1Power Output Prediction of Wave Farms Using Fully Connected Networks(IEEE, 2021) Burramukku, Bhavana; Ceylan, Oğuzhan; Ceylan, Oguzhan; Neshat, Mehdi; Management Information SystemsOne of the most important factors in the amount of power generated by a wave farm is the Wave Energy Converters (WECs) arrangement along with the usual wave conditions. Therefore, forming an appropriate arrangement of WECs in an array is a significant parameter in maximizing power absorption. This paper focuses on developing a fully connected neural model in order to predict the total power output of a wave farm based on the placement of the converters, derived from the four real wave scenarios on the southern coast of Australia. The applied converter model is a fully submerged three-tether converter called CETO. Data collected from the test sites is used to design a neural model for predicting the wave farm's power output produced. A precise analysis of the WEC placement is investigated to reveal the amount of power generated by the wave farms on the test site. We finally proposed a suitable configuration of a fully connected neural model to forecast the power output with high accuracy.Conference Object Citation - WoS: 3Citation - Scopus: 11Allocation of Distributed Generators Using Parallel Grey Wolf Optimization(IEEE, 2021) Younesi, Soheil; Ceylan, Oğuzhan; Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan; Management Information SystemsThis paper solves the allocation problem of distributed generators (DGs) in smart grids utilizing a grey wolf optimization (GWO) algorithm. By parallelizing GWO, it presents the impact of using various number of processors on speedup, efficiency. To decrease the computation time required to perform the simulations, different migration rates are applied for different number of processors. Moreover, the accuracy obtained using different number of processors is analyzed. The simulations are performed for a 33-bus distribution test system using MATLAB's parallel computing toolbox. From the simulation results it is observed that parallel GWO can be used as a tool for distribution system optimization.Conference Object Citation - Scopus: 1Optimal Allocation of Distributed Generators and Mobile Battery Energy Storage Systems in Distribution System(Institute of Electrical and Electronics Engineers Inc., 2023) Ahmadi,B.; Ceylan, Oğuzhan; Ceylan,O.; Ozdemir,A.; Management Information SystemsThis research proposes an expansion planning frame-work that determines the optimal number, location, size, and type of distributed generators (DGs) and the number, capacity, location, and operation of mobile battery energy storage systems (MBESSs) in the distribution networks to improve the voltage profiles. The framework is applied to IEEE 33-bus and 69-bus standard test systems. The framework uses the advanced grey-wolf optimization (AGWO) developed to deal with mixed-integer nonlinear programming problems and the forward-backward sweep power flow method to determine the optimal control parameters defined for the problem. This study shows that the proposed framework enables the allocation and operation of DG and MBESS units to eliminate all voltage violations. Moreover, a clear roadmap is proposed for the size and location of DG units in the system and guidelines for the monthly location of MBESS. © 2023 IEEE.Conference Object Citation - WoS: 0Citation - Scopus: 4Impacts of Load and Generation Volatilities on the Voltage Profiles Improved by Distributed Energy Resources(Institute of Electrical and Electronics Engineers Inc., 2020) Ahmedi, Bahman; Ceylan, Oğuzhan; Ceylan, Oğuzhan; Özdemir, Serpil; Özdemir, Aydoğan; Advertising; Management Information SystemsWeather-dependent distributed renewable energy sources such as photovoltaics (PVs) and wind turbines (WT) are increasingly being connected to distribution networks (DNs). Increased penetration of these intermittent sources brought the necessity of using energy storage systems (ESSs) to achieve the intended benefits. This study presents an optimization process to determine optimal numbers, sizes, locations and distributed energy resources (DERs) as well as to determine the optimal operating strategy of ESSs in a distribution network. The objective is to improve the voltage profile and to minimize the installation costs. The proposed multi-objective formulation problem is solved by using ant lion multi-objective optimization algorithm. At the second part of the study, optimal values are tested with monthly extreme distributions and the impacts of load and distributed generation volatilies on the voltage profiles which were determined by Pareto-optimal solution candidates are analysed. Simulations were performed on 33 bus radial distribution system using Matlab. Finally the benefits obtained by the optimal solutions with less risk are compared.Conference Object Citation - WoS: 1Citation - Scopus: 2Analysis of Local and Centralized Control of Pv Inverters for Voltage Support in Distribution Feeders(IEEE, 2021) Ceylan, Oguzhan; Ceylan, Oğuzhan; Paudyal, Sumit; Pisica, Ioana; Management Information SystemsHigher photovoltaic penetration on distribution system brings operational challenges including overvoltage issues. With smart inverters, efficient voltage control can be achieved through adjusting active/reactive powers of inverters. However, reactive power may not be as effective as active power in regulating voltage due to high R/X ratio of distribution networks. Thus, active power curtailment (APC) techniques in coordination with reactive power control are required in distribution networks. In this study, we aim to evaluate the performances of a sensitivity based method and an optimal power flow (OPF) based centralized method of reactive power control (in coordination with APC) from inverters in managing voltage profile on distribution networks. We performed simulations on a 730-node MV/LV system upto 100% PV penetration. Based on the case studies using different penetration levels of PVs, we observed that: a) sensitivity based method is not always able to solve overvoltage issues and energy curtailments are high, and b) OPF-based method can ensure that voltage remains within the operational bound with significantly less energy curtailment.Conference Object Citation - WoS: 0Citation - Scopus: 2A Comparative Study of Surrogate Based Learning Methods in Solving Power Flow Problem(IEEE, 2020) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Taşkın, Gülsen; Paudyal, Sumit; Management Information SystemsDue to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.