检索结果-内蒙古大学图书馆

2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS2017)

作者： HaoZhang HongjuanLi MinXu State Grid Jiangxi Electric Power Company Shangrao Power Supply Branch School of Information Engineering Nanchang University

The access of a large number of renewable energy makes the active distribution network become the inevitable trend of development, coordination of renewable distributed power and traditional reactive power compensation device can effectively achieve the active distribution network reactive power optimization. The simulation results of IEEE33 node system show that the mathematical model is correct and accurate for the reactive power optimization of the distribution network, and the particle swarm optimization algorithm can effectively search the global optimal.

关键词： active distribution network mathematical model particle swarm algorithm simulation

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation Schemes for Distribution Networks Under Source-Load Uncertainty

Resource Allocation Schemes for Distribution Networks Under ...

引用

2024 International Conference on Innovative Design and Intelligent Manufacturing, IDIM 2024

作者： Ye, Gaoxiang Yang, Jie Shao, Feifan Xia, Fangzhou Yang, Zili Zheng, Zijian Peng, Wenyan State Grid Hubei Electric Power Company Limited Economic & Technical Research Institute Wuhan430200 China

ISBN: (纸本)9781643685427

A multi-objective optimal allocation model is established by constructing probabilistic models of photovoltaic, wind turbine output, load and electric vehicle charging power. The model takes into account the economy of investment, network stability and social and environmental benefits. In order to solve this model, an improved multi-objective particle swarm algorithm is proposed, and the probabilistic trend calculation is carried out using the semi-invariant method to verify the constraints. The efficiency and effectiveness of the proposed method are verified through simulation, especially in terms of computation time and planning efficiency. © 2024 The Authors.

关键词： Distributed power supply Multi-objective optimization particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Active Power Distribution Hierarchical Energy Optimal Management of IoT Smart Power System

Active Power Distribution Hierarchical Energy Optimal Manage...

引用

2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Yin, Xiang State Grid XinJiang Electric Power Co. Ltd Urumqi361024 China

ISBN: (纸本)9798350315455

With the development of modern power systems, the research on energy optimization management at the distribution level has also become a hot topic in recent years. In the microgrid system, two safety indicators, load power shortage rate and energy waste rate, are considered and incorporated into the model as constraints. The proposed energy optimization management method is verified by Python programming. To verify the advantages of the particle swarm optimization algorithm used in this paper in energy optimization management, the method in this paper is compared with the traditional real-time one-step optimization method. In this paper, the particle swarm algorithm is used, and the average node voltage deviation is about 0.01pu. The overall voltage evaluation index obtained is 25.86% lower than that of one-step optimization, and the overall optimization effect of reactive power voltage is better. © 2023 IEEE.

关键词： hierarchical energy optimization internet of things particle swarm algorithm smart power system

来源：评论

学校读者我要写书评

暂无评论

Analysis of Inclination Mechanism and Trajectory Prediction of Deep-Hole Small-Diameter Scientific Drilling Holes

Analysis of Inclination Mechanism and Trajectory Prediction ...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Xu, Guo-Hui Liu, Hai-Sheng Mu, Yuan-Hong Qinghai Second Geological Exploration Institute Xining810028 China Qinghai Core Drilling Engineering Technology Research Center Xining810028 China

ISBN: (纸本)9798350383348

In many fields, such as geological exploration, mining and oil exploitation, the prediction of borehole inclination angle and trajectory is a key problem. Accurate prediction is not only helpful to improve work efficiency, but also can reduce various risks caused by borehole deviation. This paper mainly analyzes the inclination mechanism of borehole and discusses the prediction method of borehole trajectory. © 2024 IEEE.

关键词： Deep hole evolutionary computational theory Mechanism analysis particle swarm algorithm Small caliber Trajectory prediction

来源：评论

学校读者我要写书评

暂无评论

particle swarm Optimization Based on Gaussian Mutation and Its Application to Wind Farm Micro-siting

Particle Swarm Optimization Based on Gaussian Mutation and I...

引用

2010 49th IEEE Conference on Decision and Control

作者： Chunqiu Wan Jun Wang Geng Yang Xing Zhang Department of Automation Tsinghua University Beijing 100084 P. R. China Department of Control Science and Engineering Tongji University Shanghai 201804 P. R. China School of Aerospace Tsinghua University Beijing 100084 P. R. China

ISBN: (纸本)9781424477456

In this paper, a particle swarm optimization algorithm with Gaussian mutations, denoted by GPSO, is proposed to solve constrained optimization problems. Two Gaussian mutation operators are employed to search the promising regions for better solutions. One operator is for the region between the personal best position and the global best one. The other operator is for the region around the global best position. The Gaussian mutations help the population jump out of local optima and find better solutions with more probability. The feasibility-based method compares the performance of different particles. Evaluated by three typical optimization problems, GPSO is more accurate, robust and efficient for locating global optima. The GPSO method is applied to a wind-farm micrositing problem. Simulation results demonstrate that the power generation of the wind farm is further improved while the execution time is substantially reduced.

关键词： Wind farms particle swarm optimization Gauss execution time Power production micro Wind Mutation particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Annual Runoff Forecast Based on Cooperative particle swarm Projection Pursuit Regression Model

Annual Runoff Forecast Based on Cooperative Particle Swarm P...

引用

International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Xinxin Li Jing Xu College of Computer and Information Engineering Xinjiang Agricultural University Urumqi China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

According to the high-dimensional nonlinear problem of annual runoff prediction, to build runoff forecasting model based on projection pursuit regression model of Hermite polynomials and the cooperative particle swarm optimization algorithm. Projection pursuit prediction model projects high-dimensional data into low-dimensional space based on sample data driving, completely according to the sample data driven to enhance the prediction results objectivity. The particle swarm optimization algorithm combines the idea of co-evolution to optimize the projection direction and polynomial coefficients in parallel, and further improve the convergence rate and prediction accuracy of the model. The model is applied to the flow prediction of Jiubujiang River Reservoir. The relative error of runoff prediction is less than 15%, and the prediction result is high precision and reliability. The experimental results show that it is feasible and effective to use the cooperative particle swarm projection pursuit regression model to predict the annual runoff.

关键词： projection pursuit regression model co-evolution algorithm particle swarm algorithm Hermite polynomial annual runoff prediction Hermite polynomials particle swarm algorithm Annual runoff Projection Pursuit Regression particle swarm optimization evolutionary algorithm Data sampling data driven

来源：评论

学校读者我要写书评

暂无评论

A hybrid discrete particle swarm optimization for Job shop Scheduling

A hybrid discrete particle swarm optimization for Job shop S...

引用

International Conference on Computational Aspects of Social Networks(国际社会网络计算会议 CASoN 2010)

作者： Wanliang Wang Jing Zhang Haiyan Wang Xinli Xu Jing Jie College of Computer Science and Technology Zhejiang University of Technology JUT Hangzhou P.R.Chin College of Information Engineering Zhejiang University of Technology ZJUT Hangzhou P.R.China College of Computer Science and Technology Zhejiang University of Technology JUT Hangzhou P.R.Chin

The job shop scheduling problem (JSSP) is a well known NP-hard problem, and many algorithms have been presented to solve it, but the results are still unsatisfactory. In this paper, a hybrid discrete particle swarm optimization algorithm based on a two layer population structure is proposed to solve the JSSP, meanwhile add an improved simulated annealing algorithm to increase the ability of finding the global optimum solutions. The experimental results illustrate the high effectiveness of the proposed method, which can avoid prematurity efficiently and be more robust than the PSO and DPSO.

关键词： component particle swarm algorithm job shop scheduling makespan simulated annealing algorithm

来源：评论

学校读者我要写书评

暂无评论

Network Repair Strategy Based on the Failure of Airline

Network Repair Strategy Based on the Failure of Airline

引用

2016 4th International Conference on Electrical&Electronics Engineering and Computer Science(ICEEECS 2016)

作者： Zhang Yuxiang Wu Minggong Wang Xiaorong Wen Xiangxi College of Air Traffic Control and Navigation Air Force Engineering University

ISBN: (纸本)9781510835429

Contraposing to the network repair problem of airline network after edge failure, an optimization model was proposed based on network repair study and hub airline network theory. The model regarded the best performance under certain cost as the objective function. And it satisfied different requirements by giving different weights. The model considerated the cost caused by distance and congestion. The network efficiency was selected as measurement for performance. The model was solved by improved particle swarm optimization. At last, taking the airline between Hang zhou and Xian yang as an example, a simulation was conducted to verify the model. The experimental result shows that capacity decision coefficient can control the cost well caused by congestion through changing the node capacity, which can affect the allocation method of traffic and has an influential effect on network survivability. What's more, the model can well adjust the balance between cost and network performance. The model has a good application prospect.

关键词： Airline network Network repair Capacitated constraint Network optimization particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Calculating method of optimal path of data of traffic network

Calculating method of optimal path of data of traffic networ...

引用

2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2015)

作者： Sun- Hongfeng Li-Ying Yang-Yanchun Information Technology School Shandong Women's University

ISBN: (纸本)9781510806481

According to the influence of traffic flow change on the selection of vehicle routing, to find a solution to the planning of travel routes in the situation that knowing the change of traffic flow. By using the method of particle swarm algorithm combined with dynamic programming to optimize the path of the vehicle, we can get the excellent route of each vehicle under the influence of the traffic flow. Simulating based on the road network structure and traffic data in real environment, the results showing that this method can increase the authenticity and dynamic of path optimization, and the road traffic flow and vehicle travel time having important influence on vehicle routing choice.

关键词： Traffic flow particle swarm algorithm Dynamic programming The optimization of path Time dependent

来源：评论

学校读者我要写书评

暂无评论

particle swarm Optimization as an Efficient Computational Method in order to Minimize Vibrations of Multimesh Gears Transmission

引用

ADVANCES IN ACOUSTICS AND VIBRATION 2011年第unknown期2011卷 195642-1-195642-6页

作者： Carbonelli, Alexandre Perret-Liaudet, Joel Rigaud, Emmanuel Le Bot, Alain Univ Lyon Ecole Cent Lyon Lab Tribol & Dynam Syst UMR CNRS 5513 36 Ave Guy Collongue F-69134 Ecully France

The aim of this work is to present the great performance of the numerical algorithm of particle swarm Optimization applied to find the best teeth modifications for multimesh helical gears, which are crucial for the static transmission error (STE). Indeed, STE fluctuation is the main source of vibrations and noise radiated by the geared transmission system. The microgeometrical parameters studied for each toothed wheel are the crowning, tip reliefs and start diameters for these reliefs. Minimization of added up STE amplitudes on the idler gear of a three-gear cascade is then performed using the particle swarm Optimization. Finally, robustness of the solutions towards manufacturing errors and applied torque is analyzed by the particle swarm algorithm to access to the deterioration capacity of the tested solution.

关键词： computational methods Transmission systems order Static transmission error particle swarm optimization manufacturing error Gears Helical gears particle swarm 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：