检索结果-内蒙古大学图书馆

High-performance Brillouin distributed temperature sensor using Fourier wavelet regularised deconvolution algorithm

IET OPTOELECTRONICS 2014年第6期8卷 203-209页

作者： Pradhan, Himansu Shekhar Sahu, Prasant Kumar Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar 751013 Odisha India

In this study, the authors presented a distributed optical fibre temperature sensor whose performance is improved using the optimisation techniques and Fourier wavelet regularised deconvolution (ForWaRD) method. As the input power launched into the sensing fibre is critical, the authors have considered both conventional optimisation technique and evolutionary optimisation techniques namely: genetic algorithm, differential evolution algorithm and particle swarm optimisation algorithm to increase the stimulated Brillouin scattering (SBS) threshold power. Using the optimised value of the parameters and employing evolutionary computing techniques, the proposed 50 km long temperature sensing system offers a 4.7 dB improvement in SBS threshold power over the design of experiment based system. It is being verified that power of the backscattered signals approximately are the convolution of the input pulsed power and corresponding backscatter optical fibre impulse responses. The Fourier wavelet regularised deconvolution (ForWaRD) method is employed to improve the spatial resolution of the proposed sensing system without reducing the pulse width of the input pulse. Employing ForWaRD technique a 10 m better spatial resolution observed as compared with the Fourier deconvolution technique. The proposed 50 km long temperature sensing system exhibits a temperature resolution of 1.85 K because of suppression of SBS threshold and noise reduction.

关键词： fibre optic sensors temperature sensors stimulated Brillouin scattering Fourier transform optics wavelet transforms deconvolution genetic algorithms particle swarm optimisation distributed sensors optical information processing signal denoising high-performance Brillouin distributed temperature sensor Fourier wavelet regularised deconvolution algorithm optical fibre temperature sensor ForWaRD method evolutionary optimisation techniques genetic algorithm differential evolution algorithm particle swarm optimisation algorithm stimulated Brillouin scattering SBS threshold power input pulsed power convolution backscatter optical fibre impulse responses spatial resolution SBS threshold suppression noise reduction

来源：评论

学校读者我要写书评

暂无评论

optimisation model for power system restoration with support from electric vehicles employing battery swapping

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第3期10卷 771-779页

作者： Sun, Lei Wang, Xiaolei Liu, Weijia Lin, Zhenzhi Wen, Fushuan Ang, Swee Peng Salam, Md. Abdus Zhejiang Univ Sch Elect Engn Hangzhou 310027 Zhejiang Peoples R China Inst Teknol Brunei Dept Elect & Elect Engn BE-1410 Bandar Seri Begawan Brunei

The energy stored in the batteries of electric vehicles (EVs) could be employed for starting generators when a blackout or a local outage occurs. Considering the feature of the battery swapping mode, an available capacity model of the batteries in a centralised charging station is first developed. Then, the authors analyse the start-up characteristics of a generator powered by batteries and propose a bi-level optimisation-based network reconfiguration model to determine the restoration paths with an objective of maximising the overall generation capability. In the upper-level optimisation model, the generator start-up sequence is optimised, whereas the restoration paths are optimised in the lower-level one. Moreover, they consider the uncertainties associated with the available capacity of the batteries. The bi-level optimisation model for the network reconfiguration is developed in the chance-constrained programming framework and solved by the well-established particle swarm optimisation algorithm. Finally, case studies are employed to demonstrate the effectiveness of the presented model. Simulation results show that a centralised EV charging station could act as a power source to effectively restore a power system without black-start (BS) generators or with insufficient cranking power from BS generators, and the presented model could be used to guide actual system restorations.

关键词： power system restoration battery powered vehicles secondary cells power generation reliability particle swarm optimisation electric vehicles power system restoration blackout EV battery swapping mode starting generator start-up characteristics centralised charging station outage bilevel optimisation-based network reconfiguration upper-level optimisation model chance-constrained programming framework particle swarm optimisation algorithm black start generator BS generator

来源：评论

学校读者我要写书评

暂无评论

Feeder reconfiguration and capacitor allocation in the presence of non-linear loads using new P-PSO algorithm

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第10期10卷 2316-2326页

作者： Sayadi, Fahimeh Esmaeili, Saeid Keynia, Farshid Kerman Grad Univ Technol Dept Energy Kerman Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Grad Univ Adv Technol Dept Energy Kerman Iran

This study proposes a new particle swarm optimisation algorithm for combined problem of capacitor placement and network reconfiguration simultaneously in the presence of non-linear loads. Here, the minimising cost of real power losses and capacitor installation and also improving the power quality criteria have been pursued as the goals of an optimisation problem. In the proposed method, to achieve better control on the algorithm's exploration and exploitation capabilities, particles velocity will be dependent upon both particle's fitness and time. Perturbation module is adopted to perform perturbation on some particles and provide extra diversity to jump out from local optima and avoid premature convergence. The proposed model is implemented on two typical networks including 33-bus IEEE standard well-known test system and a 77-bus radial distribution network of Sirjan, Iran. Through showing numerical results, the performance of the presented method will be discussed in comparison to previously proposed ones. The numeric comparison also indicates that simultaneously capacitor placement and network reconfiguration lead to far better results than they are considered non-simultaneously. Furthermore, in regard to harmonic distortion, as a term of a multi-objective function, it also improves the power quality of the network during the reconfiguration and capacitor placement procedures.

关键词： particle swarm optimisation power distribution economics harmonic distortion power supply quality losses feeder reconfiguration capacitor allocation nonlinear loads P-PSO algorithm particle swarm optimisation algorithm capacitor placement network reconfiguration cost minimization power losses capacitor installation power quality criteria perturbation module 77-bus radial distribution network Sirjan Iran harmonic distortion multiobjective function

来源：评论

学校读者我要写书评

暂无评论

Generalised model predictive controller design for A DC-DC non-inverting buck-boost converter optimised with a novel identification technique

引用

IET POWER ELECTRONICS 2022年第13期15卷 1350-1364页

作者： Ghamari, Seyyed Morteza Khavari, Fatemeh Molaee, Hasan Wheeler, Patrick Shahrood Univ Technol Semnan Fac Elect & Comp Engn Semnan Iran Univ Nottingham Fac Engn Nottingham England

An on-line generalised model predictive control (GMPC) strategy is designed and optimised with a novel identification procedure in the presence of different disturbances. The principle of MPC is utilising a discrete-time model of a system to reach the control variables with a prediction over these values, which is followed by computing a cost function for the control aims. Non-inverting buck-boost converter is a non-minimum phase system based on its boost mode, which makes a challenging condition for designing a stable controller. The proposed control technique described in this paper removes the requirement for a system mathematical model adopting a black-box identification method which can decrease the computational burden. Numerous harmful disturbances can affect a DC-DC converter;thus, the GMPC scheme is used along with a novel improved exponential regressive least identification algorithm as an adaptive strategy for the controller to optimise the gains of the controller in an on-line way resulting in better disturbance rejection. A PID controller with particle swarm optimisation algorithm is designed for this converter to be compared with the GMPC controller. Finally, the efficiency of the GMPC is verified in various performances with experimental and simulation results.

关键词： generalised model predictive controller design controller design stability three-term control discrete-time model black-box identification method nonminimum phase system predictive control DC-DC noninverting buck-boost converter DC-DC power convertors control system synthesis particle swarm optimisation identification DC-DC converter GMPC controller design exponential regressive least identification algorithm power system control particle swarm optimisation algorithm system mathematical model PID controller regression analysis

来源：评论

学校读者我要写书评

暂无评论

Optimised operation of power sources of a PV/battery/hydrogen-powered hybrid charging station for electric and fuel cell vehicles

引用

IET RENEWABLE POWER GENERATION 2019年第16期13卷 3022-3032页

作者： Garcia-Trivino, Pablo Torreglosa, Juan P. Jurado, Francisco Fernandez Ramirez, Luis M. Univ Cadiz Dept Elect Engn Res Grp Elect Technol Sustainable & Renewable Ene EPS Algeciras Avda Ramon Puyol S-N Cadiz 11202 Spain Univ Huelva Elect Engn Dept Carretera Palos Huelva S-N Huelva 21071 Spain Univ Jaen Dept Elect Engn Res Grp Res & Elect Technol PAIDI TEP 152 EPS Linares Ave Univ Jaen 23700 Spain

This study presents a new energy management system (EMS) for the optimised operation of power sources of a hybrid charging station for electric vehicles and fuel cell vehicles. It is composed of a photovoltaic (PV) system, a battery and a hydrogen system as energy storage systems (ESSs), a grid connection, six fast charging units and a hydrogen supplier. The proposed EMS is designed to reduce the utilisation costs of the ESS and make them work, as much as possible, around their maximum efficiency points. The optimisation function depends on a cost prediction system that calculates the net present cost of the components from their previous performance and a fuzzy logic system designed for improving their efficiency. Finally, a particle swarm optimisation algorithm is used to solve the optimisation function and obtain the required power for each ESS. The proposed EMS is checked under Simulink environment for long-term simulations (25 years). By comparing the EMS with a simpler one that optimises only the costs, it is proved that the proposed EMS achieves better efficiency of the charging station (+7.35%) and a notable reduction in the loss of power supply probability (-57.32%) without compromising excessively its average utilisation cost (+1.81%).

关键词： electric vehicles particle swarm optimisation fuel cell vehicles energy storage battery powered vehicles power grids energy management systems optimisation distributed power generation battery storage plants hybrid power systems photovoltaic power systems energy storage systems local grid fast charging units rated power hydrogen supplier EMS utilisation costs ESS maximum efficiency points optimisation function cost prediction system net present cost fuzzy logic system particle swarm optimisation algorithm hybrid charging station power supply probability average utilisation cost optimised operation power sources cell vehicles energy management system electric vehicles photovoltaic system hydrogen system power 300 0 kW time 25 0 year

来源：评论

学校读者我要写书评

暂无评论

Optimal pricing and inventory strategies for leased equipment considering lessees' options

引用

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第19期62卷 7261-7278页

作者： Liu, Yanping Liu, Biyu Yang, Haidong Luo, Kai Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China Fuzhou Univ Logist Res Ctr Fujian Social Sci Res Base Fuzhou Peoples R China Paris Sch Business Paris France Fuzhou Univ Fuzhou Univ Town Sch Econ & Management 2 Wulong Jiangbei Ave Fuzhou Fujian Peoples R China

After the expiration of each lease, lessees may return leased equipment on schedule, renew the lease or purchase it according to the equipment status and their demands. By considering lessees' uncertain options and equipment status difference, the pricing and inventory decision-makings of leased equipment are explored. A mixed-integer nonlinear programming model by maximising the net present value of lessor's profit is presented with respect to constraints like rental revenue, manufacturing, lessees' credit check, transportation, maintenance, upgrade and inventory costs. The rental price and inventory decisions are obtained by solving the problem with a particle swarm optimisation algorithm. We also analyse the impacts of purchasing cost of old equipment from a third-party supplier on lessor's inventory, renewal price or purchasing price of leased equipment on lessees' options and lessor's profit. The results show: (1) with the extension of lease period, the rental price increases while the growth rate decreases;(2) the maintenance cost accounts for about 20% of total cost, and the preventive maintenance strategy can reduce excess maintenance cost as lease period increases;(3) the lessor shall set moderate renewal price discount coefficient and purchasing price coefficient, and analyse purchasing cost of old equipment to manage inventory timely.

关键词： Pricing and inventory leasing lessees' options preventive maintenance particle swarm optimisation algorithm

来源：评论

学校读者我要写书评

暂无评论

Soft computing applied to the build of textile defects inspection system

引用

IET COMPUTER VISION 2013年第5期7卷 373-381页

作者： Darwish, Saad Mohamed Univ Alexandria Dept Informat Technol Inst Grad Studies & Res Alexandria 21526 Egypt

The inspection of textile defects is challenging because of the large number of defects categories that are characterised by their imprecision and uncertainty. In this study, novel interval type-2 fuzzy system is proposed for resolving defects recognition problem of textile industries. The proposed system mixes interval type-2 fuzzy reasoning and swarm optimisation algorithm together in order to enhance the defects classification capabilities. Interval type-2 fuzzy logic is powerful in handling high level of indecisions in the human decision making process, including uncertainties in measurements of textile features and data used to calibrate the examination's parameters. swarm intelligence algorithm is used to optimise parameters of the membership functions to increase the accuracy of fuzzy controller. Besides, the problem of fuzzy linguistic rules learning has been tackled by utilising ant colony meta-heuristic method to reduce the complexity of the inspection system. Excellent recogniser results on real textile samples, using this system, are demonstrated.

关键词： decision making fuzzy logic fuzzy reasoning heuristic programming inspection learning (artificial intelligence) particle swarm optimisation production engineering computing textile industry ant colony optimisation soft computing textile defect inspection system defects recognition problem textile industries interval type-2 fuzzy reasoning system particle swarm optimisation algorithm defects classification capability interval type-2 fuzzy logic human decision making process examination parameter calibration swarm intelligence algorithm membership functions fuzzy controller fuzzy linguistic rules learning ant colony metaheuristic method

来源：评论

学校读者我要写书评

暂无评论

Pattern synthesis for multi-feed reflector antennas using invasive weed optimisation

引用

IET MICROWAVES ANTENNAS & PROPAGATION 2012年第14期6卷 1583-1589页

作者： Foudazi, A. Mallahzadeh, A. R. Shahed Univ Elect & Elect Engn Dept Tehran Iran

The authors present a new optimisation method for pattern synthesis of multi-feed reflector antennas in this study. By using an invasive weed optimisation (IWO) algorithm, shaped beam antennas are designed. The results of the IWO algorithm for the synthesis of cosecant squared and contoured beam patterns are presented and compared with particle swarm optimisation algorithm results. The far-field radiation patterns of reflector antennas, fed by linear and planar array of conical horn antennas, are provided by a physical optics and the error between desired and provided radiation patterns is calculated by a root mean square method. The results show that the IWO algorithm, which provides good convergence, is stable and adaptive-to-change for different boundary conditions in pattern synthesis applications.

关键词： conical horn antennas particle swarm optimisation optimisation techniques contoured beam pattern synthesis IWO algorithm horn antennas Antenna accessories planar antenna arrays physical optics particle swarm optimisation algorithm conical antennas far-field radiation patterns planar antenna array mean square error methods shaped beam antennas Antenna arrays invasive weed optimisation method antenna radiation patterns Interpolation and function approximation (numerical analysis) cosecant squared pattern synthesis root mean square method reflector antenna feeds linear antenna array multifeed reflector antennas linear antenna arrays

来源：评论

学校读者我要写书评

暂无评论

FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

引用

IET COMMUNICATIONS 2020年第12期14卷 1942-1948页

作者： Balaji Naik, Banavath Singh, Dhananjay Samaddar, Arun B. Natl Inst Technol Sikkim Dept Comp Sci & Engn Sikkim India HUFS Global Campus Dept Elect Engn Yongin South Korea Natl Inst Technol Sikkim Dept Comp Sci & Engn Sikkim India

Cloud computing and virtualisation are recent approaches to develop minimum energy usage in virtualised cloud data centre (DC) for resource management. One of the major problems faced by cloud DCs is energy consumption which increases the cost of cloud user and environmental influence. Therefore, virtual machine (VM) consolidation is properly proposed in many approaches which reallocate the VMs by VM migration with the objective of minimum energy consumption. Here, VM consolidation based on the Fruit fly Hybridised Cuckoo Search (FHCS) algorithm is proposed to obtain the optimal solution with the help of two objective functions in cloud DC. This FHCS approach efficiently minimises the energy usage and resource depletion in cloud DC. The proposed work comparison is done with Ant Colony System (ACS), particle swarm optimisation (PSO) algorithm and Genetic algorithm (GA). The simulation conclusion reveals the advantage of the FHCS and VM migration method over existing procedures such as GA, PSO and ACS in terms of energy consumption and resource utilisation. The proposed method achieves 68 Kwh less energy and 72% less resources than existing methods. Simulation results have shown that energy consumption of the proposed method is reduced with less number of active PMs than other conventional approaches.

关键词： computer centres cloud computing virtual machines particle swarm optimisation genetic algorithms virtualisation energy consumption search problems virtual machine migration task scheduling cloud data center minimum energy usage virtualised cloud data centre resource management cloud DC cloud user environmental influence virtual machine consolidation minimum energy consumption VM consolidation objective functions FHCS approach resource depletion particle swarm optimisation algorithm Genetic algorithm VM migration method resource utilisation hybridised optimisation Fruit fly Hybridised Cuckoo Search algorithm

来源：评论

学校读者我要写书评

暂无评论

Siting and sizing of electric vehicle fast-charging station based on quasi-dynamic traffic flow

引用

IET RENEWABLE POWER GENERATION 2020年第19期14卷 4204-4215页

作者： Qian, Zhang Yi, Zhu Zhong, Wang Yue, Hu Yaojia, Su Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se 174 Shazhengjie Chongqing Peoples R China State Grid Chengdu Power Supply Co 63Sect 4Renmin South Rd Chengdu Sichuan Peoples R China

In engineering practice, the Voronoi diagram is often used to plan electric vehicle (EV) fast-charging stations but it does not consider the spatio-temporal distribution of EV trip. Research on the spatio-temporal distribution of EVs mainly uses the shortest path method to reduce the amount of iterative calculations. The influence of the external environment and traffic congestion on the travel of EVs has not been considered, which results in the inaccuracy of the EV charging demand. To solve the above problems, this study proposes a method for predicting the spatio-temporal distribution of EVs based on quasi-dynamic traffic flow. This method takes into account the external environment and the impact of traffic congestions on EV trips and balances the problem between simulation accuracy and calculation efficiency. Based on this, the particle swarm optimisation algorithm is used to optimise the travel cost of the EVs and the cost of the construction and operation of the charging infrastructure. An optimal siting and sizing model for the fast-charging station based on quasi-dynamic traffic flow is established. Simulation results verify the effectiveness of the model.

关键词： particle swarm optimisation electric vehicles electric vehicle charging iterative methods computational geometry road traffic particle swarm optimisation algorithm iterative calculations Voronoi diagram sizing model optimal siting charging infrastructure EV charging demand shortest path method spatio-temporal distribution electric vehicle fast-charging stations quasidynamic traffic flow

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

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

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：