检索结果-内蒙古大学图书馆

Solar Concentrator Consisting of Multiple Aspheric Reflectors

ENERGIES 2019年第21期12卷 4038-4038页

作者： Cao, Guobin Qin, Hua Ramachandran, Rajan Liu, Bo Beijing Railway Century Soil Engn Co LTD Yangfangdian Rd Beijing 100038 Peoples R China Shandong Univ Technol Sch Phys & Optoelect Engn Lab Funct Mol & Mat Zibo 255000 Shandong Peoples R China

This paper presents an off-axis-focused solar concentrator system consisting of 190 aspheric reflectors, where the aperture radius of each reflector is 10 cm, and vertices of all reflectors are orderly arranged in the same plane. The aspheric parameters controlling the curvature of the reflectors are determined using coordinate transformations and the particle swarm optimization (pso) algorithm. Based on these aspheric parameters, the light distribution of focal plane was calculated by the ray tracing method. The analyses show that the designed concentrator system has a spot radius of less than 1 cm and the concentration ratio over 3300:1 is achieved using only one reflection. The design results have been verified with the optical design software Zemax.

关键词： concentrating solar power system aspheric reflector off-axis point focusing pso algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of swarm optimisation-based modified algorithm for selective harmonic elimination in reduced switch count multilevel inverter

引用

IET POWER ELECTRONICS 2018年第8期11卷 1472-1482页

作者： Panda, Kaibalya Prasad Panda, Gayadhar Natl Inst Technol Meghalaya Dept Elect Engn Bijni Complex Shillong Meghalaya India

Multilevel inverters (MLIs) are nowadays extensively used in integration with renewable energy sources and in drives applications. The converter cost and voltage quality improvement are the utmost importance in such application of MLI. A six-switch seven-level reduced switch symmetrical MLI (6S-7L MLI) is proposed here. Compared to the conventional and similar existing MLI topologies, the proposed MLI uses less active switches and has less driver circuit requirement. A particle swarm optimisation (pso)-based modified selected harmonic elimination technique is derived and analysed for computing optimal switching angles of proposed 6S-7L MLI to eliminate third- and fifth-order harmonics. Moreover, the performance of the proposed algorithm is compared with the two most commonly used pso variants. The analysis shows the modified version of pso is most suitable for optimising the output voltage of proposed MLI through targeted harmonic elimination. The proposed topology is investigated through simulation by applying the calculated switching angles using modified pso algorithm. Finally, a single-phase experimental prototype is designed to verify the validity of the proposed structure.

关键词： invertors renewable energy sources harmonics suppression particle swarm optimisation network topology switching convertors harmonic distortion power supply quality pso algorithm selective harmonic elimination reduced switch count multilevel inverter renewable energy sources voltage quality improvement active switches driver circuit requirement particle swarm optimisation harmonic elimination technique optimal switching angles fifth-order harmonics switching angles swarm optimisation-based modified algorithm seven-level reduced switch symmetrical MLI topology

来源：评论

学校读者我要写书评

暂无评论

Optimization for the Following Operation of a High-Speed Train Under the Moving Block System

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2018年第10期19卷 3406-3413页

作者： Liu, Hong-En Yang, Hui Cai, Bai-Gen East China Jiaotong Univ Key Lab Adv Control & Optimizat Jiangxi Prov Nanchang 330013 Jiangxi Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

Efficient and safe following operation of a high-speed train (HST) under the moving block system (MBS) has been the trend for high-speed railway (HSR) transportation. However, due to the complex operating environment and changeable operation state of HST during the following operation process, it becomes inapplicable for conventional train control approaches to satisfy HST's multi-objective operation demand including safety, punctuality, energy efficiency, and ride comfort. To resolve this problem, in this paper, practical models for the real-world characteristics of HSR's line parameters and HST's following control, as well as the multi-objective optimization model with some novel metrics, are established for achieving the optimal following control of HST under MRS. Besides, the multi-objective particle swarm optimization algorithm is modified with the preference information of control sensitivity and energy efficiency, so as to efficiently obtain the optimal following control strategy based on the real-time data of HST like velocity, location, and speed restriction. Furthermore, the convergence performance of our proposed method is demonstrated by comparative tests which also help select the proper algorithm parameters. Finally, the efficiency and feasibility of the proposed framework are illustrated by showing some experimental results.

关键词： High-speed train moving block system following operation optimization convergence performance pso algorithm

来源：评论

学校读者我要写书评

暂无评论

Anti-windup scheme based on 2DOF-PI^λD^μ controller for velocity tracking of linear induction motor

引用

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第12期29卷

作者： Saleh, Ameer L. Obed, Adel A. Al-Yasir, Yasir I. A. Elfergani, Issa T. E. Rodriguez, Jonathan Clarke, Roger W. Abd-Alhameed, Raed A. Univ Misan Coll Engn Dept Elect Engn Misan Iraq Middle Tech Univ Elect Engn Tech Coll Baghdad Iraq Univ Bradford Sch Elect Engn & Comp Sci Bradford BD7 1DP W Yorkshire England Inst Telecomunicacoes Mobile Syst Grp Aveiro Portugal

Recently, a new scheme of conventional PID (Proportional Integral Derivative) controller has been utilized in different electrical drives for speed control. This paper presents an optimal P (ID mu)-D-lambda controller with 2DOF and anti-windup techniques based on pso optimization to produce the proposed controller which is exploited in order to control the velocity control of LIM. A three-phase inverter with SVPWM technique that is depended on approach of IFOC method is exploited to obtain the necessary output voltage supplied to the main windings of the LIM in order to control the velocity with minimum harmonics and good characteristics. The end effect is taken into account in dq axis equivalent circuit. Computed investigations show that the proposed controller is efficient in improving motor performance.

关键词： 2DOF-P I(lambda)D(mu)anti-windup IFOC linear induction motor modeling and simulation pso algorithm SVPWM velocity control

来源：评论

学校读者我要写书评

暂无评论

A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation

引用

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 526卷 121073-000页

作者： Shen, Feng Zhao, Xingchao Li, Zhiyong Li, Ke Meng, Zhiyi Southwestern Univ Finance & Econ Sch Finance Chengdu 611130 Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Stat Chengdu 611130 Sichuan Peoples R China Sichuan Univ Sch Business Chengdu 611130 Sichuan Peoples R China

Significant research has been performed on credit risk evaluation, with many machine learning and data mining techniques being employed for financial decision-making. The back propagation (BP) neural network has been a popular choice for credit risk evaluation problems, but many studies have found classifier ensembles to be superior to single classifiers. In this paper, a novel ensemble model based on the synthetic minority over-sampling technique (SMOTE) and a classifier optimisation technique is proposed for personal credit risk evaluation. To mitigate the negative effects of imbalanced datasets on the performance of the credit evaluation model, the SMOTE technique is used to rebalance the target training dataset. The particle swarm optimisation (pso) algorithm is employed to search for the best-connected weights and deviations in the BP neural networks. Based on the optimised BP neural network classifiers, an ensemble model is developed that combines the AdaBoost approach with the base classifiers. To ensure that the proposed model provides accurate and stable performance, we thoroughly explore and discuss the optimal parameters for the ensemble classification model. Finally, the proposed ensemble model is tested on German and Australian real-world imbalanced datasets. The results demonstrate that this model is more effective at processing credit data problems compared to the other classification models examined in this study. (C) 2019 Elsevier B.V. All rights reserved.

关键词： BP neural network Credit risk evaluation Ensemble classification model pso algorithm SMOTE

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Particle Swarm Optimization algorithm Based on Aggregation Degree

引用

RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2018年第4期11卷 443-448页

作者： Zhang, Xiuli Zhang, Ruihua Wang, Jianping Wang, Laidi Zhejiang Wanli Univ Coll Elect & Informat Engn Ningbo 315100 Zhejiang Peoples R China Ningbo Acad Oceanol & Fishery Ningbo 315010 Zhejiang Peoples R China

Objective: In view of the issue that particle swarm optimization algorithm is easy to fall into local optimal solution and low convergence precision when searching for the feasible space solution of complex multimodal functions, the paper proposes an adaptive particle swarm optimization algorithm (pso) based on aggregation degree. Methods: The algorithm firstly makes a random disturbance to search global optimal solution through differential evolution method according to the size of population aggregation to increase the diversity of particle swarm and improve its global searching ability. Then the adaptive adjustment is made to the inertia weight parameter of particles in accordance with the aggregation degree of particles. Conclusion: The simulation example of typical optimization problems suggests that this algorithm can improve the population diversity, particle activity, convergence performance and searching ability of pso.

关键词： Particle swarm optimization aggregation degree differential evolution inertia weight adaptive adjustment pso algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimization of single and multi-areas economic dispatch problems based on evolutionary particle swarm optimization algorithm

引用

ENERGY 2018年 161卷 710-724页

作者： Mohammadian, M. Lorestani, A. Ardehali, M. M. Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Energy Syst LabCtr Excellence Power Syst Tehran Iran

Economic dispatch (ED) is a non-convex, non-linear, and non-smooth optimization problem that determines the optimal output power of generation units to meet the forecasted demand from an economic point of view. The objective of this study is to develop and examine the applicability of a newly developed evolutionary particle swarm optimization (E-pso) algorithm for optimization of the ED problem, where practical constraints, namely, valve-point effects, prohibited operating zones, multiple fuel usage, dynamic ramp rate limits, transmission losses, tie-line capacity, and spinning reserve are considered. In the developed E-pso algorithm, three operators including mutation, crossover, and selection are applied to enable the search process to skip local optimal points and enhance computational efficiency. To further enhance the performance of the algorithm, an approach is proposed to dynamically adjust the inertia, cognitive, and social weight coefficients to improve exploration and exploitation for smooth convergence. Upon validation of the E-pso algorithm by means of standard benchmark functions, four case studies including isolated and interconnected power systems are examined and the results are compared with those from other algorithms. The findings show that the proposed features enable the E-pso algorithm to successfully optimize the ED problem in lower simulation time, while all constraints are met. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch Optimization pso algorithm Power generation Operation constraints

来源：评论

学校读者我要写书评

暂无评论

SURFACE-TO-AIR MISSILE PATH PLANNING USING GENETIC AND pso algorithmS

引用

JOURNAL OF THEORETICAL AND APPLIED MECHANICS 2017年第3期55卷 801-812页

作者： Zandavi, Seid Miad Payam Noor Univ Dept Engn Tehran Iran

Optimization algorithms use various mathematical and logical methods to find optimal points. Given the complexity of models and design levels, this paper proposes a heuristic optimization model for surface-to-air missile path planning in order to achieve the maximum range and optimal height based on 3DOF simulation. The proposed optimization model involves design variables based on the pitch programming and initial pitch angle (boost angle). In this optimization model, we used genetic and particle swarm optimization (pso) algorithms. Simulation results indicated that the genetic algorithm was closer to reality but took longer computation time. pso algorithm offered acceptable results and shorter computation time, so it was found to be more efficient in the surface-to-air missile path planning.

关键词： path planning genetic algorithm pso algorithm surface-to-air missile 3DOF simulation

来源：评论

学校读者我要写书评

暂无评论

A Quantum Particle Swarm Optimization algorithm Based on Self-Updating Mechanism

引用

INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2018年第1期9卷 1-19页

作者： Wu, Shuyue Hunan Int Econ Univ Sch Informat Sci & Engn Field Elect Commun Sci Res Changsha Hunan Peoples R China Hunan Int Econ Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

The living mechanism has limited life in nature;it will age and die with time. This article describes that during the progressive process, the aging mechanism is very important to keep a swarm diverse. In the quantum behavior particle swarm (Qpso) algorithm, the particles are aged and the algorithm is prematurely convergent, the self-renewal mechanism of life is introduced into Qpso algorithm, and a leading particle and challengers are introduced. When the population particles are aged and the leading power of leading particle is exhausted, a challenger particle becomes the new leader particle through the competition update mechanism, group evolution is completed and the group diversity is maintained, and the global convergence of the algorithm is proven. Next in the article, twelve Clement2009 benchmark functions are used in the experimental test, both the comparison and analysis of results of the proposed method and classical improved Qpso algorithms are given, and the simulation results show strong global finding ability of the proposed algorithm. Especially in the seven multi-model test functions, the comprehensive performance is optimal.

关键词： Aging Diversity Global Convergence pso algorithm Quantum Behaved Self-Renewal Mechanism

来源：评论

学校读者我要写书评

暂无评论

A Heuristic Elastic Particle Swarm Optimization algorithm for Robot Path Planning

引用

INFORMATION 2019年第3期10卷 99页

作者： Wang, Haiyan Zhou, Zhiyu Zhejiang Police Vocat Acad Dept Secur Hangzhou 310018 Zhejiang Peoples R China Zhejiang Sci Tech Univ Dept Comp Hangzhou 310018 Zhejiang Peoples R China

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

关键词： path planning pso algorithm A* algorithm elastic strategy

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：