Combined economic and emission dispatch (CEED) in power system is aimed to solve the economics scheduling of generators in order to produce both minimum fuel costs and emission levels, at the same time meets the load ...
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ISBN:
(数字)9781665468060
ISBN:
(纸本)9781665468060
Combined economic and emission dispatch (CEED) in power system is aimed to solve the economics scheduling of generators in order to produce both minimum fuel costs and emission levels, at the same time meets the load demands and its operating constraints. In this paper, Search and Rescue (SAR) optimization technique has been proposed to solve the CEED problem, at which the results obtained are compared with Flower Pollination Algorithm and evolutionary programming methods. The comparisons performed can evaluate both the effectiveness and convergence rate among the studied algorithms, with multiple constraints and different cost curve nature. In general, the CEED problem is considered as biobjective problem initially and it has been transformed into single objective function by using the price penalty factor in its solution. All techniques have been implemented on IEEE 3-Generator 9-Bus system, having a valve point effect with transmission loss. Moreover, MATLAB is used to run the simulations for the test system with three different load demands on each system respectively. Results show that SAR records lowest power loss (0.37-1.62% of power demand), as compared with FPA (0.75-2.36%) and EP (1.00-3.28%). SAR method also show better performance in solving CEED problem with minimum total fuel cost, total emissions and overall CEED cost.
This paper focuses on the management of recovered thermal energy of a hybrid wind energy and grid-parallel PEM fuel cell power plant (FCPP) with the object of achieving optimal cost. The fluctuating nature or wind ene...
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ISBN:
(纸本)9781424438105
This paper focuses on the management of recovered thermal energy of a hybrid wind energy and grid-parallel PEM fuel cell power plant (FCPP) with the object of achieving optimal cost. The fluctuating nature or wind energy (WE) has a different effect on the system operational cost and constraints. Besides, FCPPs are capable of producing both electrical and thermal energy. Combining WE and FCPP in a hybrid structure for CHP system yields lower operational cost than that of individual units. An economic approach is presented which includes the operational cost, thermal recovery, power trade with the local grid, and selling of surplus thermal energy. Multiple operational strategies are developed using this approach. The strategies are then evaluated by estimating the hourly generated power, the amount of recovered thermal energy while satisfying the thermal and electrical load requirements. An evolutionary programming-based technique is used to solve for the optimal operational strategy. Results are encouraging and indicate viability of the proposed approach.
As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the study and design of human systems helps provide efficient optimization algorithms....
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ISBN:
(数字)9781522563204
ISBN:
(纸本)9781522563198;1522563199
As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the study and design of human systems helps provide efficient optimization algorithms. Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities is an essential reference source that discusses applications of bio-inspired algorithms in data mining, computer vision, image processing, and pattern recognition, as well as methods of designing competent algorithms based on decomposition principles. Featuring research on topics such as cluster analysis, metaheuristic optimization, and image processing, this book is ideally designed for IT professionals, computer engineers, researchers, academicians, and upper-level students seeking coverage on how to develop efficient clustering algorithms.
Flexible Manufacturing Systems-FMS is a term with various types of definitions,each of them trying to describe the complexity and the generalized *** of these features is their complexity,along with difficulties in bu...
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ISBN:
(纸本)9783037856017
Flexible Manufacturing Systems-FMS is a term with various types of definitions,each of them trying to describe the complexity and the generalized *** of these features is their complexity,along with difficulties in building models that capture the system in all its important *** a heterogeneous flexible system,the scheduling events or actions could be a combinatorial problem which claims a particular *** scheduling process,in special for FMS,is a very difficult scheduling problem,because involves all the aspects of the processes:order,resources,transportation system *** vehicle guided,perturbation factors such as breakdowns of machine,***,the scheduling problem is a NP-hard problem modeled in mathematical *** we simulate n jobs or orders which have to be assigned to the m machines or resources,we will observe that the mathematical solution is a huge number that means(n!)m possibilities of *** challenge of researchers is to solve this equation in a reasonable time with an optimal solution,and of course with minimal *** scientists applied many solutions which became Operational Research-OR or Combinatorial Optimization-CO areas using a various methods:Local Search-LS,Artificial Intelligence-AI,heuristic method,priority rules,memetic or hybrid techniques which combine this techniques.
Emulation plays an important role in engineering design. However, most emulators such as Gaussian processes (GPs) are exclusively developed for interpolation/regression and their performance significantly deteriorates...
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Emulation plays an important role in engineering design. However, most emulators such as Gaussian processes (GPs) are exclusively developed for interpolation/regression and their performance significantly deteriorates in extrapolation. To address this shortcoming, we introduce evolutionary Gaussian processes (EGPs) that aim to increase the extrapolation capabilities of GPs. An EGP differs from a GP in that its training involves automatic discovery of some free-form symbolic bases that explain the data reasonably well. In our approach, this automatic discovery is achieved via evolutionary programming (EP) which is integrated with GP modeling via maximum likelihood estimation, bootstrap sampling, and singular value decomposition. As we demonstrate via examples that include a host of analytical functions as well as an engineering problem on materials modeling, EGP can improve the performance of ordinary GPs in terms of not only extrapolation, but also interpolation/regression and numerical stability.
Evoluční techniky jsou neustále se vyvíjející a progresivní část informatiky. Evoluční algoritmy se v praxi používají k řešení mnohých druhů problémů od...
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Evoluční techniky jsou neustále se vyvíjející a progresivní část informatiky. Evoluční algoritmy se v praxi používají k řešení mnohých druhů problémů od optimalizace až k plánování. Tato práce se zabývá genetickým a kartézským genetickým programováním, které patří mezi nejčastěji používané algoritmy. Cílem práce je implementovat jednotlivé přístupy a vyhodnotit jejich účinnost v úloze symbolické regrese.
In this paper a family of new methods are proposed for constructing hierarchical-interpolative fuzzy rule bases in the frame of a fuzzy rule based supervised machine learning system modeling black box systems defined ...
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In this paper a family of new methods are proposed for constructing hierarchical-interpolative fuzzy rule bases in the frame of a fuzzy rule based supervised machine learning system modeling black box systems defined by input-output pairs. The resulting hierarchical rule base is constructed by using structure building pure evolutionary and memetic techniques, namely, Genetic and Bacterial programming Algorithms and their memetic variants containing local search steps. Applying hierarchical-interpolative fuzzy rule bases is a rather efficient way of reducing the complexity of knowledge bases, whereas evolutionary methods (including memetic techniques) ensure a relatively fast convergence in the learning process. As it is presented in the paper, by applying a newly proposed representation schema these approaches can be combined to form hierarchical-interpolative machine learning systems.
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