检索结果-内蒙古大学图书馆

Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms

引用

ENERGY 2012年第1期45卷 994-1006页

作者： Khorramdel, Benyamin Raoofat, Mahdi Shiraz Univ Dept Power & Control Eng Sch Elect & Comp Eng Shiraz Iran

Distributed Generators (DGs) in a microgrid may operate in three different reactive power control strategies, including PV, PQ and voltage droop schemes. This paper proposes a new stochastic programming approach for reactive power scheduling of a microgrid, considering the uncertainty of wind farms. The proposed algorithm firstly finds the expected optimal operating point of each DG in V-Q plane while the wind speed is a probabilistic variable. A multi-objective function with goals of loss minimization, reactive power reserve maximization and voltage security margin maximization is optimized using a four-stage multi-objective nonlinear programming. Then, using Monte Carlo simulation enhanced by scenario reduction technique, the proposed algorithm simulates actual condition and finds optimal operating strategy of DGs. Also, if any DGs are scheduled to operate in voltage droop scheme, the optimum droop is determined. Also, in the second part of the research, to enhance the optimality of the results, pso algorithm is used for the multi-objective optimization problem. Numerical examples on IEEE 34-bus test system including two wind turbines are studied. The results show the benefits of voltage droop scheme for mitigating the impacts of the uncertainty of wind. Also, the results show preference of pso method in the proposed approach. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Stochastic reactive power scheduling Voltage droop characteristic Scenario reduction Expected reactive power reserve Expected voltage security margin pso algorithm

来源：评论

学校读者我要写书评

暂无评论

Parameter identification and optimization algorithm in microbial continuous culture

引用

APPLIED MATHEMATICAL MODELLING 2012年第2期36卷 585-595页

作者： Shen, Bangyu Liu, Chongyang Ye, Jianxiong Feng, Enmin Xiu, Zhilong Dalian Univ Technol Sch Math Sci Dalian 116024 Liaoning Peoples R China Huaiyin Normal Univ Sch Math Sci Huaian 223300 Jiangsu Peoples R China Shandong Inst Business & Technol Sch Math & Informat Sci Yantai 264005 Shandong Peoples R China Dalian Univ Technol Sch Environm & Biol Sci Dalian 116024 Liaoning Peoples R China

In this study, a novelty mathematical model is established to formulate the continuous culture of glycerol to 1,3-Propanediol (1,3-PD) by Klebsiella pneumoniae, in which the inhibition of 3-hydroxypropionaldehyde (3-HPA) to cells growth and activity of some enzymes (such as glycerol dehydratase (GDHt) and 1,3-PD oxidoreductase (PDOR)), and the passive diffusion and active transport of glycerol and 1,3-PD across cell membrane are all taken into consideration. Taking the mean relative error between the experimental data and calculated values as the performance index, a parameter identification model involving multiple nonlinear dynamic systems is presented. The identifiability of the parameter identification model is also proved. Finally, an improved particle swarm optimization (pso) algorithm is constructed to find the optimal parameters for the systems under substrate limitation and excess conditions, respectively. Numerical results not only show that the established model can be used to describe the continuous fermentation reasonably, but also the improved pso algorithm is valid. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Parameter identification pso algorithm 1,3-Propanediol Continuous culture Nonlinear dynamic system

来源：评论

学校读者我要写书评

暂无评论

Integrated scheduling of flexible manufacturing system using evolutionary algorithms

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第5-8期61卷 621-635页

作者： Udhayakumar, P. Kumanan, S. Natl Inst Technol Dept Prod Engn Tiruchirappalli 620015 Tamil Nadu India

Effective sequencing and scheduling of the material handling system (MHS) have an impact on the productivity of the flexible manufacturing system (FMS). The MHS cannot be neglected while scheduling the production tasks. It is necessary to take into account the interaction between machines and MHS. This paper highlights the importance of integration between production schedule and MHS schedule in FMS. The Giffler and Thompson algorithm with different priority dispatching rules is developed to minimize the makespan in the FMS production schedule. Its output is used for MHS scheduling where the distance traveled and the number of backtrackings of the automated-guided vehicles are minimized using an evolutionary algorithms such as an ant colony optimization algorithm and particle swarm optimization (pso) algorithm. The proposed evolutionary algorithms are validated with benchmark problems. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithms. The analysis reveals that pso algorithm provides better solution with reasonable computational time.

关键词： FMS Production scheduling MHS scheduling ACO algorithm pso algorithm

来源：评论

学校读者我要写书评

暂无评论

A new fuzzy sliding mode controller for load frequency control of large hydropower plant using particle swarm optimization algorithm and Kalman estimator

引用

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER 2012年第6期22卷 812-830页

作者： Hooshmand, R. Ataei, M. Zargari, A. Univ Isfahan Dept Elect Engn Esfahan Iran

The load frequency control (LFC) is very important in power system operation and control for supplying sufficient, reliable, and high-quality electric power. The conventional LFC uses an integral controller. In this paper, a new control system based on the fuzzy sliding mode controller is proposed for controlling the load frequency of nonlinear model of a hydropower plant, and this control system is compared with the proportionalintegral controller and the conventional sliding mode controller. To regulate the membership functions of fuzzy system more accurately, the particle swarm optimization algorithm is also applied. Moreover, because of the unavailability of the control system variables, a nonlinear estimator is suggested for estimating and identifying the system state variables. This estimator provides the physical realization of the method and will reduce the costs of implementation. The proposed control method is performed for the LFC of hydropower plant of Karoon-3 in Shahrekord, Iran. The simulation results show the capability of the controller system in controlling local network frequency. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： hydropower plant sliding mode control extended Kalman estimator pso algorithm fuzzy control

来源：评论

学校读者我要写书评

暂无评论

Optimal power management and powertrain components sizing of fuel cell/battery hybrid electric vehicles based on particle swarm optimisation

引用

INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2012年第2-4期58卷 200-222页

作者： Hegazy, Omar Van Mierlo, Joeri Vrije Univ Brussel Dept ETEC B-1050 Brussels Belgium

Combining a Fuel Cell (FC), as primary power source, with a Battery Energy System (BES), as an auxiliary source, for high power demands is a promising approach for future hybrid electric vehicles (HEV). The powertrain control strategy and the component sizing significantly affect the vehicle performance, cost, vehicle efficiency and fuel economy. This paper presents a developed control strategy for optimising the power sharing between sources and components sizing by using Particle Swarm Optimisation (pso) algorithm. This control strategy implemented on FC/Battery hybrid electric vehicle in order to achieve the best performance with minimum fuel consumption and minimum powertrain components sizing for a given driving cycle with high efficiency. The powertrain and the proposed control strategy have been simulated by Matlab/Simulink. The simulation results have demonstrated that the optimal sizing of the powertrain of FC/battery components and the minimum fuel consumption have been improved by applying the pso control strategy.

关键词： FCHEV fuel cell hybrid electric vehicle powertrain modelling IMIPEC interleaved multiple-input power electronic converter fuel cell model battery model pso algorithm control strategies

来源：评论

学校读者我要写书评

暂无评论

A new approach to multi-aircraft air combat assignments

引用

SWARM AND EVOLUTIONARY COMPUTATION 2012年 6卷 39-46页

作者： Su, Mu-Chun Lai, Shih-Chang Lin, Shih-Chieh You, Liang-Fu Natl Cent Univ Dept Comp Sci & Informat Engn Taipei Taiwan

In this paper, a multi-layer perceptron based on 6 important features is proposed for predicting the time needed by our aircraft to approach enemy aircraft when they are in a combat situation. Then the prediction module is incorporated into a SOMO-based decision aid for solving multi-aircraft assignment problems. The problem of assigning our aircraft to meet head-on enemy aircraft in a combat situation is formulated as a constrained resource allocation problem. The objective function of the multi-aircraft assignment problem is a measure for computing the expected overall predominance value of aircraft over enemy aircraft based on four predominance factors. The SOMO algorithm which is an extension version of the SOM algorithm for optimization is executed to find an assignment such that the expected overall predominance value of aircraft can be maximized. Four hundred combat situations are simulated to test the effectiveness of the proposed SOMO-based decision aid. The simulation results demonstrate that the SOMO-based decision aid could provide appealing solutions within 0.5 s even for combat situations with a large number of aircraft. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Multi-aircraft assignment Optimization Swarm intelligence pso algorithm SOM algorithm

来源：评论

学校读者我要写书评

暂无评论

Particle swarm optimization algorithm to maximize assembly efficiency

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第5-8期59卷 719-736页

作者： Raj, M. Victor Sankar, S. Saravana Ponnambalam, S. G. Dr Sivanthi Aditanar Coll Engn Dept Mech Engn Tiruchendur 628215 Tamil Nadu India Kalasalingam Univ Dept Mech Engn Krishnankoil 626190 Tamil Nadu India Monash Univ Dept Mech Petaling Jaya 46150 Selangor Malaysia

The demands on assembly accuracy require accurate operations both in machining and assembly in order to achieve the high performance of products. Although advanced machining technologies can be used to satisfy most of the demands on precision assembly, the corresponding manufacturing cost will also be increased. Selective assembly provides an effective way for producing high-precision assembly from relatively low-precision components. The accuracy of selective assembly is mainly based on the number of groups and the range of the group (allocated equally on the design tolerance). However, there are often surplus parts in some groups due to the imbalance of mating parts, especially in the cases of undesired dimensional distributions, which makes the methods developed and reported in the literature often not suitable for practice. In this work, a particle swarm optimization (pso) algorithm is proposed by applying batch selective assembly method to a complex assembly with three mating components (as in ball bearing: an inner race, ball and outer race), to minimize the surplus parts and thereby maximizing the assembly efficiency. Due to the continuous nature of particle swarm optimization algorithm, standard pso equations cannot be used to generate discrete combination of mating parts. An effective encoding scheme is developed to make the combination of mating parts feasible. The evolution performance of the pso algorithm with different control parameter values is also analysed.

关键词： Batch selective assembly Surplus parts pso algorithm

来源：评论

学校读者我要写书评

暂无评论

Modelling and intelligent solving of foodstuff distribution VRP based on disruption management

引用

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第2期44卷 80-87页

作者： Xu, Degang Xiao, Renbin Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Henan Univ Technol Sch Informat Sci & Engn Zhengzhou 450001 Peoples R China

VRP problem is one of the key issues which affects the foodstuff distribution cost. Based on the features of foodstuff distribution and requirement of customer satisfaction, we construct a basic model that can be easily extended. In order to achieve the minimum cost of all kinds of disruptions, we add restrictions of cost and time, etc., introduce virtual task point and build a foodstuff distribution VRP model based on the disruption management. For the requirement of the complexity, timeliness of the model solution, we develop an improved Particle Swarm Optimisation algorithm adopting the Guided Local Search algorithm, and give the basic steps for solving the model. The research results of the case study show that, in dealing with disruptive VRP events, the model can effectively avoid the global adjustment of the foodstuff distribution VRP program.

关键词： foodstuff distribution vehicle routing problem disruption management pso algorithm

来源：评论

学校读者我要写书评

暂无评论

pso algorithm for Amending the "Minus Beam" Pointing Error After Phase Defocusing

PSO Algorithm for Amending the "Minus Beam" Pointing Error A...

引用

8th International Symposium on Antennas, Propagation and EM Theory

作者： Li, Jin Ding, Ying Hu, Rong Song, Lianguo Commun Repair Shop Unit 71375 Yuanxiao St Weifang 261041 Peoples R China PLAUST Dept Satellite Commun Nanjing 210007 Jiangsu Peoples R China

ISBN: (纸本)9781424421923

The "minus beam" steering of the phased arrays deviates from the given direction because of the quantization phase in the digital phased shifter. This paper studied the theory of wide-beam and "minus beam" and investigated the model of the particle swarm optimization method on the arrays' beam steering, and modified the range of the position and speed factor by the principle of random phase quantization. The simulating results show the effectiveness and correctness of the method.

关键词： pso algorithm phased array phase defocusing minus beam digital phase shifter quantization error

来源：评论

学校读者我要写书评

暂无评论

Based on Improved Particle Swarm Optimization algorithm Investment Combination Analysis

Based on Improved Particle Swarm Optimization Algorithm Inve...

引用

2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Shen Jia-jie E China Normal Univ Ctr Comp Sch Informat Sci & Technol Shanghai *** Peoples R China

ISBN: (纸本)9781467329644;9781467329637

Aiming to the problem about how to make optimization investment combination. The improved particle swarm optimization (pso) algorithm is presented to handle this question. Though theoretical deviation, the improved pso algorithm is proofed to be more adaptability than standard particle swarm optimization algorithm under this question. The correctness of theoretical deviation is verified by the experiment.

关键词： investment combination pso algorithm GA algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：