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Design and Application of Vague Set Theory and Adaptive Grid particle swarm optimization algorithm in Resource Scheduling optimization

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JOURNAL OF GRID COMPUTING 2023年第2期21卷 1-15页

作者： Han, Yibo Han, Pu Yuan, Bo Zhang, Zheng Liu, Lu Panneerselvam, John Nanyang Inst Technol Nanyang Inst Big Data Res Nanyang 473004 Peoples R China Nanyang Inst Technol Sch Informat Engn Nanyang 473004 Peoples R China Univ Leicester Dept Informat Univ Rd Leicester LE1 7RH England Nanyang Inst Technol Sch Comp & Software Nanyang 473004 Peoples R China

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

关键词： Resource scheduling Vague set theory particle swarm optimization algorithm Model Multi objective

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Design of Zero-Differential Steering Controller for Tracked Vehicles With Hydraulic-Mechanical Transmission Based on particle swarm optimization algorithm

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IEEE ACCESS 2023年 11卷 32187-32200页

作者： Jia, Weijian Liu, Xixia Zhang, Chuanqing Qiu, Mianhao Tan, Yongying Yu, Zhang Army Acad Armored Forces Beijing 100072 Peoples R China Beijing Special Engn & Design Inst Beijing 100028 Peoples R China

The handling stability of tracked vehicles not only affects the handling convenience of drivers but is also an important part of vehicle active safety. A zero-differential steering controller for tracked vehicles with hydraulic-mechanical transmissions was designed in this paper. First, the working principle of the steer-by-wire systems of tracked vehicles was analyzed, and the vehicle speed calculation model was established. Then, the steering dynamics model of the tracked vehicle was established based on the shear stress model. Finally, based on the particle swarm optimization algorithm, the established tracked vehicle steering dynamics model was iteratively solved, and the optimal yaw rate gain K-r was calculated in real time and used for vehicle steering control. The steering control simulation model of tracked vehicles was established in MATLAB/Simulink, and the control effect of the designed steering controller was verified by the simulation. The control effect was evaluated by the comprehensive evaluation index of the handling stability. The simulation results showed that the steering controller based on the particle swarm optimization algorithm effectively improved the handling stability of the tracked vehicle and reduced the burden on the driver.

关键词： Mathematical models Stability criteria Wheels Vehicles Hydraulic systems Gears Engines particle swarm optimization algorithm steer-by-wire system tracked vehicle zero-differential steering controller

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Implementation of genetic and particle swarm optimization algorithm for voltage profile improvement and loss reduction using capacitors in 132 kV Manipur transmission system

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ENERGY REPORTS 2023年 9卷 738-746页

作者： Bidyanath, Khaidem Singh, Sanasam Dhanabanta Adhikari, Shuma NIT Manipur Imphal 795004 Manipur India

Complex power plays a key role in maintaining and sustaining the fields i.e. magnetic and electric fields. The efficiency of the power system depends entirely on the loss and voltage profile level of the system. The lower the level of loss and the higher the voltage profile, the higher the system efficiency. Installation of the capacitors on a system greatly helps in controlling and reducing levels of the reactive energy of the system. The location and size of the capacitors that need to be installed are of great importance in the installation of the bank of capacitors. The idea of a Genetic algorithm and also a particle swarm algorithm are deployed in this paper for calculating the optimal location and size of the capacitor. In this paper, the system of the 132 KV Manipur Transmission System is designed in ETAP considering a radial distribution part and the optimization method of a genetic method, and also the particle swarm algorithm is utilized for finding the optimal location and size of the capacitor. The system is further optimized by reducing the loss and improving the voltage profile of the system. And the comparison of both the optimization techniques is presented. The results show that PSO provides optimal solutions while GA is simpler in the optimization process. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Etap Matlab Reactive power Genetic algorithm particle swarm optimization algorithm Optimal capacitor placement Power loss

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Smart prediction for tunnel fire state evolution based on an improved fire simulation curve through particle swarm optimization algorithm

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FIRE SAFETY JOURNAL 2023年第1期136卷

作者： Liu, Xiaojiang Xu, Zhao-Dong Sun, Bin Liu, Xuanya Xu, Dajun Southeast Univ China Pakistan Belt & Rd Joint Lab Smart Disaster Nanjing 210096 Peoples R China Tianjin Fire Res Inst Minist Emergency Management Tianjin 300381 Peoples R China

Tunnel fires have received significant attention because they can cause large economic losses every year. To estimate the fire development and realize a prompt response to the fire disaster are thus crucial to tackle the above issue. Here, a smart prediction method for fire state evolution is developed based on an improved fire simulation curve through the particle swarm optimization algorithm. The improved fire simulation curve is established by supplementing the stable stage during the tunnel fire based on a fire curve proposed in 2002. Experimental results from a prototypical tunnel fire test indicate that the proposed method can effectively portray the behavior of a typical tunnel fire development. Moreover, it is accurate over a wide range of conditions, including different heat release rates, different measurement noises, different sampling intervals, and different prior durations. Particularly, the fluctuation range of estimation results is becoming larger with the increasing noise level. The influence of the sampling interval is mainly reflected in the calculation time. Prior duration is the most important factor of the prediction accuracy, the longer prior duration corresponds to the more precise results. 200s and 300s can be regarded as the critical duration by considering the estimation error of the whole-time domain and the preliminary stage, respectively. The proposed method has a broad application prospect, it can avoid complicated temperature spreading mechanisms, the additional workload for prior information, and the limitation of the specific fire scene.

关键词： Tunnel fire Fire state evolution particle swarm optimization algorithm Fire simulation curve Full-scale tests

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Multi-objective optimization of double-walled steel cofferdams based on response surface methodology and particle swarm optimization algorithm

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STRUCTURES 2023年 49卷 256-266页

作者： Shao, Junhu Fan, Zihao Huang, Yuanyuan Zhan, Yulin Cai, Qinhao Chengdu Univ Sch Architecture & Civil Engn Chengdu 610106 Peoples R China Southwest Jiaotong Univ Dept Bridge Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Inst Civil Engn Mat Chengdu 610031 Peoples R China China Railway Erju Fifth Engn Co Ltd Chengdu 610031 Peoples R China

To improve the safety of steel cofferdams and reduce the cost to the extent possible, a multi-objective optimi-zation method based on response surface methodology (RSM) and particle swarm optimization (PSO) algorithm was proposed to optimize the structure of double-walled steel cofferdams in the deep-water bridge foundation construction. The thickness of the inner and outer wall plate, the height and thickness of the inner-wall brace and the vertical spacing between annular plates were selected as design variables, taking the stress of the inner-wall brace and inner wall plate as the optimization objectives and the cost as the constraint condition, an optimization model of double-wall steel cofferdam was proposed. On the basis of the finite element calculation results, the experimental design and RSM were used to obtain the explicit relationship between the design variables and the structural response of the cofferdam. The optimal values of the design parameters of the cofferdam were obtained using PSO algorithm. The optimal design parameters and the structural response of the cofferdam variation with the cost were investigated. It is shown that the combination of RSM and PSO is able to optimize the cofferdam structure, with a stress optimization rate of 49.8% for the inner-wall brace while keeping the original cost constant. In addition, by adjusting the cost threshold, the reasonable range of cost coefficient K for this cofferdam is 0.119 to 0.162, and the section size of inner-wall brace is the key design parameter for the cofferdam, which provides a design idea for the optimization of deep-water cofferdam structure.

关键词： Double-walled steel cofferdam Response surface methodology Constraints particle swarm optimization algorithm Multi-objective optimization design

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Research on Minimum Copper Loss Control Strategy Based on particle swarm optimization algorithm 4th

Research on Minimum Copper Loss Control Strategy Based on Pa...

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4th International Symposium on New Energy and Electrical Technology (ISNEET)

作者： Zhou, Xuan Wang, Xiaona Naval Univ Engn Sch Elect Engn Wuhan 430033 Hubei Peoples R China

ISBN: (纸本)9789819770496;9789819770472;9789819770465

The open winding permanent magnet synchronous motor is driven by two sets of inverters and has the characteristics of high power and torque, which is applied to electric vehicles. This article aims to reduce the copper loss of open winding permanent magnet synchronous motors and proposes a variable decoupling angle modulation strategy based on the system. This strategy takes the angle (decoupling angle) between the output voltage vectors of two sets of inverters as the control variable, and uses particle swarm optimization algorithm to select the optimal decoupling angle based on different load states of the motor, driving an open-winding permanent magnet synchronous motor to reduce copper consumption. Through simulation verification, this strategy effectively reduces motor copper consumption and provides theoretical support for controlling electric vehicle operating losses.

关键词： Open-winding permanent magnet synchronous motor particle swarm optimization algorithm copper Loss

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Optimal DFIG-Based Wind Turbine Speed And Reactive Power Control Using particle swarm optimization algorithm 2

Optimal DFIG-Based Wind Turbine Speed And Reactive Power Con...

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2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Harkat, Djoumana Bouchemha, Amel Yousfi, Laatra Kahla, Sami Echahid Cheikh Larbi Tebessi Univ Tebessa LAMIS Lab Tebessa Algeria Echahid Cheikh Larbi Tebessi Univ Tebessa LAVIA Lab Tebessa Algeria Res Ctr Ind Technol CRTI Algiers LAVIA Lab Tebessa Algeria

ISBN: (纸本)9798350349740;9798350349757

As the demand for alternative energy sources grows, wind energy systems are developing at a rapid pace. In this research paper, optimally tuned PI controllers are used for controlling a wind turbine based on of a Doubly Fed Induction Generator (DFIG). The particle swarm optimization (PSO) algorithm for PI controller parameters tuning, with the objective of ensuring better energy quality and system performance. The speed Integral Square Error is used as a fitness function of the PSO. The performance evaluation of controllers with PSO implementation is carried out by the simulation through MATLAB /Simulink under linear and nonlinear load conditions. The PI and PI-PSO implementation results comparison proves the superiority of the PI-PSO controller.

关键词： Wind turbine Doubly-fed induction generator particle swarm optimization algorithm PI controller

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Research on Prediction Intervals of Greenhouse Tomato Yield Based on Improved particle swarm optimization algorithm 36

Research on Prediction Intervals of Greenhouse Tomato Yield ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Yu, Xiaoyu Luo, Yuzhu Wang, Jianjun Tian, Shujiao Peng, Xiuyuan Liaoning Acad Agr Sci Inst Informat Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Tomato is the main cultivated crop in China, and greenhouse tomato can improve the quality and yield of tomato by improving the planting environment. Predicting greenhouse tomato yield is an important basis for making production plan, and the accuracy of yield prediction directly affects economic benefits. In order to improve the accuracy of the prediction intervals model of greenhouse tomato yield, a new objective function was proposed to improve the Lower Upper Bound Estimation (LUBE) prediction intervals model. Then, based on the improved LUBE prediction intervals model, the particle swarm optimization algorithm (PSO) was further improved by adaptive inertia weight and escape strategy. Finally, a LUBE prediction intervals model based on the improved PSO algorithm was proposed in this paper. The prediction intervals of greenhouse tomato yield was simulated by using LUBE prediction intervals model, the improved LUBE prediction intervals model and the LUBE prediction intervals model based on the improved PSO algorithm. The experimental results showed that the prediction intervals coverage probability (PICP) of the LUBE prediction intervals model, the improved LUBE prediction intervals model and the LUBE prediction intervals model based on the improved PSO algorithm were 75%, 80% and 80%, respectively, and the prediction intervals normalized average width (PINAW) were 0.3938, 0.3725 and 0.3629, respectively. It can be concluded that the LUBE prediction intervals model based on the improved PSO algorithm has higher prediction accuracy and better fitting ability.

关键词： adaptive inertia weight escape strategy particle swarm optimization algorithm greenhouse tomato prediction intervals

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An Improved particle swarm optimization algorithm-Based MPPT Method for Distributed Photovoltaic Hydrogen Production Systems 9

An Improved Particle Swarm Optimization Algorithm-Based MPPT...

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9th International Conference on Power and Renewable Energy

作者： Zhong, Yin Peng, Yunfeng Chen, Hongtian Hao, Guolin Gao, Fenglin Zhou, Zheng Shanghai Jiao Tong Univ Sch Elect & Elect Engn Shanghai Peoples R China Xiangtan Univ Sch Phys & Optoelect & Hunan Inst Adv Sensing & I Xiangtan Peoples R China Shanghai Jiao Tong Univ Sch Smart Energy Shanghai Peoples R China

ISBN: (纸本)9798350377477;9798350377460

In order to cope with the increase in carbon emissions caused by the excessive use of fossil energy, the world's energy structure needs to be adjusted. Distributed photovoltaic (PV) hydrogen production system, as an integrated energy system, is an important pathway to the zero-carbon goal. But distributed PV is prone to face the shading situation, while the PV module power output curve will show the phenomenon of multiple peaks. The traditional maximum power tracking methods are prone to fall into the local optimal situation, and the intelligent tracking methods are inherently more complex. The hydrogen production electrolyzer puts forward the requirement of stability on the output power of the PV power supply. Existing research focuses on the maximum power tracking of PV system, or the control of electrolyzer, lacking the coupled modeling control research of distributed PV hydrogen production system. In order to fill the gap of the existing research, this paper proposes a composite algorithm which combines the Improved particle swarm algorithm (IPSO) with the Improved Perturbation Observation (IP&O) method. The initial population and termination conditions of the particle swarm search method (PSO) are improved to enhance the convergence speed of the algorithm and reduce the oscillations during the convergence process of the algorithm, and the IP&O method is utilized to provide stable inputs for the electrolytic tanks. The performance of the algorithm is verified through the comparative experiments with other algorithms.

关键词： Photovoltaic hydrogen production Maximum power point tracking particle swarm optimization algorithm

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optimization of Cable Force for Hybrid Girder Cable-Stayed Bridge Based on particle swarm optimization algorithm 8th

Optimization of Cable Force for Hybrid Girder Cable-Stayed B...

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8th International Conference on Civil Architecture and Structural Engineering (ICCASE)

作者： Xiao, Wenhao Changsha Univ Sci & Technol Sch Civil Engn Changsha Peoples R China

ISBN: (纸本)9783031761010;9783031761027

In order to make the hybrid girder cable-stayed bridge force reasonable, in line with the reasonable bridge formation state, the adaptive particle swarm optimization algorithm is used to optimize the cable-stayed bridge of hybrid girder cable-stayed bridge, the adaptive particle swarm optimization algorithm is written by using MATLAB software, and the finite element model is established by using Midas software for the analysis and calculation, and the objective function is constructed by using the influence matrix, and the value of the fitness is calculated, and the adaptive particle swarm optimization algorithm is used to find the global optimal solution. The results show that the vertical displacement of the main beam and the lateral displacement of the main tower are obviously reduced after optimization, and the bending moment of the main tower is obviously improved.

关键词： particle swarm optimization algorithm Hybrid girder cable-stayed bridge Cable-stayed force optimization Influence matrix

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