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Design optimization of wind turbine tower with lattice-tubular hybrid structure using particle swarm algorithm

STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2016年第15期25卷 743-758页

作者： Chen, Junling Yang, Rongchang Ma, Renle Li, Jinwei Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China Tongji Architectural Design Grp Co Ltd Shanghai Peoples R China

As the size of wind turbine grows, the cost of the commonest tubular structural system will also increase because of the increasing cost of transportation, assembly, erection and servicing. A lattice-tubular hybrid structural system, which is composed of a four-angle cross-shaped lattice structure at the bottom and a tubular structure at the top, is proposed for large wind turbine systems. The welding processes can be totally avoided and the fatigue strengths effectively improved because all members can be assembled by bolts in site after cutting and drilling in the factory. The ultimate bearing capacities of combined cross-shaped members subjected to axial compressions are obtained by a series of numerical analyses. The column curve of four-angle-combined cross-shaped members is obtained by fitting numerical results with a piecewise function. The particle swarm optimization algorithm is adopted to optimize the shape and size of the lattice partition in this study. The constraints including stress, slenderness ratio and frequency are applied to find the minimum weight of the lattice partition in the hybrid tower. The optimal results show that the proposed structural system is feasible and can resolve the disadvantages of the traditional tubular system in the fabricating, mounting and transporting. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： wind turbines lattice tower hybrid structure particle swarm algorithm cross-shaped section structural optimization

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Dynamic Optimization Method on Electromechanical Coupling System by Exponential Inertia Weight particle swarm algorithm

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Chinese Journal of Mechanical Engineering 2009年第4期22卷 602-607页

作者： LI Qiang WU Jianxin SUN Yan College of Mechanical and Electrical Engineering North China University of Technology Beijing 100144 China College of Mechanical Engineering lnner Mongolia University of Technology Hohhot 010051 China

Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm（EPSA）, is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimizati on. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.

关键词： particle swarm algorithm electromechanical coupling system dynamic optimization

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A novel feasible task sequence-oriented discrete particle swarm algorithm for simple assembly line balancing problem of type 1

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第9-12期69卷 2445-2457页

作者： Dou, Jianping Li, Jun Su, Chun Southeast Univ Sch Mech Engn Nanjing Jiangsu Peoples R China Southeast Univ Sch Automat Nanjing Jiangsu Peoples R China

To solve the simple assembly line balancing problems of type 1 (SALBP-1), almost all of particle swarm algorithms (PSAs) for SALBP-1 adopt task sequence-oriented solution representation and are limited to the priority-based indirect encoding of feasible task sequence (FTS) so far. In this paper, firstly a novel FTS-oriented particle swarm algorithm (FTSOPSA) that directly records a FTS by a particle, named direct discrete PSA (DDPSA), is proposed to solve SALBP-1. In the DDPSA, a new multi-fragment crossover-based updating mechanism is developed, and the fragment mutation is incorporated into the DDPSA to improve exploration ability. Secondly, a systematic comparison of DDPSA and two existing FTSOPSAs as well as two existing genetic algorithms (GAs) has been presented against a set of instances selected from the literature and 15 randomly generated instances of SALBP-1. Comparisons between the FTSOPSAs and existing GAs show promising higher performance of the proposed DDPSA for SALBP-1, and also show that the direct encoding of FTS seems superior to the priority-based indirect encoding of FTS for solving SALBP-1.

关键词： Assembly line balancing particle swarm algorithm Feasible task sequence Fragment crossover Comparative study

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Optimization of pipeline system with multi-hoop supports for avoiding vibration, based on particle swarm algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第9期235卷 1524-1538页

作者： Liu, Xudong Sun, Wei Gao, Ye Ma, Hui Northeastern Univ Sch Mech Engn & Automat Shenyang Peoples R China Northeastern Univ Key Lab Vibrat & Control Aeroprop Syst Minist Educ Shenyang Peoples R China

In the dynamics design of aero-engine pipeline systems, it is necessary to avoid the excitation frequency of the engine (mainly including the rotational frequencies of high pressure and low pressure rotor systems) to improve the operational reliability of the pipeline system. In this study, a single-pipe system with multi-hoop supports was chosen as the research object, and a method based on the particle swarm algorithm was developed to optimize the layout of the hoops for effectively avoiding vibration of the pipeline system. A finite element model (FEM) of the pipeline system was created and the group of spring elements with non-uniform distribution of stiffness values was used to simulate the hoop support for improving the analysis accuracy of the model in the modeling process. Taking the hoop position as design variable, an optimization model of the pipe hoop layout was established, which aims at avoiding one or two excitation frequencies at the same time. Furthermore, the calculation procedure of optimizing pipe hoop layout using the particle swarm algorithm was given. Finally, a case study was carried out, the rationality of the created FEM was validated by experiments, and the optimal layout of hoops was obtained using the proposed optimization method.

关键词： Pipeline system with multi-hoop supports finite element model optimization avoiding vibration particle swarm algorithm

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Trajectory Planning of Rail Inspection Robot Based on an Improved Penalty Function Simulated Annealing particle swarm algorithm

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第10期21卷 3368-3381页

作者： Xu, Ruoyu Tian, Jianyan Li, Jifu Zhai, Xinpeng Taiyuan Univ Technol Coll Elect & Power Engn 79 Yingze West St Taiyuan Shanxi Peoples R China

To ensure the smooth operation of each joint and shorten the joint movement time of a rail inspection robot, a trajectory planning method based on time optimization with a penalty function is proposed. According to the Denavit-Hartenberg (D-H) model of the inspection robot, a kinematic solution is found, and the trajectory of each joint is generated using a mixed polynomial interpolation algorithm. Taking time optimization as the standard, the traditional particle swarm algorithm cannot handle complex constraints, easily falls to local optimum solutions, and has a slow convergence speed. An improved simulated annealing particle swarm algorithm with a penalty function (IPF-SA-PSO) is proposed to optimize the trajectory generated by the mixed polynomial interpolation algorithm. The simulation results show that the proposed algorithm, compared with the mixed polynomial interpolation method, can limit the angular velocity and reduce the running time of each manipulator joint. The two algorithms are experimentally verified based on a rail inspection robot, and the results show that after adopting the optimization algorithm, the angular velocity of each joint is within the angular velocity limit, the run time is shorter, and the operation is smoother, which indicates the effectiveness of the proposed algorithm. The proposed algorithm can optimize the robot running time, improve the smoothness, and be applied to the fields of the automatic tracking of abnormal targets and video acquisition.

关键词： particle swarm algorithm penalty function rail inspection robot simulated annealing algorithm trajectory planning

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Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S2761-S2769页

作者： Chen, Xuan Long, Dan Zhejiang Ind Polytech Coll Shaoxing 312006 Peoples R China Zhejiang Univ Hangzhou 310007 Zhejiang Peoples R China

The optimization of task scheduling in cloud computing is built with the purpose of improving its working efficiency. Aiming at resolving the deficiencies during the method deployment, supporting algorithms are therefore introduced. This paper proposes a particle swarm optimization algorithm with the combination of based on ant colony optimization, which proposes the parameter determination into particle swarm algorithm. The integrated algorithm is capable of keeping particles in the fitness level at a certain concentration and guarantee the diversity of population. Further, the global best solution with high accurate converge can be exactly gained with the adjustment of learning factor. After the implementation of proposed method in task scheduling, the scheme for optimizing task scheduling shows better working performance in fitness, cost as well as running period, which presents a more reliable and efficient idea of optimal task scheduling.

关键词： Cloud computing particle swarm algorithm Ant colony optimization Task scheduling

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A composite particle swarm algorithm for global optimization of multimodal functions

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Journal of Central South University 2014年第5期21卷 1871-1880页

作者：谭冠政鲍琨 Richard Maina Rimiru School of Information Science and Engineering Central South University

During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.

关键词： particle swarm algorithm global numerical optimization novel learning strategy assisted search mechanism feedbackprobability regulation

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Optimization of heat pump system in indoor swimming pool using particle swarm algorithm

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APPLIED THERMAL ENGINEERING 2008年第13期28卷 1647-1653页

作者： Lee, Wen-Shing Kung, Chung-Kuan Natl Taipei Univ Technol Dept Energy & Refrigerating Air Conditioning Engn Taipei Taiwan

When it comes to indoor swimming pool facilities, a large amount of energy is required to heat up low-temperature outdoor air before it is being introduced indoors to maintain indoor humidity. Since water is evaporated from the pool surface, the exhausted air contains more water and specific enthalpy. In response to this indoor air, heat pump is generally used in heat recovery for indoor swimming pools. To reduce the cost in energy consumption, this paper utilizes particle swarm algorithm to optimize the design of heat pump system. The optimized parameters include continuous parameters and discrete parameters. The former consists of outdoor air mass flow and heat conductance of heat exchangers;the latter comprises compressor type and boiler type. In a case study, life cycle energy cost is considered as an objective function. In this regard, the optimized outdoor air flow and the optimized design for heating system can be deduced by using particle swarm algorithm. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： particle swarm algorithm heat pump optimum

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Variable power regulation charging strategy for electric vehicles based on particle swarm algorithm

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ENERGY REPORTS 2022年 8卷 824-830页

作者： Ji, Yang Zhang, Jian Li, Siwei Deng, Youjun Mu, Yunfei Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Beijing Fibrlink Commun Co LTD Beijing 100070 Peoples R China

With the widespread application of electric vehicles, the constant-power charging method that users can charge immediately makes the charging period of the vehicle coincide with the peak period of regular electricity consumption of the distribution network, which will cause the phenomenon of "peak-on-peak" of the basic load of the power grid. In this paper, a variable power regulation charging optimization strategy is proposed, which takes the charging power and charging state of the electric vehicle in each period as the optimization variables, and uses the particle swarm algorithm to solve the charging strategy. Taking an office area as an example, the results show that the acceptance capacity is significantly improved when the variable power charging strategy is adopted. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Electric vehicle Distribution network particle swarm algorithm Variable power regulation Charging strategy

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Stability analysis and control optimisation based on particle swarm algorithm of modular multilevel matrix converter in fractional frequency transmission system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第14期14卷 2641-2655页

作者： Meng, Yongqing Li, Sijia Zou, Yichao Li, Kaikai Wang, Xiuli Wang, Xifan Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Smart Grid Xian Shaanxi Peoples R China State Grid Jiangsu Elect Power Co Maintenance Branch Wuxi Operat & Maintenance Stn Wuxi Jiangsu Peoples R China

Modular multilevel matrix converter (M3C) is a competitive option in the fractional frequency transmission system (FFTS) application. Focusing on stability and power quality issues, this study firstly proposes a new mathematical model and control strategy. Different from the previous research, this control scheme is based on the frequency decoupling model of doubledqcoordinate transformation and the control of the sub-converter, which implements the frequency decoupling control and solves the frequency leakage problem. Subsequently, a complete state-space model and small-signal model of M3C are built for analysing small disturbance stability. On this basis, the optimisation of M3C in FFTS is studied, and an optimisation method based on particle swarm algorithm is proposed. This method can directly design the adaptive objective function according to the optimisation requirements of system control performance to simultaneously optimise all controller parameters of the system. After optimisation, the stability and dynamic performance of the system have been significantly improved. Finally, the effectiveness of the proposed control and optimisation is verified by the simulation results in MATLAB/ Simulink.

关键词： particle swarm optimisation state-space methods power supply quality matrix convertors optimisation complete state-space model small-signal model M3C analysing small disturbance stability FFTS optimisation method particle swarm algorithm optimisation requirements system control performance controller parameters dynamic performance stability analysis modular multilevel matrix converter competitive option fractional frequency transmission system application power quality issues mathematical model control scheme frequency decoupling model frequency decoupling control frequency leakage problem

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