检索结果-内蒙古大学图书馆

A Method for Analyzing Boundary Conditions of Typical Power Supply Structure in AC/DC Hybrid Distribution Network with Large-scale Photovoltaic Access

A Method for Analyzing Boundary Conditions of Typical Power ...

引用

IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Li, Wei Bai, Hao Yang, Weichen Fu, Yu Cai, Yongxiang Ye, Yuanhong Tang, Heng South Power Grid Co Ltd Inst Power Distribut Elect Power Res Inst Guangzhou Peoples R China Guizhou Power Grid Co Ltd Elect Power Res Inst Guiyang Peoples R China

ISBN: (纸本)9798350339345

In the situation of high proportion of distributed energy access, AC/DC hybrid distribution network has lower network loss and more flexible regulation ability than traditional AC distribution network. In order to provide relatively accurate guidance for AC/DC construction of distribution area, it is necessary to analyze its characteristic index boundary conditions. In this study, under the background of high percentage of photovoltaic access, the operation economy, photovoltaic consumption, power supply quality and other evaluation indexes of AC/DC hybrid distribution network were comprehensively considered, and the intelligent optimization algorithm was used to regulate the flexible equipment, and the boundary conditions of three characteristic indexes of photovoltaic permeability, power supply radius and load capacity were analyzed.

关键词： AC/DC distribution network optimization algorithm voltage source converter boundary condition

来源：评论

学校读者我要写书评

暂无评论

A novel probabilistic approach to the plastic analysis of steel moment frames

引用

Asian Journal of Civil Engineering 2023年第4期24卷 1001-1014页

作者： Abdulwahed, Larah R. Middle Technical University Technical Institute of Baquba Diyala Iraq

The limit plastic analysis procedure based on the basic mechanism determination is a classic procedure for fast analysis of structures, which results in determining the critical collapse mechanism of structures with acceptable precision and without prior information on structural details. However, this procedure loses efficiency in high-rise structures since it becomes a labor-intensive and almost impossible assignment. With the aid of optimization algorithms, this defect can be obviated. This paper implemented a novel approach to the probabilistic assessment of plastic analysis theory. Besides, two popular optimization algorithms, the dolphin echolocation, and the bat algorithm were employed for the optimization process of the plastic analysis method. The chaos was implemented in DE algorithms to have better global convergence. The first- and second-order reliability methods (FORM and SORM) were employed to formulate the limit state function in the reliability analysis. The limit state function was defined based on the critical load factor of two sample frames. Within the classic plastic analysis, the dolphin echolocation exhibited a 100% convergence reliability to the actual response in both sample frames, while the bat algorithm had 90% and 80% of reliability for samples one and two, respectively. Overall, both algorithms predicted the critical collapse mode of sample frames accurately. The FORM analysis revealed the approximate reliability index of two for both frames. Sensitivity measures based on the probabilistic approach also determined the most influential random variables in the probability of failure in both sample frames. © 2022, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

关键词： Bat Dolphin echolocation optimization algorithm Plastic analysis Reliability analysis

来源：评论

学校读者我要写书评

暂无评论

optimization of engineering parameters of deflagration fracturing in shale reservoirs based on hybrid proxy model

GEOENERGY SCIENCE AND ENGINEERING

引用

GEOENERGY SCIENCE AND ENGINEERING 2023年 231卷

作者： Yang, Xin Guo, Tiankui Yu, Mengqi Chen, Ming Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China China Univ Petr Coll Petr Engn Qingdao 266580 Peoples R China

Engineering parameter optimization of deflagration fracturing in shale reservoirs is operated with single factor analysis and orthogonal experimental design analysis. There are problems of the difficulty to search the global optimal solution, time-consuming optimization and low computation efficiency. To resolve these problems, a method of optimizing engineering parameters of deflagration fracturing based on the hybrid proxy model was proposed in this paper. The parameters with significant effects on deflagration fracturing were selected as var-iables, and the models for prediction of reservoir burst degree and stimulated area were established based on Radial Basis Function model, Kriging interpolation model and eXtreme Gradient Boosting regression model respectively. A hybrid proxy model based on the roulette method was established by integrating the priorities of these three models. Then, a multi-objective optimization method of deflagration fracturing based on the hybrid proxy model and was established by integrating the Nondominated Sorting Genetic algorithm II with the en-gineering parameters as variables and the maximum stimulated area and minimum burst degree as objectives. Optimal design of the deflagration fracturing scheme was achieved. The results show that the hybrid proxy model can accurately predict the evaluation index of the fracturing results in vertical and horizontal wells, and the prediction accuracy is maintained at about 0.9. At the same time, compared with numerical model, the proposed method greatly saves the calculation time. The method proposed by this paper provides the solution to the problem of multi-objective optimization of deflagration fracturing engineering parameters, weighs the reservoir failure degree and the stimulation range, and provides guidance for field operation.

关键词： Deflagration fracturing Hybrid proxy model Engineering parameter optimization algorithm Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Bio-Inspired optimization-Based Path Planning algorithms in Unmanned Aerial Vehicles: A Survey

引用

SENSORS 2023年第6期23卷 3051-3051页

作者： Poudel, Sabitri Arafat, Muhammad Yeasir Moh, Sangman Chosun Univ Dept Comp Engn 309 Pilmun daero Gwangju 61452 South Korea

Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

关键词： bio-inspired algorithm optimization algorithm path planning unmanned aerial vehicle UAV communication UAV path planning

来源：评论

学校读者我要写书评

暂无评论

FiWi network planning for WiFi enabled gram panchayats of India: A frame work using component placement optimization

引用

OPTICAL FIBER TECHNOLOGY 2023年第1期76卷

作者： Chouhan, Nitin Bhatt, Uma Rathore Upadhyay, Raksha Bhat, Vijay Devi Ahilya Univ Inst Engn & Technol Dept Elect & Telecommun Engn Indore 452020 India SAGE Univ Inst Adv Comp Indore India

Fiber Wireless (FiWi) access network consists of high speed passive optical network (PON) at back end and wireless mesh network at front end. It is emerging as the most prominent access network due to its inherent feature. It may provide fast Internet services at minimum possible cost. Many of the villages of the India are still not connected with high speed network hence the present paper provides FiWi network planning for creating the Wireless Fidelity (WiFi) enabled villages (Gram Panchayats) of India. The main aim of the paper is to deploy the cost effective FiWi network to provide the faster internet services at reasonable prices to the users of the gram panchayat (GP) in India. The area of the GP and the user's location in particular GP is taken as input for network planning. The proposed planning provides the optimized position of wireless routers and Optical Network Units (ONUs) in terms of latitude and longitude. For optimizing position of wireless router and ONUs, we propose to use customized Slime Mould algorithm (SMA). We considered four gram panchayats of Indore region of India to make them WiFi enabled using FiWi access network. The customized SMA algorithm provides the best possible position and number of wireless routers and ONUs to connect all the users of gram panchayat. Beside, the total fiber length from the Optical Line Terminal (OLT) to all the ONUs and total cost estimation is also given to create the complete network.

关键词： Network Planning FiWi access network WiFi enabled Gram Panchayat (GP) Component optimization optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction

引用

ENERGY 2023年第PartA期262卷

作者： Wang, Hao Ye, Jingzhen Huang, Linxuan Wang, Qiang Zhang, Haohua Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou 341000 Peoples R China

Offshore wind power prediction is the basis for safe operation and grid dispatch. However, it is difficult due to the high volatility. Aiming at the three shortcomings of current methods: lack of analysis of the impact of multiple variables;the reconstruction method of decomposition components often adopts the summation method;the traditional machine learning prediction methods are not accurate enough, while the deep learning methods are prone to overfitting. This paper proposes a multi-variable hybrid prediction model based on multi-stage optimization and reconstruction prediction. Firstly, the isolated forest is used for data preprocessing. Secondly, the power sequence is decomposed by the variational modal decomposition optimized by the gray wolf algorithm to reduce the non-stationarity. Thirdly, the kernel extreme learning machine optimized by sparrow algorithm is used to predict. Finally, the reconstruction prediction is carried out through the long short-term memory network. Compared with the traditional machine learning method and the deep learning method, the model is effectively improved on two European offshore datasets. Then the interval prediction based on this model further verifies the accuracy and reliability.

关键词： Offshore wind power Decomposition and reconstruction model optimization algorithm Interval prediction

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization and improvement of multi-energy combined cooling, heating and power system based on system simplification

引用

RENEWABLE ENERGY 2023年第1期217卷

作者： Zhao, Xiangming Guo, Jianxiang He, Maogang Xi An Jiao Tong Univ Sch Energy & Power Engn MOE Key Lab Thermofluid Sci & Engn Xian 710049 Peoples R China Qingdao Univ Technol Qingdao Qingdao 266000 Peoples R China

The multi-objective optimization of combined cooling, heating, and power (CCHP) systems typically focuses on optimizing objective functions that consider energy, environmental, and economic factors. However, the implementation of optimization solutions, which is related to decision variables, is often overlooked. This paper proposes a novel improved optimization method based on system simplification by reorganizing, re-optimizing and re-selecting the initial optimized population to improve decision variables. And created algorithm pseudocode. The improved algorithm is adopted to perform multi-objective optimization on the established multi-energy CCHP system. The hypervolume of the improved/original algorithm is 0.9770/0.9887, with only a 1.18% difference. The optimization results of the improved algorithm and the original algorithm are equal in the minimum values of the net present value (NPV) and fossil energy consumption (FEC), which are 2.26 x 10(7)$ and 1.21 x 10(5)GJ, respectively. There is only 1.4% difference in the carbon dioxide emissions (CDE). The number of equipment types corresponding to the decision variables of the improved algorithm is reduced from 8 to 4, significantly simplifying the systems. The proposed improved optimization method can harness the local energy-saving potential and comprehensively analyze optimization solutions in terms of energy, economy, and environment. It also obtains simplified systems to promote the implementation of the project.

关键词： System simplification Multi-objective optimization and improvement Combined cooling Heating and power system optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Route Planning and Power Management for Hybrid UAV Using A* algorithm

Optimal Route Planning and Power Management for Hybrid UAV U...

引用

AIAA Aviation Forum

作者： Jadischke, Jacob H. Wolff, Mitch Zumberge, Jon Hencey, Brandon Ngo, Anhtuan Wright State Univ Dayton OH 45435 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

For the practical application of a gas-electric hybrid UAV, algorithms are needed to synthesize joint motion and power plans that satisfy path and energy constraints. Specifically, this paper considers the problem of finding an energy-optimal flight path toward a destination under the flight path constraints and battery-only operational constraints. Standard techniques such as reinforcement learning, or Dynamic Programming are computationally expensive. This paper investigates the performance of the A* algorithm on this problem to reduce the computational complexity and, by extension, the computational time. By constructing the objective as the minimization of unrecoverable energy expenditures, the A* algorithm, Dijkstra's algorithm, and a proposed iterative A* solution, were successfully implemented for this problem. The developed optimal control policies were observed to satisfy regions with battery-only restrictions and match the total route cost through comparison to the Dynamic Programming solution of the same environment. Results show that the use of the A* algorithm and Dijkstra's algorithm obtain an optimal solution of 1.64-8.85 times faster than the optimal solution obtained through Dynamic Programming.

关键词： Unmanned Aerial Vehicle Optimal Control Problem Dijkstra's algorithm Reinforcement Learning Energy Consumption Electric Power System Closed Loop optimization algorithm Dynamic Programming algorithm Mixed Integer Linear Programming

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Component Cost Models for Power-Electronic Converters 25

Data-Driven Component Cost Models for Power-Electronic Conve...

引用

25th European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Fronczek, Carsten Fritz, Niklas De Doncker, Rik W. Rhein Westfal TH Aachen Inst Power Elect & Elect Drives Campus Blvd 89 Aachen Germany

ISBN: (纸本)9789075815412

Multi-objective optimization significantly shortens the design time for converters and improves initial design decisions. In addition to technical objectives, economic aspects such as cost play a role in almost every application. This paper provides cost models for converter components based on publicly available data from spring 2023. These cost models are based on the largest dataset of any converter-component cost model in the literature. Besides physical properties of the components, they include economic factors and allow their application from prototype to series production. The cost models were evaluated using statistical metrics. Further, the derived component cost models serve as a basis to a simplified design optimization for a 10 kW dc-dc converter.

关键词： Artificial intelligence Cost analysis Data analysis Design optimization optimization algorithm Buck converter

来源：评论

学校读者我要写书评

暂无评论

Adaptive population-based multi-objective optimization in SDN controllers for cost optimization

引用

PHYSICAL COMMUNICATION 2023年第1期58卷

作者： Qaffas, Alaa A. Kamal, Shoaib Sayeed, Farrukh Dutta, Papiya Joshi, Shubham Alhassan, Ibrahim Univ Jeddah Coll Business Dept Management Informat Syst Jeddah Saudi Arabia Global Acad Technol Dept Artificial Intelligence & Data Sci Bengaluru 560098 Karnataka India Ace Coll Engn Dept Elect & Elect Engn Trivandrum India Bharat Inst Engn & Technol Dept Elect & Commun Engn Hyderabad 501510 Telangana India SVKMS NMIMS MPSTME Shirpur Campus Dept Comp Engn Savalade India Saudi Elect Univ E Commerce Dept Riyadh Saudi Arabia

In Wireless Sensor Networks, Software Defined Networks (SDN) provide a logically centralized control plane as a potential means of streamlining network management (WSNs). The employment of several SDN controllers to build a physically distributed SDN is a common tactic to boost speed, expand scalability, and offer fault tolerance. However, the deployment of many controllers results in increased synchronization and deployment expenses. Therefore, selecting the optimal location for SDN controllers to improve WSN performance is a research issue. In this paper, the multi-objective optimization problem known as the controller placement problem (CPP) is initially formulated. Cost, time, and reliability are just a few of the restraints that are taken into consideration in this regard. In addition, a new Adaptive Population-Based Cuckoo optimization (APB-CO) for optimal controller placement is implemented. In the end, APB-CO performs experiments to validate the efficacy by analyzing Sync (7.5), Coverage (47), Controller Cost (4.8), and Fitness (0.6983) for the 100th node variation at network 1. The proposed model obtained the controller cost as 34.4, compared to the existing method such as Simulated Annealing (44.3) and Greedy Approach (42.6).(c) 2023 Elsevier B.V. All rights reserved.

关键词： Adaptive population based Cuckoo optimization algorithm Controllers Software defined network Reliability Timing

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：