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Öğe 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, MustafaCombined 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.Öğe 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, SefaThis 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.Öğe 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, UgurThis 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.Öğe Energy Hub Economic Dispatch by Symbiotic Organisms Search Algorithm(Springer International Publishing Ag, 2020) Guvenc, Ugur; Ozkaya, Burcin; Bakir, Huseyin; Duman, Serhat; Bingol, OkanEnergy 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.Öğe Entropy-Based Skin Lesion Segmentation Using Stochastic Fractal Search Algorithm(Springer International Publishing Ag, 2020) Bingol, Okan; Pacaci, Serdar; Guvenc, UgurSkin cancer is a type of cancer that attracts attention with the increasing number of cases. Detection of the lesion area on the skin has an important role in the diagnosis of dermatologists. In this study, 5 different entropy methods such as Kapur, Tsallis, Havrda and Charvat, Renyi and Minimum Cross were applied to determine the lesion area on dermoscopic images. Stochastic fractal search algorithm was used to determine threshold values with these 5 methods. PH2 data set was used for skin lesion images.Öğe 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, MehmetOne 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.Öğe Fitness-Distance-Constraint (FDC) based guide selection method for constrained optimization problems(Elsevier, 2023) Ozkaya, Burcin; Kahraman, Hamdi Tolga; Duman, Serhat; Guvenc, UgurIn 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.Öğe Genetic PI based model and path tracking control of four traction electrical vehicle(Springer, 2020) Dogan, Muhsin Ugur; Guvenc, Ugur; Elmas, CetinModeling and control of four-wheel electric vehicles are difficult due to their dynamic parameters and variable road conditions. In this paper, a robust and adaptive electric vehicle model and position control that can be adapted to state variables using a dynamic lateral and longitudinal model of a four-wheel electric vehicle have been proposed. The longitudinal and lateral forces have been modeled according to Newton's second law, depending on the parameters such as the vehicle's size, width, height, weight and slope angle by using dynamic equations of the vehicle. In this paper, a permanent magnet synchronous hub motor has been used for each wheel of the electric vehicle. The magic formula wheel model has been used to determine the relationship between the slip and the friction of the designed vehicle. Using the slip system, the relationship between the speed of the electric vehicle itself and the wheel speeds have been defined. The proportional controller at the position loop and proportional + integral controller at the speed loop of the designed system have been used. In the path tracking control system, position controls have been made in the X and Y coordinate planes. A P position controller and a PI speed controller have been used for each plane. Thus, there are 6 controller coefficients in total. Because of the complicated structure of the system, it is difficult to determine the most suitable controller coefficients by analytical methods. Therefore, the genetic algorithm which is one of the heuristic algorithms has been used in determining these coefficients. Simulation studies have been conducted with a different path and position references to see the effectiveness of the proposed electric vehicle model and position control. The obtained results show that the proposed model and control system are robust, effective and reliable.Öğe Improved adaptive gaining-sharing knowledge algorithm with FDB-based guiding mechanism for optimization of optimal reactive power flow problem(Springer, 2023) Bakir, Huseyin; Duman, Serhat; Guvenc, Ugur; Kahraman, Hamdi TolgaOptimal reactive power flow (ORPF) is of great importance for the electrical reliability and economic operation of modern power systems. The integration of distributed generations (DGs) and two-terminal high voltage direct current (HVDC) systems into electrical networks has further complicated the ORPF problem. Due to the high computational complexity of the ORPF problem, a powerful and robust optimization algorithm is required to solve it. This paper proposes a powerful metaheuristic algorithm namely fitness-distance balance-based adaptive gaining-sharing knowledge (FDBAGSK). In the performance evaluation, 39 IEEE CEC benchmark functions are used to compare FDBAGSK with the original AGSK algorithm. Moreover, the proposed algorithm is applied to perform the ORPF task in modified IEEE 30- and IEEE 57-bus test systems. The effectiveness of the FDBAGSK method was tested for the optimization of three non-convex objectives: active power loss, voltage deviation and voltage stability index. The ORPF results obtained from the FDBAGSK algorithm are compared with other optimization algorithms in the literature. Given that all results are together, it has been observed that FDBAGSK is an effective method that can be used in solving global optimization and constrained real-world engineering problems.Öğe A new objective function design for optimization of secondary controllers in load frequency control(Gazi Univ, Fac Engineering Architecture, 2021) Yilmaz, Zumre Yenen; Bal, Gungor; Celik, Emre; Ozturk, Nihat; Guvenc, Ugur; Arya, YogendraIn this study, load frequency control (LFC) of two-area non-reheat thermal power system and multi-source power systems is addressed. A simple PID-structured controller is used as a secondary controller in these systems. To raise the performance of PID controller, a new multi-objective function is designed and PID controller parameters are acquired by minimizing the value of this function with symbiotic organisms search (SOS) algorithm. All electrical power systems simulated are modeled in MATLAB/Simulink environment and the optimizer is coded in MATLAB/M-file platform. In order to affirm the contribution of the work, results collected from each power system are compared with popular results published in prestigious journals. As per the comparative results, despite its simplicity, SOS:PID controller tuned via the proposed objective function is observed to result in better performance than other approaches in terms of oscillations, settling time, maximum overshoot and maximum undershoot time domain indicators of the frequency and tie-line power change curves.Öğe Novel active-passive compensator-supercapacitor modeling for low-voltage ride-through capability in DFIG-based wind turbines(Springer, 2019) Dosoglu, M. Kenan; Ozkaraca, Osman; Guvenc, UgurLow-voltage ride-through is important for the operation stability of the system in balanced- and unbalanced-grid-fault-connected doubly fed induction generator-based wind turbines. In this study, a new LVRT capability approach was developed using positive-negative sequences and natural and forcing components in DFIG. Besides, supercapacitor modeling is enhanced depending on the voltage-capacity relation. Rotor electro-motor force is developed to improve low-voltage ride-through capability against not only symmetrical but also asymmetrical faults of DFIG. The performances of the DFIG with and without the novel active-passive compensator-supercapacitor were compared. Novel active-passive compensator-supercapacitor modeling in DFIG was carried out in MATLAB/SIMULINK environment. A comparison of the system behaviors was made between three-phase faults, two-phase faults and a phase-ground fault with and without a novel active-passive compensator-supercapacitor modeling. Parameters for the DFIG including terminal voltage, angular speed, electrical torque variations and d-q axis rotor-stator current variations, in addition to a 34.5 kV bus voltage, were investigated. It was found that the system became stable in a short time and oscillations were damped using novel active-passive compensator-supercapacitor modeling and rotor EMF.Öğe A novel optimal power flow model for efficient operation of hybrid power networks(Pergamon-Elsevier Science Ltd, 2023) Bakir, Huseyin; Duman, Serhat; Guvenc, Ugur; Kahraman, Hamdi TolgaIn the power industry, the design of an efficient optimal power flow (OPF) model is one of the important research challenges. This study presents the formulation and solution of the OPF problem in the presence of RESs, VSC-MTDC transmission lines, and FACTS devices, simultaneously. Fuel cost, voltage deviation, and power loss were selected as OPF objectives and optimized with state-of-the-art metaheuristic algorithms such as MFO, BSA, COA, MRFO, TLABC, and FDB-TLABC. Based on the optimization results, FDB-TLABC has obtained the best fuel cost results of 785.5850 $/h, 815.1251 $/h, and 820.3022 $/h on the IEEE 30-bus power network. Besides, the algorithm reduced voltage deviation and active power loss by 11.76% and 0.52% compared to TLABC which second most successful algorithm, respectively. The experimental results are statistically analyzed using Wilcoxon signed-rank test. The analysis results show that the FDB-TLABC is a robust and powerful method to solve the introduced OPF problem.Öğe Optimal Power Flow for Hybrid AC/DC Electrical Networks Configured With VSC-MTDC Transmission Lines and Renewable Energy Sources(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Bakir, Huseyin; Guvenc, Ugur; Duman, Serhat; Kahraman, Hamdi TolgaThis article presents the single objective optimal power flow (OPF) formulation incorporating both renewable energy sources, and voltage source converter-based multiterminal direct current transmission lines, simultaneously. To solve the formulated OPF problem, powerful metaheuristic optimization algorithms including adaptive guided differential evolution, marine predators algorithm, atom search optimization, stochastic fractal search (SFS), and fitness-distance balance-based SFS (FDB-SFS) are employed. The performance of the algorithms is tested for the minimization of fuel cost, pollutant emissions of thermal generators, voltage deviation, and active power loss in a modified IEEE 30-bus power network. The simulation results give that FDB-SFS achieved the best results on the fuel cost (786.5361 $/h), the fuel cost with valve point effect (815.6644 $/h), and the fuel cost with emission-carbon tax (820.5991 $/h). In addition, FDB-SFS reduced voltage deviation and active power loss values by 14.2587% and 6.7438% compared to SFS. The nonparametric Wilcoxon and Friedman statistical test results confirmed that FDB-SFS is an effective and robust algorithm that can be used in the optimization of the introduced OPF problem.Öğe Optimal power flow solution with stochastic wind power using the Levy coyote optimization algorithm(Springer London Ltd, 2021) Kaymaz, Enes; Duman, Serhat; Guvenc, UgurOptimal power flow (OPF) is one of the most fundamental single/multi-objective, nonlinear, and non-convex optimization problems in modern power systems. Renewable energy sources are integrated into power systems to provide environmental sustainability and to reduce emissions and fuel costs. Therefore, some conventional thermal generators are being replaced with wind power sources. Although wind power is a widely used renewable energy source, it is intermittent in nature and wind speed is uncertain at any given time. For this reason, the Weibull probability density function is one of the important methods used in calculating available wind power. This paper presents an improved method based on the Levy Coyote optimization algorithm (LCOA) for solving the OPF problem with stochastic wind power. In the proposed LCOA, Levy Flights were added to the Coyote optimization algorithm to avoid local optima and to improve the ability to focus on optimal solutions. To show the effect of the novel contribution to the algorithm, the LCOA method was tested using the Congress on Evolutionary Computation-2005 benchmark test functions. Subsequently, the solution to the OPF problem with stochastic wind power was tested via the LCOA and other heuristic optimization algorithms in IEEE 30-bus, 57-bus, and 118-bus test systems. Eighteen different cases were executed including fuel cost, emissions, active power loss, voltage profile, and voltage stability, in single- and multi-objective optimization. The results showed that the LCOA was more effective than the other optimization methods at reaching an optimal solution to the OPF problem with stochastic wind power.Öğe Optimal solution of the combined heat and power economic dispatch problem by adaptive fitness-distance balance based artificial rabbits optimization algorithm(Pergamon-Elsevier Science Ltd, 2024) Ozkaya, Burcin; Duman, Serhat; Kahraman, Hamdi Tolga; Guvenc, UgurCombined heat and power economic dispatch (CHPED) problem is one of the most widely handled, optimization problem by researchers in modern power systems. CHPED problem is a complicated, non-continuous, and nonconvex optimization problem due to the constraints. Moreover, considering the valve-point loading effect (VPLE), transmission losses (TLs), and prohibited operating zones (POZs) of power-only units as constraints, the complexity of CHPED problem increases. Therefore, a powerful optimization algorithm needs to be introduced to find global solution that meets all constraints. In this paper, a novel adaptive fitness-distance balance based artificial rabbits optimization (AFDB-ARO) is developed to solve CHPED problems. AFDB-based guiding mechanism was implemented to enhance the exploration capability of ARO and to strengthen exploitation-exploration balance. A comprehensive experimental study was realized to prove the performance of the proposed algorithm on the CHPED and benchmark problems. In experimental study between AFDB-ARO variants and ARO on 40 benchmark problems, according to Wilcoxon analysis results, all AFDB-ARO variants outperformed the base ARO, and the best AFDB-ARO variant won victory in 20 of 40 problem and achieved similar results in other 20 problem. In other experimental study, AFDB-ARO algorithm was implemented on the CHPED systems with 4-, 5-, 7-, 24-, 48-, 96-, and 192-units, and fifteen case studies were considered using these systems, VPLE, TLs, and POZs. One of the important points of this study was that POZs were considered for the first time in 96-and 192 -units system. The results show that AFDB-ARO achieved the best optimal solution in ten of fifteen cases, was same in one case, and obtained almost same results in four cases compared to the literature. Moreover, the stability of the AFDB-ARO and base ARO algorithms in solving the CHPED problem were tested by performing stability analysis. While the mean success rate, mean iteration number, and mean search time were obtained 87.62%, 353.63, and 2.91 sec of AFDB-ARO, respectively, ARO managed to find the optimal solution in two cases. Thus, the superior performance of AFDB-ARO algorithm is confirmed by experimental studies and analysis against ARO algorithm. The source codes of the AFDB-ARO algorithm (proposed method) can be accessed at this link: https://www.mathworks.com/matlabcentral/fileexchange/136846-afdb-aro-an-improved-aro-algorithm-for-optimization-problem.Öğe Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications(Elsevier Sci Ltd, 2023) Kocak, Burak; Pinarci, Brahim; Guvenc, Ugur; Kocak, YilmazIn this study, two different Artificial neural networks (ANN) and two different adaptive network-based fuzzy inference systems (ANFIS) models were constructed to predict the compressive strength of 7 different cement mortar samples with or without pumice and/or diatomite on different days. Five parameters including day, PC, pumice, diatomite and water were employed as the inputs, and the compressive strength was used as the output variable. The compressive strengths used in the model construction were obtained from laboratory experiments accounting for a total of 168 data. Statistical methods such as R2, RMS and MAPE preferred in the literature were used to compare the four different models. According to the test results obtained from R2, RMS and MAPE, ANN and ANFIS models were able to make very good predictions performance. For this reason, it can be said that these cement mortars' compressive strength can be estimated with a very small error and in a short time with both ANN and ANFIS models.
