Oğuzhan Ceylan

Ceylan, Oğuzhan

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

Job Title

Doç. Dr.

Email Address

oguzhan.ceylan@khas.edu.tr

ORCID ID

0000-0002-0892-6380

Scopus Author ID

26665865200

Full item page

Scholarly Output

67

Articles

19

Citation Count

362

Supervised Theses

3 Scholarly Output Search Results

Now showing 1 - 10 of 66

Active 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.
This 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.
A Comparative Study of Metaheuristic Algorithms for Wave Energy Converter Power Take-Off Optimisation: A Case Study for Eastern Australia
(MDPI, 2021) Amini, Erfan; Ceylan, Oğuzhan; Golbaz, Danial; Asadi, Rojin; Nasiri, Mandieh; Ceylan, Oğuzhan; Nezhad, Meysam Majidi; Neshat, Mehdi
One of the most encouraging sorts of renewable energy is ocean wave energy. In spite of a large number of investigations in this field during the last decade, wave energy technologies are recognised as neither mature nor broadly commercialised compared to other renewable energy technologies. In this paper, we develop and optimise Power Take-off (PTO) configurations of a well-known wave energy converter (WEC) called a point absorber. This WEC is a fully submerged buoy with three tethers, which was proposed and developed by Carnegie Clean Energy Company in Australia. Optimising the WEC's PTO parameters is a challenging engineering problem due to the high dimensionality and complexity of the search space. This research compares the performance of five state-of-the-art metaheuristics (including Covariance Matrix Adaptation Evolution Strategy, Gray Wolf optimiser, Harris Hawks optimisation, and Grasshopper Optimisation Algorithm) based on the real wave scenario in Sydney sea state. The experimental achievements show that the Multiverse optimisation (MVO) algorithm performs better than the other metaheuristics applied in this work.
A Comparative Study of Surrogate Based Learning Methods in Solving Power Flow Problem
(IEEE, 2020) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Taşkın, Gülsen; Paudyal, Sumit
Due 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.
Branch Outage Simulation Based Contingency Screening by Gravitational Search Algorithm
(Praise Worthy Prize Srl, 2012) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Özdemir, Aydoğan; Dağ, Hasan; Dağ, Hasan; Özdemir, Serpil
Power systems contingency analysis is an important issue for electric power system operators. This paper performs branch outage simulation based contingency screening using a bounded network approach. Local constrained optimization problem representing the branch outage phenomena is solved by the gravitational search algorithm. The proposed method is applied to IEEE 14 30 57 and 118 Bus Test systems and its performance from the point of capturing violations is evaluated. In addition false alarms and the computational accuracy of the proposed method are also analyzed by using scattering diagrams. Finally the proposed gravitational search based contingency screening is compared with full AC load flow solutions from the point of computational speed. Copyright (C) 2012 Praise Worthy Prize S.r.l. - All rights reserved.
Feature Selection Using Self Organizing Map Oriented Evolutionary Approach
(Institute of Electrical and Electronics Engineers Inc., 2021) Ceylan, O.; Ceylan, Oğuzhan; Taskin, G.
Hyperspectral images are the multidimensional matrices consisting of hundreds of spectral feature vectors. Thanks to these large number of features, the objects on the Earth having similar spectral characteristics can easily be distinguished from each other. However, the high correlation and the noise between these features cause a significant decrease in the classification performances, especially in the supervised classification tasks. In order to overcome these problems, which is known in the literature as Hughes's effects or curse of dimensionality, dimensionality reduction techniques have frequently been used. Feature selection and feature extraction methods are the ones used for this purpose. The feature selection methods aim to remove the features, including high correlation and noise, out of the original feature set. In other words, a subset of relevant features that have the ability to distinguish the objects is determined. The feature extraction methods project the high dimensional space into a lower-dimensional feature space based on some optimization criterion, and hence they distort the original characteristic of the dataset. Therefore, the feature selection methods are more preferred than the feature extraction methods since they preserve the originality of the dataset. Based on this motivation, an evolutionary based optimization algorithm utilizing self organizing map was accordingly modified to provide a new feature selection method for the classification of hyperspectral images. The proposed method was compared to well-known feature selection methods in the classification of two hyperspectral datasets: Botswana and Indian Pines. According to the preliminary results, the proposed method achieves higher performance over other feature selection methods with a very less number of features. © 2021 IEEE.
A Heuristic Methods-Based Power Distribution System Optimization Toolbox
(Mdpi, 2022) Ceylan, Oğuzhan; Baimakhanov, Olzhas; Saukhimov, Almaz; Ceylan, Oguzhan
This paper proposes a toolbox for simulating the effective integration of renewable energy sources into distribution systems. The toolbox uses four heuristic methods: the particle swarm optimization (PSO) method, and three recently developed methods, namely Gray Wolf Optimization (GWO), Ant Lion Optimization (ALO), and Whale Optimization Algorithm (WOA), for the efficient operation of power distribution systems. The toolbox consists of two main functionalities. The first one allows the user to select the test system to be solved (33-, 69-, or 141-bus test systems), the locations of the distributed generators (DGs), and the voltage regulators. In addition, the user selects the daily active power output profiles of the DGs, and the tool solves the voltage deviation problem for the specified time of day. The second functionality involves the simulation of energy storage systems and provides the optimal daily power output of the resources. With this program, a graphical user interface (GUI) allows users to select the test system, the optimization method to be used, the number of DGs and locations, the locations and number of battery energy storage systems (BESSs), and the tap changer locations. With the simple user interface, the user can manage the distribution system simulation and see the results by making appropriate changes to the test systems.
Post-outage state estimations for outage management
(IFAC Secretariat, 2011) Ceylan, Oğuzhan; Dağ, Hasan; Ozdemir, Aydogan; Ceylan, Oğuzhan; Dağ, Hasan
Real time outage information is required to the utility operators for outage management process. In addition to some basic information regarding the outage post-outage system status will help to improve the response to outages and management of system reliability. This paper presents particle swarm optimization based reactive power estimations for branch outages. Post outage voltage magnitudes and reactive power flows results for IEEE 14 and IEEE 30 bus systems are given. Simulation results show that post outage voltage magnitudes and reactive power flows can be computed with a reasonable accuracy. © 2011 IFAC.
Armoni Araması Yöntemi ile Elektrik Dağıtım Sistemlerinin Yeniden Yapılandırılması: Elektrikli Araçların Etkisi
(2019) Ceylan, Oğuzhan
Bilindiği üzere son yirmi yılda elektrik güç sistemleri yoğun değişimler yaşamıştır. Elektrik piyasalarının yapısı değişmiş, tüm dünyada elektrik dağıtım sistemlerinde yenilenebilir enerji kaynaklarının ve elektrikli araçların (𝐸𝐴) kullanımı gün geçtikçe artmıştır. Pek çok ekonomik ve çevresel getirisi bulunan 𝐸𝐴’ların menzillerinin sınırlı olması, neredeyse her gün şarj edilmelerini gerektirmekte ve bu da elektrik güç sistemine ek yük getirmektedir. Bu çalışmada elektrik dağıtım sistemlerinde çok sayıda 𝐸𝐴 olması durumunda karşılaşılan gerilim problemleri ve kayıpları minimize etmek için yeniden yapılandırma yaklaşımı incelenmektedir. Eniyileme probleminin çözümü için armoni araması yöntemi (𝐴𝐴𝑌) kullanılmaktadır. Ortaya konan yaklaşımla, sistemde farklı sayıda 𝐸𝐴 ve dağıtık generatör olması durumları dikkate alınarak IEEE 33 bara test sisteminde çözülmekte ve ardından sonuçlara yer verilmektedir.
Impacts 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
Weather-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.
Power Output Prediction of Wave Farms Using Fully Connected Networks
(IEEE, 2021) Burramukku, Bhavana; Ceylan, Oğuzhan; Ceylan, Oguzhan; Neshat, Mehdi
One 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.