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Application of gravitational search algorithm for optimal reactive power dispatch problem
(2011) Duman, Serhat; Sönmez, Yusuf; Güvenç, Uğur; Yörükeren, Nuran
In this paper, Gravitational Search Algorithm (GSA) is applied to solve the optimal reactive power dispatch (ORPD) problem. The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss and the bus voltage deviations are to be minimized separately. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system consisting 6 generator and compared other algorithms reported those before in literature. Results show that GSA is more efficient than others for solution of single-objective ORPD problem. © 2011 IEEE.
Application of Symbiotic Organisms Search Algorithm to Solve Various Economic Load Dispatch Problems
(Ieee, 2016) Güvenç, Uğur; Duman, Serhat; Döşoğlu, M. Kenan; Kahraman, H. Tolga; Sönmez, Yusuf; Yılmaz, Cemal
This paper proposes the application of Symbiotic Organisms Search (SOS) Algorithm to solve the various Economic Load Dispatch (ELD) problems. Both classical ELD problem which has smooth fuel cost function and nonconvex ELD problem which has nonconvex and discontinuous fuel cost function due to considering of some practical constraints like valve point effects, ramp rate limits and prohibited generating zones have been solved in the study. Three different test cases have been used to show the efficiency and reliability of the proposed algorithm. 38-unit test system has been used for classical ELD and 3-unit and 15-unit test systems have been used for nonconvex ELD problem. Results have been compared to various heuristic methods reported before in the literature and they show that proposed algorithm converges to the global optimum in early iterations and can produce superior results than others in the solution of ELD problems which have both smooth and nonconvex and discontinuous fuel cost function.
Automatic Generation Control of the Two Area Non-reheat Thermal Power System using Gravitational Search Algorithm
(Wydawnictwo Sigma-N O T Sp Z O O, 2012) Duman, Serhat; Yörükeren, Nuran
In this study, determination of the optimal proportional-integral-derivate (PID) parameters with Gravitational search algorithm (GSA) for automatic generation control (AGC) of the two area non-reheat thermal power system is proposed. GSA is applied to search for the optimal PID controller parameters to minimize various performance indexes. The designed PID controller with the proposed approach is simulated under variety of operating conditions. Simulation results are shown that dynamic performance of the two area non-reheat thermal power system is improved by the designed PID controller with the proposed approach.
Chaotic Moth Swarm Algorithm
(Ieee, 2017) Güvenç, Uğur; Duman, Serhat; Hınıslıoğlu, Yunus
Moth Swarm Algorithm (MSA) is one of the newest developed nature-inspired heuristics for optimization problem. Nevertheless MSA has a drawback which is slow convergence. Chaos is incorporated into MSA to eliminate this drawback. In this paper, ten chaotic maps have been embedded into MSA to find the best numbers of prospectors for increase the exploitation of the best promising solutions. The proposed method is applied to solve the well-known seven benchmark test functions. Simulation results show that chaotic maps can improve the performance of the original MSA in terms of the convergence speed. At the same time, sinusoidal map is the best map for improving the performance of MSA significantly.
Combined economic and emission dispatch solution using gravitational search algorithm
(Elsevier Science Bv, 2012) Güvenç, Uğur; Sönmez, Yusuf; Duman, Serhat; Yörükeren, Nuran
In this article, the Gravitational Search Algorithm (GSA) has been proposed to find the optimal solution for Combined Economic and Emission Dispatch (CEED) problems. It is aimed, in the CEED problem, that scheduling of generators should operate with both minimum fuel costs and emission levels, simultaneously, while satisfying the load demand and operational constraints. In this paper, the CEED problem is formulated as a multi-objective problem by considering the fuel cost and emission objectives of generating units. The bi-objective optimization problem is converted into a single objective function using a price penalty factor in order to solve it with GSA. The proposed algorithm has been implemented on four different test cases, having a valve point effect with transmission loss and having no valve point effect without transmission loss. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature. Results show that the GSA is more powerful than other algorithms. (C) 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
Combined heat and power economic emission dispatch using dynamic switched crowding based multi-objective symbiotic organism search algorithm
(Elsevier, 2024) Ozkaya, Burcin; Kahraman, Hamdi Tolga; Duman, Serhat; Guvenc, Ugur; Akbel, Mustafa
Combined heat and power economic emission dispatch (CHPEED) problem is a highly complex, non-linear, non -convex multiobjective optimization problem due to two conflicting objectives and various operational constraints such as valve-point loading effect, power transmission loss, prohibited operating zone, and the feasible operating region of combined heat and power unit. In order to overcome these challenges, it is necessary to design an algorithm that exhibits a search behavior, which is suitable for the characteristics of objective and constraint space of the CHPEED problem. For these reasons, a dynamic switched crowding based multi-objective symbiotic organism search (DSC-MOSOS) algorithm was designed to meet the requirements and geometric space of the CHPEED problem. By applying the DSC method in the MOSOS algorithm, it was aimed to improve the exploration ability, to strengthen exploitation-exploration balance, and to prevent the catching into local solution traps. A comprehensive experimental study was carried out to prove the performance of the proposed al-gorithm on IEEE CEC 2020 multi-modal multi-objective problems (MMOPs) and CHPEED problem. In the experimental study conducted among eleven versions of MOSOS variations created with DSC-method and the base MOSOS algorithm on IEEE CEC 2020 MMOPs, according to Friedman scores based on the four performance metrics, the base MOSOS algorithm ranked the last. In other experimental study, the best DSC-MOSOS variant was applied to solve the CHPEED problem, where 5-, 7-, 10-and 14-unit test systems and eight case studies were considered. The important points of this study were that 10-unit and 14-unit test systems were presented to the literature, and the prohibited operating zone was considered in CHPEED problem for the first time. According to the results obtained from eight case studies obtained from the DSC-MOSOS and fourteen competitor algorithms, while the improvement in cost was between 0.2% and 16.55%, the reduction of the emission value was between 0.2 kg and 42.97 kg compared to the competitor algorithms. On the other hand, the stability of the DSC-MOSOS and the base MOSOS was evaluated using stability analysis. While the MOSOS algorithms was not able to perform a success in any case study, the DSC-MOSOS was achieved an average success rate with 91.16%. Thus, the performance of the DSC-MOSOS over the MOSOS was verified by the results of experimental studies and analysis.
DETERMINATION OF THE CONDITIONS OF OPTIMAL OPERATION IN POWER SYSTEMS USING GENETIC ALGORITHM
(Gazi Univ, Fac Engineering Architecture, 2009) Öztürk, Ali; Duman, Serhat
In this study, optimum operating conditions in electric power systems are determined by using Genetic Algorithm (GA), which is one of the optimization methods. Main objective of the work is determination of the voltage amplitude values of the load buses that ensure active power losses in transmission line will be minimum. A five-buses system is considered as an example and the operating voltage values of the load buses are calculated for this system using both GA and Newton-Raphson (NR) power flow method. Using these values, Static VAR Compensation (SVC) and reactive power compensation are implemented. Results show that it will be economically and energy-providently if the operating conditions are determined using GA, since the active power loss is minimized.
Determination of the PID controller parameters for speed and position control of DC motor using gravitational search algorithm
(2011) Duman, Serhat; Maden, Dinçer; Güvenç, Uğur
In this paper we use a new search heuristic called Gravitational Search Algorithm (GSA) to determination of the optimal PID controller parameters in the speed and position control of a DC motor. The model of a DC motor is considered as second and third order system. Mean squared error (MSE) performance index has been used as objective function. End of the optimization process, the rise and the settling times and the overshoot are compared to those reported in the literature. To show that effectiveness of proposed method are compared with Ziegler-Nichols method in speed control of DC motor. Simulation results show the effectiveness and robustness of proposed controllers to provide the speed and position control of DC motor. © 2011 Chamber of Turkish Electric.
Development of a Levy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems
(Springer, 2021) Duman, Serhat; Kahraman, Hamdi T.; Guvenc, Ugur; Aras, Sefa
This article presents an improved version of the coyote optimization algorithm (COA) that is more compatible with nature. In the proposed algorithm, fitness-distance balance (FDB) and Levy flight were used to determine the social tendency of coyote packs and to develop a more effective model imitating the birth of new coyotes. The balanced search performance, global exploration capability, and local exploitation ability of the COA algorithm were enhanced, and the premature convergence problem resolved using these two methods. The performance of the proposed Levy roulette FDB-COA (LRFDBCOA) was compared with 28 other meta-heuristic search (MHS) algorithms to verify its effectiveness on 90 benchmark test functions in different dimensions. The proposed LRFDBCOA and the COA ranked, respectively, the first and the ninth, according to nonparametric statistical results. The proposed algorithm was applied to solve the AC optimal power flow (ACOPF) problem incorporating thermal, wind, and combined solar-small hydro powered energy systems. This problem is described as a constrained, nonconvex, and complex power system optimization problem. The simulation results showed that the proposed algorithm exhibited a definite superiority over both the constrained and highly complex real-world engineering ACOPF problem and the unconstrained convex/nonconvex benchmark problems.
Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination
(Springer, 2021) Kahraman, Hamdi Tolga; Bakir, Huseyin; Duman, Serhat; Kati, Mehmet; Aras, Sefa; Guvenc, Ugur
This article has four main objectives. These are: to develop the dynamic fitness-distance balance (dFDB) selection method for meta-heuristic search algorithms, to develop a strong optimization algorithm using the dFDB method, to create an optimization model of the coordination of directional overcurrent relays (DOCRs) problem, and to optimize the DOCRs problem using the developed algorithm, respectively. A comprehensive experimental study was conducted to analyze the performance of the developed dFDB selection method and to evaluate the optimization results of the DOCRs problem. Experimental studies were carried out in two steps. In the first step, to test the performance of the developed dFDB method and optimization algorithm, studies were conducted on three different benchmark test suites consisting of different problem types and dimensions. The data obtained from the experimental studies were analyzed using non-parametric statistical methods and the most effective among the developed optimization algorithms was determined. In the second step, the DOCRs problem was optimized using the developed algorithm. The performance of the proposed method for the solution to the DOCRs coordination problem was evaluated on five test systems including the IEEE 3-bus, the IEEE 4-bus, the 8-bus, the 9-bus, and the IEEE 30-bus test systems. The numerical results of the developed algorithm were compared with previously proposed algorithms available in the literature. Simulation results showed the effectiveness of the proposed method in minimizing the relay operating time for the optimal coordination of DOCRs.
Economic dispatch by using different crossover operators of genetic algorithm
(Istanbul University, 2010) Duman, Serhat; Öztürk, Ali; Döşoğlu, M. Kenan; Tosun, Salih
The great advances in technology and industry have brought about an increase in the demand for energy in electrical power systems. In order to meet this increased demand, planning the operation of power systems and optimum operation of those systems are required. To obtain it, optimal power flow, reactive power optimization and solutions of the economic dispatch problem are required. The problem of Economical Dispatch (ED) is one of the limited non-linear optimization problems of electrical power systems. Operation of generators with minimum cost by being held in certain limit values is required. Intuitional methods resulting in better conclusions have been used in solving this complicated non-linear problem so far. In this study, the conclusions obtained on conditions with line loss and without line loss of 3 and 6 switchboards with thermal fuel were compared with each other by using different crossover operators of genetic algorithm (GA).
Ekonomik Emisyon Yük Dağıtımı Problemi İçin Kaotik Yıldırım Arama Algoritması
(2018) Duman, Serhat; Yıldız, Bayram
Elektrik enerjisinin üretiminde kullanılan fosil yakıtlı kaynaklar çevre kirliliğine yolaçmaktadır. Bu nedenle, termal yakıtlı generatörlerde emisyon salınımının önemigiderek artmaktadır. Ekonomik emisyon yük dağıtımı problemi modern güçsistemlerinin en önemli doğrusal olmayan optimizasyon problemlerinden biridir.Yıldırım Arama Algoritması (YAA), ışık olayına dayalı lider yayılmamekanizmasından esinlenerek geliştirilen ve doğrusal olmayan optimizasyonproblemlerin çözümünde kullanılan sezgisel algoritmalardan biridir. Bu çalışmada,ekonomik emisyon yük dağıtımı problemi YAA ve kaotik YAA algoritmalarıkullanılarak çözülmüştür. Önerilen yaklaşımlar iki farklı test sistemine uygulanmışolup, benzetim çalışmalarından elde edilen sonuçlar literatürdeki diğer sonuçlarlakarşılaştırılmıştır. Önerilen yaklaşımın doğrusal olmayan mühendislik problemlerinçözümünde başarılı bir şekilde uygulandığı gösterilmiştir.
Energy Hub Economic Dispatch by Symbiotic Organisms Search Algorithm
(Springer International Publishing Ag, 2020) Guvenc, Ugur; Ozkaya, Burcin; Bakir, Huseyin; Duman, Serhat; Bingol, Okan
Energy hub receives various energy carriers such as gas, electricity, and heat in its input and then converts them into required demands such as gas, cool, heat, compressed air, and electricity. The energy hub economic dispatch problem is a non-smooth, high-dimension, non-convex, and non-differential problem, it should be solved subject to equality and inequality constraints. In this study, symbiotic organisms search algorithm is carried out for energy hub economic dispatch problem to minimize the energy cost of the system. In an attempt to show the efficiency of the proposed algorithm, an energy hub system, which has 7 hubs and 17 energy production units, has been used. Simulation results of the symbiotic organisms search algorithm have been compared with some heuristic algorithms to show the ability of the proposed algorithm.
Environmental/economic dispatch using genetic algorithm and simulated annealing
(2009) Erdoğmuş, Pakize; Öztürk, Ali; Duman, Serhat
In recent years, running of the generators at minimum cost and desired limit values has gradually increasing importance at Power systems using thermal-fueled generators. Various algorithms for solving economic dispatch problem have been found in the literature. In this study, Genetic Algorithm (GA) and Simulated Annealing (SA) solutions to Economic Cost Dispatch (ECD), Environmental Dispatch (ED), Environmental/Economic Dispatch (EED) have been found, by taking into account the environmental issue. A sample consisting of six thermal generators are presented. Transmission losses are included. Results taken with both methods have been compared to each other.
Estimation of the Induction Motor Parameters Using Biogeography Based Optimization Method
(Düzce Üniversitesi, 2013) Saraçoğlu, Bilal; Güvenç, Uğur; Dursun, Mustafa; Poyraz, Gökhan; Duman, Serhat
In this study, the determination of the electrical equivalent circuit parameters induction motors (IM) was carried out via Biogeography-Based Optimization (BBO). As an objective function in the algorithm, the equations of full-load torque, startup torque and breakdown torque of induction motors were used. The determination of the equivalent circuit parameters was performed with three different induction motors. Determination of the equivalent circuit parameters was performed for the induction motors which are 1.1kW and 0.37kW. The obtained results from the proposed approach are compared to obtained results from the Genetic Algorithm (GA).
FACTS Cihazlarını İçeren Reaktif Güç Planlama Probleminin Hibrit PSOGSA Algoritması Kullanarak Çözülmesi
(2018) Duman, Serhat
Optimal reaktif güç planlama problemi modern güç sistemlerinin en önemli problemlerinden biridir. Modern güçsistemlerinde reaktif güç planlamanın ana amacı, gerilim profilini iyileştirmek ve iletim hattının aktif güçkayıplarını azaltmaktır. Bu çalışmada, hibrit PSOGSA algoritması kullanılarak FACTS cihazlarını içeren reaktifgüç planlama probleminin çözülmesi amaçlanmıştır. Amaçlanan algoritma, tristör kontrollü seri kapasitör vetristör kontrollü faz kaydırıcı FACTS cihazlı IEEE 30 bara test sistemine uygulanmıştır. Amaçlanan hibritPSOGSA yaklaşımından elde edilen sonuçlar girdap algoritması (VS), ateş böceği algoritması (FA) veyerçekimsel arama algoritmasından elde edilen sonuçlarla karşılaştırılmıştır. Karşılaştırma sonuçları amaçlananyaklaşımın kullanılan diğer algoritmalara üstünlüğünü göstermektedir.
Fitness-Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources
(Elsevier, 2021) Guvenc, Ugur; Duman, Serhat; Kahraman, Hamdi Tolga; Aras, Sefa; Kati, Mehmet
One of the most difficult types of problems computationally is the security-constrained optimal power flow (SCOPF), a non-convex, nonlinear, large-scale, nondeterministic polynomial time optimization problem. With the use of renewable energy sources in the SCOPF process, the uncertainties of operating conditions and stress on power systems have increased even more. Thus, finding a feasible solution for the problem has become a still greater challenge. Even modern powerful optimization algorithms have been unable to find realistic solutions for the problem. In order to solve this kind of difficult problem, an optimization algorithm needs to have an unusual exploration ability as well as exploitation-exploration balance. In this study, we have presented an optimization model of the SCOPF problem involving wind and solar energy systems. This model has one problem space and innumerable local solution traps, plus a high level of complexity and discrete and continuous variables. To enable the optimization model to find the solution effectively, the adaptive guided differential evolution (AGDE) algorithm was improved by using the Fitness-Distance Balance (FDB) method with its balanced searching and high-powered diversity abilities. By using the FDB method, solution candidates guiding the search process in the AGDE algorithm could be selected more effectively as in nature. In this way, AGDE's exploration and balanced search capabilities were improved. To solve the SCOPF problem involving wind and solar energy systems, the developed algorithm was tested on an IEEE 30-bus test system under different operational conditionals. The simulation results obtained from the proposed algorithm were effective in finding the optimal solution compared to the results of the metaheuristics algorithms and reported in the literature. (C) 2021 Elsevier B.V. All rights reserved.
Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem
(Taylor & Francis Ltd, 2022) Sönmez, Yusuf; Duman, Serhat; Kahraman, Hamdi T.; Kati, Mehmet; Aras, Sefa; Güvenç, Uğur
The Transient Stability Constrained Optimal Power Flow (TSCOPF) has become an important tool for power systems today. TSCOPF is a nonlinear optimisation problem, making its solution difficult, especially for small power systems. This paper presents a new optimisation method that incorporates Fitness-Distance Balance (FDB) with the Artificial Ecosystem Optimisation (AEO) algorithm to improve the solution quality in multi-dimensional and nonlinear optimisation problems. The proposed method, named the Fitness-Distance Balance Artificial Ecosystem Optimisation (FDBAEO), also has the capacity to solve the TSCOPF problem efficiently. In order to evaluate the proposed algorithm, it was tested on IEEE CEC benchmarks and on an IEEE 30-bus test system for the TSCOPF problem. Simulation results were compared with the basic AEO algorithm and other current meta-heuristic methods reported in the literature. The results showed that the proposed method was more effective in converging at the global optimum point in solving the TSCOPF problem compared to the other algorithms. This situation indicates that the design changes made in the decomposition phase of the AEO were more suitable for simulating the operation of the algorithm in the real world. The FDBAEO has exhibited a promising performance in solving both single-objective optimisation and constrained real-world engineering design problems.
Fitness-Distance-Constraint (FDC) based guide selection method for constrained optimization problems
(Elsevier, 2023) Ozkaya, Burcin; Kahraman, Hamdi Tolga; Duman, Serhat; Guvenc, Ugur
In the optimization of constrained type problems, the main difficulty is the elimination of the constraint violations in the evolutionary search process. Evolutionary algorithms are designed by default according to the requirements of unconstrained and continuous global optimization problems. Since there are no constraint functions in these type of problems, the constraint violations are not considered in the design of the guiding mechanism of evolutionary algorithms. In this study, two new methods were introduced to redesign the evolutionary algorithms in accordance with the requirements of constrained optimization problems. These were (i) constraint space-based, called Fitness-Distance -Constraint (FDC), selection method and (ii) dynamic guiding mechanism. Firstly, thanks to the FDC guide selection method, the constraint violation values of the individuals in the population were converted into score values and the individuals who increase the diversity in the search process were selected as guide. On the other hand, in dynamic guiding mechanism, the FDC method was applied in case of constraint violation, otherwise the default guide selection method was used The proposed methods were used to redesign the guiding mechanism of adaptive guided differential evolution (AGDE), a current evolutionary algorithm, and the FDC-AGDE algorithm was designed. The performance of the FDC-AGDE was tested on eleven different constrained real-world optimization problems. The results of the FDC-AGDE and AGDE were evaluated using the Friedman and Wilcoxon test methods. According to Wilcoxon pairwise results, the FDC-AGDE showed better performance than the AGDE in nine of the eleven problems and equal performance in two of the eleven problems. Moreover, the proposed algorithm was compared with the competitive and up-to-date MHS algorithms in terms of the results of Friedman test, Wilcoxon test, feasibility rate, and success rate. According to Friedman test results, the first three algorithms were the FDC-AGDE, LSHADE-SPACMA, and AGDE algorithms with the score of 2.69, 4.05, and 4.34, respectively. According to the mean values of the success rates obtained from the eleven problems, the FDC-AGDE, LSHADE-SPACMA, and AGDE algorithms ranked in the first three with the success rates of 67%, 48% and 28%, respectively. Consequently, the FDC-AGDE algorithm showed a superior performance comparing with the competing MHS algorithms. According to the results, it is expected that the proposed methods will be widely used in the constrained optimization problems in the future.& COPY; 2023 Elsevier B.V. All rights reserved.
FOTOVOLTAİK ENERJİ SİSTEMLERİNİN MODELLENMESİ, BENZETİMİ ve UYGULAMASI
(Düzce Üniversitesi, 2014) Duman, Serhat; Yörükeren, Nuran; Altaş, İsmail Hakkı
The photovoltaic energy systems are one of the renewable energy sources. The output power of solar energy systems are changed with respect to environmental conditions of temperature and solar irradiance. In this study, the mathematical model of solar cell has been used and the simulation studies of this model have been performed in Matlab/Simulink software. Power-Voltage (P-V), Current-Voltage (I-V) characteristics have been investigated for different number of series-parallel connected solar cell and changing environmental conditions. Also, the maximum power points have been found to use perturbation-observation algorithm, which is one of the traditional algorithms under various environmental conditions of the solar energy systems. The performance of the algorithm has been examined in different step coefficients. Application of this algorithm is carried out in a laboratory environment.

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