Evaluation of atrial electromechanical conduction delay in case of hemodynamically insignificant rheumatic heart disease: A tissue Doppler study

Background: Atrial electromechanical delay (AEMD) that reflects delayed conduction may show us the clinical reflection of pathological changes in the atria. The main objective of the present study is to investigate AEMD in patients who had previous rheumatic carditis but without hemodynamically significant valvular disease. Methods: A total of 40 patients, previously diagnosed as rheumatic carditis but without significant valvular stenosis/regurgitation and atrial enlargement; and 39 age- and-sex matched controls were enrolled for the present study. Parameters of AEMD (lateral mitral annulus electromechanical delay, septal mitral annulus electromechanical delay and lateral tricuspid annulus electromechanical delay) were measured with tissue Doppler echocardiography and left intra-atrial and inter-atrial conduction times were calculated accordingly. A 24h ambulatory Holter monitoring was used in both groups to detect atrial fibrillation episodes and quantify atrial extrasystoles. Results: Parameters of AEMD, including left intra-atrial and inter-atrial conduction times of subjects in the study group were longer compared to the control group (23.7 ± 7.0 vs. 18.3 ± 6.2). Conclusions: Increased AEMD is observed in patients with previous rheumatic carditis and no significant valvular stenosis/regurgitation and atrial enlargement, which may partly explain the increased incidence of atrial fibrillation observed in these patients

Genetic PI based model and path tracking control of four traction electrical vehicle

Modeling 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.WOS:0005347255000012-s2.0-8508529761

Investigation the Success of Semidefinite Programming for the Estimating of Fuel Cost Curves in Thermal Power Plants

Accurate estimation of fuel cost curve parameters in thermal power plants is of great importance because these parameters directly influence the economic dispatch calculations. In this paper, a semidefinite programming (SDP) approach was proposed for the estimation of fuel cost functions' parameters in thermal power plants. The parameter estimation problem was designed as a minimization problem, where the objective function was accepted as the total absolute error (TAE) in the study. Also, linear, quadratic, and cubic fuel cost functions were used to estimate the fuel cost parameters. Different fuel types such as coal, oil and gas were preferred for simulation studies. The results achieved from the semidefinite programming method were compared with that of particle swarm optimization (PSO), artificial bee colony (ABC), crow search algorithm (CSA) and least error square (LES) methods, respectively. The performance of the methods were compared according to the TAE parameter. Simulation results showed that SDP method is more successful than other methods considered in this paper. Clearly, the present paper showed that SDP has a higher potential to solve parameter estimation problems

Entropy-Based Skin Lesion Segmentation Using Stochastic Fractal Search Algorithm

International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYSkin 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.WOS:0006787710000692-s2.0-8508343055

Optimal power flow solution with stochastic wind power using the Levy coyote optimization algorithm

Optimal 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.WOS:0005905348000022-s2.0-8509632391

Novel active-passive compensator-supercapacitor modeling for low-voltage ride-through capability in DFIG-based wind turbines

Low-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.WOS:0004902298000022-s2.0-8507449521

Development of a Levy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems

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.WOS:0006250396000032-s2.0-8510202754

Development of a Levy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems

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.WOS:0006250396000032-s2.0-8510202754

Fitness-Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources

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.WOS:0006700685000092-s2.0-8510480687

Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination

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