Yönetim Bilişim Sistemleri Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/68

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Citation - WoS: 2
Citation - Scopus: 3
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.
Citation - WoS: 6
Citation - Scopus: 10
An Adaptive Affinity Matrix Optimization for Locality Preserving Projection Via Heuristic Methods for Hyperspectral Image Analysis
(IEEE-Inst Electrıcal Electronıcs Engıneers Inc, 2019) Taşkın, Gülşen; Ceylan, Oğuzhan
Locality preserving projection (LPP) has been often used as a dimensionality reduction tool for hyperspectral image analysis especially in the context of classification since it provides a projection matrix for embedding test samples to low dimensional space. However, the performance of LPP heavily depends on the optimization of two parameters of the graph affinity matrix: k-nearest neighbor and heat kernel width, when one considers an isotropic kernel. These two parameters might be optimally chosen simply based on a grid search. In case of using a generalized heat kernel where each feature is separately weighted by a kernel width, the number of parameters that need to be optimized is related to the number of features of the dataset, which might not be very easy to tune. Therefore, in this article, we propose to use heuristic methods, including genetic algorithm (GA), harmony search (HS), and particle swarm optimization (PSO), to explore the effects of the heat kernel parameters aiming to analyze the embedding quality of LPP's projection in terms of various aspects, including 1-NN classification accuracy, locality preserving power, and quality of the graph affinity matrix. The results obtained with the experiments on three hyperspectral datasets show that HS performs better than GA and PSO in optimizing the parameters of the affinity matrix, and the generalized heat kernel achieves better performance than the isotropic kernel. Additionally, a feature selection application is performed by using the kernel width of the generalized heat kernel for each heuristic method. The results show that very promising results are obtained in comparison with the state-of-the-art feature selection methods.
Citation - WoS: 6
Citation - Scopus: 9
Assessment of Harmonic Distortion on Distribution Feeders With Electric Vehicles and Residential Pvs
(IEEE, 2017) Ceylan, Oğuzhan; Paudyal, Sumit; Dahal, Sudarshan; Karki, Nava R.
Power-electronic interfacing based devices such as photovoltaic (PV) panels and electric vehicles (EVs) cause voltage/current harmonic distortions on the power grid. The harmonic current profiles from EVs and PVs depend on the design of the controllers integrated to the PV inverters and EV chargers. Similarly the voltage and current harmonic distortions on a grid change throughout the day as the PV output power number of grid connected EVs and the other load pattern change. In this context we present harmonic assessment to demonstrate cumulative effect of large number of EVs and PVs on a medium voltage distribution grid. We will demonstrate the case studies on the IEEE 123-node distribution feeder with 20% 50% and 100% PV and EV penetrations based on time series simulations carried out for an entire day.
Citation - Scopus: 15
Branch outage solution using particle swarm optimization
(2008) Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan
For post outage MW line flows and voltage magnitude calculations most of the methods use linear methods because of their simplicity. Especially for reactive power flow calculations one can face high errors. In this paper we use a minimization method that minimizes the errors resulting from the linear system model implementation. We solve the optimization problem using particle swarm optimization. We give some outage examples using IEEE 14 bus IEEE 30 bus and IEEE 57 bus data and compare the results with full ac load flow calculation. © 2008 Australasian Universities Power Engineering Conference (AUPEC'08).
Citation - WoS: 8
Citation - Scopus: 14
Comparative Study of Active Power Curtailment Methods of Pvs for Preventing Overvoltage on Distribution Feeders
(IEEE, 2018) Paudyal, Sumit; Bhattarai, Bishnu P.; Tonkoski, Reinaldo; Dahal, Sudarshan; Ceylan, Oğuzhan
Overvoltage is one of the major issues on distribution grids with high penetration of photovoltaic (PV) generation. Overvoltage could be prevented through the control of active/reactive power of PVs. However given the high R/X ratio of low voltage feeders voltage control by using reactive power would not be as effective as using active power. Therefore active power curtailment (APC) of PVs though not desirable becomes necessary at times to prevent the overvoltage issues. Existing literature is rich in centralized and droop-based methods for APC and/or reactive power control of PVs to prevent overvoltage issues. In this context this paper revisits the most popular existing methods and evaluates the performance of droop-based and centralized methods using a typical North American 240 V low voltage feeder with 24 residential homes. In this work our key findings are: a) droop-based methods provided conservative solutions or did not eliminate the overvoltages completely b) power flow sensitivity based droop approach led to 13% more curtailment than the centralized approaches c) centralized approach had 40% less energy curtailed compared with standard droop while no overvoltages were observed and d) operating PVs at non-unity power factor in centralized approach led to 5% less energy curtailment.
Citation - Scopus: 3
A Comparative Study of Surrogate Based Learning Methods in Solving Power Flow Problem
(IEEE, 2020) 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.
Citation - WoS: 140
Citation - Scopus: 180
Coordinated Electric Vehicle Charging With Reactive Power Support To Distribution Grids
(IEEE, 2019) Wang, Jingyuan; Bharati, Guna R.; Paudyal, Sumit; Ceylan, Oğuzhan; Bhattarai, Bishnu P.; Myers, Kurt S.
We develop hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed electric vehicles (EVs) incorporating distribution grid level constraints. The frameworks consist of detailed mathematical models which can benefit the operation of both entities involved i.e. the grid operations and EV charging. The first model comprises of a comprehensive optimal power flow model at the distribution grid level while the second model represents detailed optimal EV charging with reactive power support to the grid. We demonstrate benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5000). Case studies demonstrate that in constrained distribution grids coordinated charging reduces the average cost of EV charging if the charging takes place at nonunity power factor mode compared to unity power factor. Similarly the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at nonunity power factor mode compared to unity power factor.
Citation - WoS: 4
Citation - Scopus: 5
Generic Dynamic Load Modelling Using Cluster Analysis
(IEEE, 2018) Barzegkar-Ntovom, Georgios A.; Ceylan, Oğuzhan; Papadopoulos, Theofilos A.; Yetkin, E. Fatih
In this paper a new generic load modelling procedure is proposed based on the application of cluster analysis on load model parameters identified from measured dynamic responses. The performance of the proposed approach is assessed using measurements obtained from a low-voltage laboratory scale test configuration. In order to develop robust generalized load models applicable to a wide range of operating conditions different load compositions operating conditions and voltage disturbances are considered in the analysis. The findings of this paper verify the validity of the proposed generic modelling procedure and indicate robust results using the proposed methodology.
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.
Citation - WoS: 22
Citation - Scopus: 34
Grey Wolf Optimizer for Allocation and Sizing of Distributed Renewable Generation
(Institute of Electrical and Electronics Engineers Inc., 2019) Ahmadi, Bahman; Ceylan, Oğuzhan; Özdemir, Aydoğan
Increasing penetration of distributed energy resources (DERs) have brought operational and control philosophy changes in Smart Grids (SGs). Renewable energy based technologies are becoming more important due to their economic and environmental impacts. Distributed generations (DGs) in the form of small renewable energy resources such as solar photovoltaics (PVs) and Wind Turbines (WTs) are connected in radial distribution networks near to the loads. This paper presents optimal siting and sizing of distributed renewable energy resource to maintain voltage magnitude profiles. Bus voltage magnitude differences for each hour in a day of a distribution system are formulated as an objective function. Three consecutive days are taken into account representing the three seasons of a year. A new nature inspired algorithm Grey Wolf Optimizer (GWO) is used as a solution tool. The proposed formulation is applied to 33 bus and 69 bus radial distribution networks. MATLAB simulations are performed to validate the performance of the approach. Simulation results are discussed and compared with of the several available ones'.
Citation - WoS: 4
Citation - Scopus: 6
Harmony Search Algorithm Based Management of Distributed Energy Resources and Storage Systems in Microgrids
(MDPI, 2020) Ceylan, Oğuzhan; Sezgin, Mustafa Erdem; Goel, Murat; Verga, Maurizio; Lazzari, Riccardo; Kwaye, Marcel Pendieu; Sandroni, Carlo
Microgrids are composed of distributed energy resources (DERs), storage devices, electric vehicles, flexible loads and so on. They may either operate connected to the main electricity grid (on-grid operation) or separated from the grid (islanded operation). The outputs of the renewable energy sources may fluctuate and thus can cause deviations in the voltage magnitudes especially at islanded mode. This may affect the stability of the microgrids. This paper proposes an optimization model to efficiently manage controllable devices in microgrids aiming to minimize the voltage deviations both in on-grid and islanded operation modes. RSE Distributed Energy Resources Test Facility (DER-TF), which is a low voltage microgrid system in Italy, is used to verify the algorithm. The test system's data is taken through an online software system (REDIS) and a harmony search based optimization algorithm is applied to control the device parameters. The experimental results show that the harmony search based optimization approach successfully finds the control parameters, and can help the system to obtain a better voltage profile.
Citation - Scopus: 5
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.
Citation - Scopus: 1
Multi-Agent Model of Electricity Networks - a Perspective on Distribution Network Charges
(Institute of Electrical and Electronics Engineers Inc., 2019) Pisica, Ioana; Ceylan, Oğuzhan
In the UK, DNOs are regulated by Ofgem to use two common distribution use of system charging methodologies: Common Distribution Charging Methodology and Extra-High Voltage Distribution Charging Methodology. To account for the changing landscape of the energy sector, Ofgem has recently published a consultation paper on changes to DUoS charging structure. This paper looks into the implications of distribution network charging in consumer-level adoption of low carbon technologies and vice-versa, using an agent-based model approach.
Citation - WoS: 13
Citation - Scopus: 16
Multi-Verse Optimization Algorithm- and Salp Swarm Optimization Algorithm-Based Optimization of Multilevel Inverters
(Springer, 2020) Ceylan, Oğuzhan
Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient usage of cascaded H-bridge multilevel inverters (MLIs) for power control applications becomes vital for sustainable electricity. Conventionally, selective harmonic elimination equations need to be solved for obtaining optimum switching angles of MLIs. The objective of this study is to obtain switching angles for MLIs to minimize total harmonic distortion. This study contributes to the solution of this problem by utilizing two recently developed intelligent optimization algorithms: multi-verse optimization algorithm and salp swarm algorithm. Moreover, well-known particle swarm optimization is utilized for MLI optimization problem. Seven-level, 11-level and 15-level MLIs are used to minimize total harmonic distortions. Simulation results with different modulation indexes for seven-, 11- and 15-level MLIs are calculated and compared in terms of the accuracy and solution quality. Numerical calculations are verified by using MATLAB/Simulink-based models.
Citation - WoS: 4
Citation - Scopus: 4
A Novel Approach for Voltage Control in Electrical Power Distribution Systems
(IEEE, 2018) Ceylan, Oğuzhan; Dimitrovski, Aleksandar; Starke, Michael; Tomsovic, Kevin
This paper proposes a novel approach for voltage control in 3-phase unbalanced distribution systems. The approach is based on the change of the magnetizing reactances of the voltage regulators in the system. We used an augmented Lagrangian-based optimization model to determine the optimal settings on a modified IEEE 123 Bus Test System with large PV penetration. Simulations were performed on a minute-based resolution either by controlling regulators or by controlling the magnetizing reactances of the regulators only. The simulation results presented and their comparison show that the control of the magnetizing reactances of the regulators may improve the voltage profile and may be an addition or alternative to both traditional voltage control devices such as capacitors tap changers and newer devices such as smart inverters.
Citation - Scopus: 13
Optimal Allocation Of Multi-Type Distributed Generators For Minimization Of Power Losses In Distribution Systems
(Institute of Electrical and Electronics Engineers Inc., 2019) Ahmadi, Bahman; Ceylan, Oğuzhan; Özdemir, Aydoğan
Distributed generation (DG), including photovoltaics (PVs), wind turbines (WTs) are becoming vital for Active Distribution Networks (ADN). Therefore, optimal sizing and allocation of these units can improve voltage profiles and reduce active power losses. This study concentrates on optimal allocation and sizing of two kinds of DG units (PVs and WTs) with 1 MW maximum size limits, due to the regulations in Turkey. The Whale optimization algorithm (WOA) and Grey wolf optimization algorithm (GWO) are used as optimization tools to minimize active power losses of 33 and 69 Bus Test Systems. The performance analysis of the methods are performed through simulations and the numerical results are compared in terms of optimal OF values and convergence characteristics.
Optimization of Graph Affinity Matrix With Heuristic Methods in Dimensionality Reduction of Hypespectral Images
(IEEE, 2019) Ceylan, Oğuzhan; Taşkın, Gülşen
Hyperspectral images include hundreds of spectral bands, adjacent ones of which are often highly correlated and noisy, leading to a decrease in classification performance as well as a high increase in computational time. Dimensionality reduction techniques, especially the nonlinear ones, are very effective tools to solve these issues. Locality preserving projection (LPP) is one of those graph based methods providing a better representation of the high dimensional data in the low-dimensional space compared to linear methods. However, its performance heavily depends on the parameters of the affinity matrix, that are k-nearest neighbor and heat kernel parameters. Using simple methods like grid-search, optimization of these parameters becomes very computationally demanding process especially when considering a generalized heat kernel, including an exclusive parameter per feature in the high dimensional space. The aim of this paper is to show the effectiveness of the heuristic methods, including harmony search (HS) and particle swarm optimization (PSO), in graph affinity optimization constructed with a generalized heat kernel. 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 heat kernel with a single parameter.
Citation - Scopus: 2
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
Citation - Scopus: 1
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.
Citation - Scopus: 3
Two-Layer Earth Structure Parameter Estimation and Seasonal Analysis
(IEEE, 2018) Papadopoulos, Theofilos A.; Ceylan, Oğuzhan; Papagiannis, Grigoris K.
The determination of soil parameters is an important topic regarding the safe and efficient design of electrical grounding systems as well as the accurate calculation of the per-unit-length parameters of overhead transmission lines and underground cable systems. Scope of this paper is to investigate the seasonal variation of the two-layer earth structure parameters and propose simple generic models for this type of the analysis. For the determination of the two-layer earth parameters the application of different optimization methods is evaluated. Furthermore soil resistivity measurements are conducted at three different sites during a period of a whole year. The obtained results of the two-layer earth structure are analyzed in order to investigate the seasonal variation and their correlation to changes of rainfall. From the analysis of the results significant conclusions are drawn regarding the accuracy of the resulting soil parameters as well as their annual variation.

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