Recent ten years, the teaching learning based optimization algorithm (TLBO) has been widely concerned and successfully applied to solve various constraints and non-constraints problems. However, its convergence accura...
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Electrical energy distribution networks have had many developments in recent years. The advances in distributed generation (D.G) technology are also increasing in the presence of sensitive loads that require high reli...
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ISBN:
(纸本)9798350376357;9798350376340
Electrical energy distribution networks have had many developments in recent years. The advances in distributed generation (D.G) technology are also increasing in the presence of sensitive loads that require high reliability in this network. This article has attempted to capacity credit (C.C) as a new issue in Electrical energy distribution by using D.G sources. The D.G studied in this article was considered wind sources. The problem of planning D.G in the distribution network was modeled as a non-linear optimization. The objective function includes increasing the reliability, reducing loss, and improving the capacity credit of wind-dispersed generation sources. The problem model also considers the technical and the network's economic constraints and D.G's economic constraints. In the planning process, the uncertainties of the network load and the generation power of wind sources were modeled by combining two methods, Monte Carlo and k-means. The cut-set was also used to evaluate the reliability of the network. For the efficiency of the proposed response, the IEEE 33-bus distribution network was studied under two scenarios using the teachinglearning-basedoptimizationalgorithm. The article's results showed that D.G can create capacity credit up to 33% of the network load.
Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usua...
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Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teachinglearningbasedoptimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.
The prime objective of this paper is to devise optimization technique for the position control of Sun Tracking System (STS). In order to control the position of this system, PID controller tuned by different methods s...
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The prime objective of this paper is to devise optimization technique for the position control of Sun Tracking System (STS). In order to control the position of this system, PID controller tuned by different methods such as Genetic algorithm (GA), Particle Swarm optimization (PSO) and teachinglearningbasedoptimization (TLBO) is used. These methods have been carried out to determine the most optimized and robust one for the position control. The simulated results for nominal and perturbed value of servo amplifier gain show that the PID controller tuned with TLBO technique gives most optimized and robust performance in comparison to other tuning techniques. (C) 2019 Published by Elsevier Ltd.
The prime objective of this paper is to devise optimization technique for the position control of Sun Tracking System (STS). In order to control the position of this system, PID controller tuned by different methods s...
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The prime objective of this paper is to devise optimization technique for the position control of Sun Tracking System (STS). In order to control the position of this system, PID controller tuned by different methods such as Genetic algorithm (GA), Particle Swarm optimization (PSO) and teachinglearningbasedoptimization (TLBO) is used. These methods have been carried out to determine the most optimized and robust one for the position control. The simulated results for nominal and perturbed value of servo amplifier gain show that the PID controller tuned with TLBO technique gives most optimized and robust performance in comparison to other tuning techniques. (C) 2019 Published by Elsevier Ltd.
The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints bas...
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The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using teachinglearningbasedoptimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimizationalgorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.
This study presents comparison of methods to compute optimum value of input parameter to optimize given objective function. In this study objective functions are a basic thermal system and 5 benchmark function. Compar...
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ISBN:
(纸本)9781538617199
This study presents comparison of methods to compute optimum value of input parameter to optimize given objective function. In this study objective functions are a basic thermal system and 5 benchmark function. Comparison is made between T.L.B.O. algorithm (teaching learning based optimization algorithm, a nature inspired evolutionary algorithm) and P.E.A. (Planetary Extinction algorithm, a proposed algorithm). Results are concluded by testing both algorithms on thermal system and on 5 benchmark function. In this study thermal system is a basic steam power plant system consisting of boiler, turbine, condenser, pump (neglected because it does not impart much work). Study suggests PEA performed better than TLBO in 5 out of 6 test performed.
This paper deals with a novel quasi-oppositional harmony search algorithm (QOHSA) based design of load frequency controller for an autonomous hybrid power system model (HPSM) consisting of multiple power generating un...
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This paper deals with a novel quasi-oppositional harmony search algorithm (QOHSA) based design of load frequency controller for an autonomous hybrid power system model (HPSM) consisting of multiple power generating units and energy storage units. QOHSA is a novel improved version of music inspired harmony search algorithm for obtaining the best solution vectors and faster convergence rate. In this paper, the efficacy of the proposed QOHSA is adjudged for optimized load frequency control (LFC) of an autonomous HPSM. The studied HPSM consists of renewable/non-renewable energy based generating units such as wind turbine generator, solar photovoltaic, solar thermal power generator, diesel engine generator, fuel cell with aqua-electrolyzer while energy storage units consists of battery energy storage system, flywheel energy storage system and ultra-capacitor. Gains of the conventional controllers such as integral (I) controller, proportional-integral (PI) controller and proportional-integral-derivative (PID) controller (installed as frequency controller one at a time in the proposed HPSM) is optimized using QOHSA to mitigate any frequency deviation owing to sudden generation/load change. In order to corroborate the efficacy of QOHSA, performance of QOHSA to design optimal LFC is compared with that of other well-established technique such as teaching learning based optimization algorithm (TLBOA). The comparative performances of the HPSM under the action of QOHSA/TLBOA based optimized conventional controllers (I or PI or PID) are investigated and compared in the present work. It is found that the QOHSA tuned frequency controllers improves the overall dynamic response in terms of settling time, overshoot and undershoot in the profile of frequency deviation and power deviation of the studied HPSM. (C) 2015 Elsevier Ltd. All rights reserved.
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