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

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.
Optimization of Mode in Distribution Electrical Grid by Using Renewable Energy Sources for Rural Energy Supply
(IAEME Publication, 2018) Shokolakova, Shinar K.; Keshuov, Seitkazy A.; Saukhimov, Almaz A.; Tokhtibakiev, Karmel K.; Ceylan, Oğuzhan; Shuvalova, Jelena
Kazakhstan plans to support integration of renewable energy sources (RES). For instance according to [12] until 2020 it is planned to connect 53 RES with a total value of 2000 MW. From those 53 RES most of them will be located in rural areas and will be connected to electrical power distribution grid. Power losses is an important problem in Kazakhstan power systems and largest share of power losses are related to distribution system losses and are approximately 65 %. Using RES as distributed generators (DGs) in near future in order to reduce power losses may be one of the important tasks of Distribution System Operators (DSOs) of Kazakhstan. Approach of minimizing power losses may be applied by changing the injected/absorbed active and reactive powers at the points of DG connection [12]. This paper models the power losses optimization problem by using a recently developed heuristics based optimization model: Moth Flame Optimization (MFO). It solves the power loss minimization problem on a modified 33 bus electrical grid with DGS. © IAEME Publication
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; 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, 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.
Heuristic Optimization Approaches for Capacitor Sizing and Placement: a Case Study in Kazakhstan
(Mdpi, 2022) Baimakhanov, Olzhas; Senyuz, Hande; Saukhimov, Almaz; Ceylan, Oguzhan
Two methods for estimating the near-optimal positions and sizes of capacitors in radial distribution networks are presented. The first model assumes fixed-size capacitors, while the second model assumes controllable variable-size capacitors by changing the tap positions. In the second model, we limit the number of changes in capacitor size. In both approaches, the models consider many load scenarios and aim to obtain better voltage profiles by minimizing voltage deviations and active power losses. We use two recently developed heuristic algorithms called Salp Swarm Optimization algorithm (SSA) and Dragonfly algorithm (DA) to solve the proposed optimization models. We performed numerical simulations using data by modifying an actual distribution network in Almaty, Kazakhstan. To mimic various load scenarios, we start with the baseline load values and produce random variations. For the first model, the optimization algorithms identify the near-optimal positioning and sizes of fixed-size capacitors. Since the second model assumes variable-size capacitors, the algorithms also decide the tap positions for this case. Comparative analysis of the heuristic algorithms shows that the DA and SSA algorithms give similar results in solving the two optimization models: the former gives a slightly better voltage profile and lower active power losses.
A Multi-Objective Optimization Evaluation Framework for Integration of Distributed Energy Resources
(Elsevier, 2021) Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan
Renewable distributed generation and energy storage systems (ESSs) have been a gamechanger for a reliable and sustainable energy supply. However, this new type of generation should be optimally planned and operated to maximize the expected benefits. In this regard, this paper presents a new formulation for optimal allocation and sizing of distributed energy resources and operation of ESSs to improve the voltage profiles and minimize the annual costs. The multi-objective multiverse optimization method (MOMVO) is used as a solution tool. Moreover, the resulting Pareto optimal solution set is minimized under economic concerns and cost sensitivity to provide a decision-support for the utilities. The proposed formulation and solution algorithm are tested for the revised 33-bus and 69-bus test systems where the load and renewable generation characteristics are taken from real Turkish data. When compared with the base case operating conditions, the proposed formulation eliminated all the voltage magnitude violations, and provided almost 50% loss reductions and 20% energy transfers to off-peak hours. Moreover, Pareto fronts of the proposed method are found to better than the ones provided by non dominated sorting genetic algorithm and multi-objective particle swarm optimization, according to two multi-objective optimization metrics.
Semi-Centralized Control of Distributed Generation in Smart Grids
(IEEE, 2018) Ceylan, Oğuzhan; Pisica, Ioana; Paudyal, Sumit
This 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.
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; 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.
Double Branch Outage Modeling and Its Solution Using Differential Evolution Method
(2011) Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan
Power 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.
Graph Optimized Locality Preserving Projection Via Heuristic Optimization Algorithms
(IEEE, 2019) Ceylan, Oğuzhan; Taşkın, Gülşen
Dimensionality 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.