Browsing by Author "Ceylan, Oğuzhan"

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Citation Count: 1
Active and Reactive Power Load Profiling Using Dimensionality Reduction Techniques and Clustering
(Institute of Electrical and Electronics Engineers Inc., 2019) Ceylan, Oğuzhan; 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 Count: 4
An Adaptive Affinity Matrix Optimization for Locality Preserving Projection via Heuristic Methods for Hyperspectral Image Analysis
(IEEE-Inst Electrıcal Electronıcs Engıneers Inc, 2019) Ceylan, Oğuzhan; 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 Count: 11
An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids
(Springer, 2022) Ceylan, Oğuzhan; Younesi, Soheil; Ceylan, Oguzhan; Ozdemir, Aydogan
Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.
Citation Count: 0
Akıllı Şebekeler Ve Akıllı Toplum İçin Gelişmiş Evrimsel Hesaplamalar
(2020) Özdemir, Aydoğan; Ahmadı, Bahman; Ceylan, Oğuzhan; Younesı, Soheıl
Elektrik ve enformasyon altyapılarının birleşimi ile oluşan akıllı şebekelerin (AŞ), geleceğin elektrik üretim, iletim ve dağıtım sistemlerinin, daha az karbon salınımı ile daha ekonomik ve güvenilir bir hizmet sağlamasında kilit rol oynaması beklenmektedir. Diğer yandan, dağılmış enerji kaynaklarının (DEK) yaygın bir şekilde elektrik dağıtım sistemlerine bağlanması ve serbestleştirme olguları, yeni işletim ve kontrol felsefeleri geliştirilmesini zorunlu kılmıştır. Bu projenin amacı, AŞ?nin belirtilen hesaplama ihtiyacını karşılamak üzere, gelişmiş evrimsel algoritma tabanlı çözümler sunmaktır. Bu kapsamda, iki temel göreve odaklanılması düşünülmüştür. ? Dağıtık enerji ve depolama kaynakları ve talep gelişimi dikkate alınarak dağıtım şebekesi geliştirme ve genişleme planları, ? Dağıtık enerji kaynakları ve depolama birimleri dikkate alınarak bara gerilim profillerinin iyileştirilmesi, yatırım ve işletme giderlerinin minimizasyonu, dağıtım şebeke kayıplarının azaltılması, güvenilirliğin arttırılması, gerilim ve reaktif güç kontrolü, uzun süreli arıza sonrası yeniden yapılandırma ve şebeke restorasyonu. Yukarıda belirtilen problemler, uygun amaç fonksiyonları ile modellenerek birer eniyileme (optimizasyon) problemi olarak ifade edilerek, çeşitli gelişmiş evrimsel algoritmalarla çözülmüşlerdir. Burada önerilen gelişmiş evrimsel hesaplamaların, alışılagelmiş çözüm tekniklerine göre şu temel farklılıkları içermektedir. ? Bu projede henüz daha çok yeni geliştirilen evrimsel algoritmalar ve bunların gelişmiş halleri olup, yerel arama destekleri ile birlikte, hızla ve yeteri doğrulukta çözüm veren tekniklerdir. ? Söz konusu algoritmalar, tüm yarı-optimal çözüm seçeneklerini veren güvenilir çözümler sağlamaktadır. ? Paralel/dağıtık hesaplama teknikleri ve/veya karma (memtic) algoritmalarla birlikte uygulanarak, gelişmiş evrimsel algoritma tabanlı çözümleri gerçekleşmiştir. Geliştirilen algoritmaların performansı, 33 bara, 69 bara ve 141 baralı test sisteminde denenmiş; fakat pandemi dönemi kısıtları nedeniyle BEDAŞ Elektrik şebekesinde uygulaması henüz tamamlanamamıştır.
Citation Count: 1
Allocation of Distributed Generators Using Parallel Grey Wolf Optimization
(IEEE, 2021) Ceylan, Oğuzhan; Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan
This 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.
Citation Count: 1
Analysis of Local and Centralized Control of PV Inverters for Voltage Support in Distribution Feeders
(IEEE, 2021) Ceylan, Oğuzhan; Paudyal, Sumit; Pisica, Ioana
Higher 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.
Citation Count: 8
The Arithmetic Optimization Algorithm for Optimal Energy Resource Planning
(IEEE, 2021) Ceylan, Oğuzhan; Younesi, Soheil; Ceylan, Oguzhan; Ozdemir, Aydogan
This study presents a new formulation regarding optimal placement and sizing of multi-type distributed generations (DGs) and energy storage systems (ESSs) to enhance the reliability of a radial distribution system and to reduce the line losses employing Arithmetic Optimization Algorithm (AOA) method. The model determines the number of DGs and ESSs automatically, and is designed to minimize the losses and the reliability indices such as Customer Average Interruption Duration Index (CAIDI). The performance of the algorithm is tested on 69-bus radial distribution system. The objective functions corresponding to optimal type, location, and size of distributed energy resources are compared to the base-case values. Finally, a comparative performance analysis of the proposed algorithm is performed in terms of reliability indices and power losses with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO).
Citation Count: 0
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.
Citation Count: 3
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 Count: 2
Branch outage simulation based contingency screening by gravitational search algorithm
(Praise Worthy Prize Srl, 2012) Ceylan, Oğuzhan; Dağ, Hasan; Dağ, Hasan
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.
Citation Count: 15
Branch outage solution using particle swarm optimization
(2008) Ceylan, Oğuzhan; Dağ, Hasan; 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 Count: 5
Cascaded H-bridge multilevel inverters optimization using adaptive grey wolf optimizer with local search
(Springer, 2021) Ceylan, Oğuzhan; Neshat, Mehdi; Mirjalili, Seyedali
With the transformation of transmission and distribution grids into smart grids that are more dominated by renewable energy, power electronics-based inverters that can improve power quality are becoming more visible. In order to maximize the output voltage quality and reduce the total harmonic distortion (THD), efficient operation of inverters is required. Therefore, in this paper, the problem of harmonic elimination in multilevel inverters is solved by using an adaptive grey wolf optimizer with local search. We have performed a grid search-based landscape analysis of the seven-level inverter to understand the behaviour of the proposed algorithm. For verification, the numerical results of the proposed adaptive grey wolf optimizer are compared with those of the original grey wolf optimization algorithm, a modified version of the grey wolf optimization algorithm, the particle swarm optimization algorithm, multi-verse optimization algorithm, and salp swarm algorithm. In the simulations, we solved the optimization model for three different structures of multilevel inverters (7, 11, and 15 levels) by changing the modulation indexes. It is found that the adaptive grey wolf optimization provides lower total harmonic distortion for different modulation indexes.
Citation Count: 6
Comparative Study of Active Power Curtailment Methods of PVs for Preventing Overvoltage on Distribution Feeders
(IEEE, 2018) Ceylan, Oğuzhan; 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 Count: 16
A Comparative Study of Metaheuristic Algorithms for Wave Energy Converter Power Take-Off Optimisation: A Case Study for Eastern Australia
(MDPI, 2021) 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.
Citation Count: 1
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 Count: 12
Comparison of post outage bus voltage magnitudes estimated by harmony search and differential evolution methods
(2009) Ceylan, Oğuzhan; Dağ, Hasan; Dağ, Hasan
Contingency studies are indispensable tools of both the power system planning and operational studies. Real time implementation of operational problems makes necessary the use of high speed computational methods while requiring reasonable accuracies. On the other hand, accuracy of the results and the speed of calculation depend on branch outage modeling as well as solution algorithm used. This paper presents a comparison of post outage bus voltage magnitudes calculated by two meta-heuristic approaches; namely differential evolution (DE) and harmony search (HS) methods. The methods are tested on IEEE 14, IEEE 30, IEEE 57, and IEEE 118 bus test systems and the results are compared both in terms of accuracy and calculation speed.
Citation Count: 97
Coordinated Electric Vehicle Charging With Reactive Power Support to Distribution Grids
(IEEE, 2019) Ceylan, Oğuzhan; 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 Count: 4
Cuckoo search algorithm for optimal siting and sizing of multiple distributed generators in distribution grids
(IEEE, 2019) Ceylan, Oğuzhan; Ceylan, Oguzhan; Ozdemir, Aydogan
Distribution networks (DNs) are facing numerous challenges such as variability of demands, environmental issues, high power losses, and fluctuating v oltage p rofiles. Distributed energy resources (DERs) are becoming more important due to their economic and environmental impacts. This paper presents optimal siting and sizing of the Photovoltaics (PVs) and Wind Turbines (WTs) to improve the voltage magnitude profiles. This planning problem is formulated using DER generation and load profiles of the three representative days, one for each season. The resulting constrained optimization problem is solved using Cuckoo Search Algorithm (CSA). The proposed solution approach is applied to the 33 bus and 69 bus radial distribution networks. Several simulations are performed for the performance analysis of the methods and the results are compared to several available ones.
Citation Count: 23
Distributed energy resource allocation using multi-objective grasshopper optimization algorithm
(Elsevier Science Sa, 2021) Ceylan, Oğuzhan; Ceylan, Oguzhan; Ozdemir, Aydogan
The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.
Citation Count: 5
Double branch outage modeling and its solution using differential evolution method
(2011) Dağ, Hasan; Ceylan, Oğuzhan; 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.