It is difficult to calculate reservoir parameters of tight sand reservoirs using conventional interpretation methods, due to their complex lithology and variable pore structure. An optimization log interpretation meth...
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It is difficult to calculate reservoir parameters of tight sand reservoirs using conventional interpretation methods, due to their complex lithology and variable pore structure. An optimization log interpretation method is able to take full advantage of the log data and geological information. Therefore, it is an effective method to evaluate tight sand reservoirs. In this study, in order to calculate the reservoir parameters of tight sand reservoirs, an appropriate interpretation model needed to be first established according to the reservoirs’ characteristics. Then, the interpretation parameters were chosen, and the specific form of the objective function was determined. Next, an optimization algorithm was adopted to search for the optimal solution. A bacterial foraging algorithm (BFA) is a newly developed algorithm which has strong global search capabilities. It simulates the behavior of the colon bacillus which swims with flagella for food in the human gut. However, since it slowly converges in the later part of the optimization, it was combined in this study with a complex algorithm (CM) for constituting a BFA-CM hybrid algorithm, in order to improve the precision and efficiency of the search process. Also in this study, the unknown reservoir parameters of the optimization log interpretation method were determined using a genetic algorithm (GA), particle swarm optimization (PSO), BFA algorithm, and BFA-CM hybrid algorithm, respectively. The calculation results showed that, when compared with the GA and PSO, the errors of the porosity and the component content calculated by the BFA were minimal. However, the calculation result curves were found to be inconsistent. Therefore, by combining a BFA algorithm with a CM algorithm to constitute a BFA-CM hybrid algorithm for calculating reservoir parameters, the accuracy was improved, and the curves became more stable. The results of the BFA-CM optimization log interpretation method verified that the objective function va
The proposed research work deals with the execution of BFOA control for switched capacitor converter (SCC) with closed loop performance analysis. The above-culled converter is incipiently developed novel DC to DC conv...
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ISBN:
(纸本)9781467399265
The proposed research work deals with the execution of BFOA control for switched capacitor converter (SCC) with closed loop performance analysis. The above-culled converter is incipiently developed novel DC to DC converter with voltage hoist technique. The dynamic behavioural characteristics of power electronic converters is exceedingly nonlinear due to the nature of switching operation and unstable nature of time and, hence BFOA predicated bio-inspired controller is introduced for the developed novel converter [Intelligent swarm optimization network was developed for the current research work]. The closed loop performance investigation of the above converter utilizing software simulation (MATLAB) and proto type implementation with FPGA was validated. For the conditions like supply perturbances and load transmutes the performance of the converter is found to be preponderant.
With the rapid development of wind power and photovoltaic (PV) power industry, the curtailment of wind power or PV power in 'Three North' areas is serious, due to their short planning and construction time per...
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ISBN:
(纸本)9781467371063
With the rapid development of wind power and photovoltaic (PV) power industry, the curtailment of wind power or PV power in 'Three North' areas is serious, due to their short planning and construction time period, as well as being disjoined with the regional generation and grid plan. A novel formulation based on two-stage optimization under low-carbon economy is proposed in present paper to optimize the proportion of wind and PV capacity for provincial power systems, in which, carbon emissions of generator units and features of renewable resources are all taken into account. In the lower-level formulation, a time sequence production simulation (TSPS) model that is suitable for actual power system has been adopted. In order to maximize benefits of energy-saving and emissions reduction resulted from renewable power generation, General Algebraic Modeling System (GAMS), a commercial software, is employed to optimize the annual operation of the power system. In the upper-level formulation, a hybrid bacterial foraging algorithm and particle swarm optimization (BFAPSO) algorithm is utilized to optimize the proportion of wind and PV capacity. The objective of the upper level formulation is to maximize benefits of energy conservation and carbon emissions reductions optimized in the lower-level problem. Simulation results in practical provincial power systems validate the proposed model and corresponding solving algorithms. The optimization results can provide support to policy makers to make renewable energy related policies.
The Partial Transmit Sequence which reduces the PAPR (Peak-to-Average Power Ratio) in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system using a novel optimization algorithm i...
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In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the propose...
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In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rat e limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterialforaging (BF) algorithm with the Nelder-Mead (NM) method (called BF-NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF-NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
This paper presents current work on biologically-inspired optimisation techniques based on bacterial foraging algorithms (BFAs) and their application to modelling of a single-link flexible manipulator. The objective o...
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This paper presents current work on biologically-inspired optimisation techniques based on bacterial foraging algorithms (BFAs) and their application to modelling of a single-link flexible manipulator. The objective of this work is to develop a single-link flexible manipulator model based on modified BFAs. First, three adaptation mechanisms of the chemotactic step size mechanism of BFA are proposed. New approaches of adaptable chemotactic step size are based on linear, quadratic and exponential functions of cost function value. Then, these three adaptive BFAs are used to develop three single-input single-output models to characterise a flexible manipulator from torque input to hub-angle, hub velocity and end-point acceleration responses. The performances of the adaptive BFAs are compared to that of standard BFA based on convergence to optimum value, the optimum value achieved and time-domain and frequency domain responses of the developed models. (C) 2012 Elsevier Ltd. All rights reserved.
Environmental and economical aspects of Volt/Var Control problem as well as technical issues of the networks, transform the classical problem into multiobjective one. In this paper, a Multiobjective theta-Smart Bacter...
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Environmental and economical aspects of Volt/Var Control problem as well as technical issues of the networks, transform the classical problem into multiobjective one. In this paper, a Multiobjective theta-Smart bacterial foraging algorithm (M theta-SBFA) is proposed to solve the WC problem in distribution networks including Renewable Energy Sources (RESs) involving the conflicting objectives;i.e. the electrical energy losses, the voltages deviations, the total electrical energy costs and the total emissions of RESs and substations. The proposed algorithm goes through the search space in the polar coordinates instead of the Cartesian one;whereby the feasible space is more compact. Also, several modifications are applied to restraint the premature convergence of the solutions. Hence, the bacteria in chemotaxis process are moved in short or long steps as well as swimming movement. Furthermore, replacement the least healthy bacteria are done by the strong dominated solutions reserved along the iterative search process. A new approach based on the variance of the objective functions is utilized to select the population;thereby lead to uniformly distributed the Pareto Optimal Front (POF) as well as reach to extreme points of the solutions. Niching mechanism, besides the smart population, leads the bacteria towards the lesser covered space of the POF. A fuzzy clustering technique controls the size of the repository when it gets filled. Finally, two test distribution feeders are used to assess the performance of the proposed modifications. (C) 2013 Elsevier Ltd. All rights reserved.
In this paper it is proposed a parallel approach for the pixel intensity based image registration (IR) problem on multi-core processors. While IR is an optimization problem which computes the optimal parameters for a ...
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ISBN:
(纸本)9781479923724
In this paper it is proposed a parallel approach for the pixel intensity based image registration (IR) problem on multi-core processors. While IR is an optimization problem which computes the optimal parameters for a geometric transform, two classes of bio-inspired algorithms are studied: bacterialforaging Optimization algorithm (BFOA) and Genetic algorithm (GA). The optimal transform is applied to a source image in order to align it to a model image by maximizing a similarity measure. In the presented experiment, mutual information (MI) is used to evaluate the IR quality and most of the processing time is spent in this evaluation. The proposed parallel approach aims to reduce the processing time by using the full computing power of multi-core processors. A comparison of the sequential and parallel versions for different registration problems is presented.
Contrast enhancement plays an important role in image processing system, which is used to improve image quality or extract the fine details in degraded images. Image enhancement was regarded as an optimization problem...
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ISBN:
(纸本)9781479927913
Contrast enhancement plays an important role in image processing system, which is used to improve image quality or extract the fine details in degraded images. Image enhancement was regarded as an optimization problem and a kind of hybrid intelligent algorithm was proposed in this paper to optimize parameters of image enhancement operator who took the advantage of local gray distribution and the global statistical information of source image. Advantages of bacterial foraging algorithm and particle swarm optimization were combined into the Hybrid intelligent algorithm proposed in this paper, and the optimized fitness function was based on entropy and edge information of image. The results of simulation and experiment showed that after the application of this method not only the overall image contrast was enhanced but also details information of target image was effectively enriched, and noise amplification was restrained.
A smart grid is expected to be more flexible and more reliable than traditional electric power grids. Smart grid technology combines, photovoltaic systems, wind turbines, fuel cells, energy storages and electric vehic...
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ISBN:
(纸本)9781479910731
A smart grid is expected to be more flexible and more reliable than traditional electric power grids. Smart grid technology combines, photovoltaic systems, wind turbines, fuel cells, energy storages and electric vehicles charging from the grid and discharging back to the grid. Electric vehicles are one of the key pieces of the renewable energy economy of the future, but they do come with a few challenges-charging electric vehicles can add considerably to a home's overall electricity use, and when scaled up to thousands or millions of homes - that charging places a lot of extra demand on an electrical grid. This paper presents an economical insight into the management of renewable energy generation and charging/discharging of electric vehicles. This analysis will make use of an approach that will not only reduce the operational cost of renewables and capital cost of energy storages, it will also save cost for electric vehicle owners.
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