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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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A parallel particle swarm optimization algorithm based on GPU/CUDA

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APPLIED SOFT COMPUTING 2023年 144卷

作者： Zhuo, Yanhong Zhang, Tao Du, Feng Liu, Ruilin Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Jingchu Univ Technol Sch Math & Phys Jingmen Hubei Peoples R China

Parallel computing is the main way to improve the computational efficiency of metaheuristic algorithms for solving high-dimensional, nonlinear optimization problems. Previous studies have typically only implemented local parallelism for the particle swarm optimization (PSO) algorithm. In this study, we proposed a new parallel particle swarm optimization algorithm (GPU-PSO) based on the Graphics Processing Units (GPU) and Compute Unified Device Architecture (CUDA), which uses a combination of coarse-grained parallelism and fine-grained parallelism to achieve global parallelism. In addition, we designed a data structure based on CUDA features and utilized a merged memory access mode to further improve data-parallel processing and data access efficiency. Experimental results show that the algorithm effectively reduces the solution time of PSO for solving high-dimensional, large-scale optimization problems. The speedup ratio increases with the dimensionality of the objective function, where the speedup ratio is up to 2000 times for the high-dimensional Ackley function. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： particle swarm optimization algorithm Parallel computing CUDA GPU function optimization [3] traveling salesman problem [4] wire

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A new boredom-aware dual-resource constrained flexible job shop scheduling problem using a two-stage multi-objective particle swarm optimization algorithm

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INFORMATION SCIENCES 2023年第1期643卷

作者： Shi, Jiaxuan Chen, Mingzhou Ma, Yumin Qiao, Fei Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China Tongji Univ Sch Econ & Management Shanghai 201804 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

Dual-resource constrained flexible job shop scheduling problem has become a hot research field in recent years. However, few studies have considered workers' boredom sensations when allo-cating resources and scheduling tasks, leading to inappropriate task allocation and negative effects such as efficiency reductions and absenteeism. Therefore, a new boredom-aware dual -resource constrained flexible job shop scheduling problem is investigated in this study, which considers the increase in workers' boredom caused by repetitive job assignments and constructs an efficiency function to characterize the impact of workers' boredom. For this problem, a bi-level lexicographic model, which takes the effective completion of all manufacturing tasks as the primary optimization objective and higher worker job satisfaction as the secondary optimization objective, is established. A two-stage multi-objective particle swarm optimization algorithm with a three-dimensional representation scheme is presented to solve this model. In this algorithm, a new position-updating mechanism and a local search strategy are devised to effectively evolve the solution, and a decomposition strategy with a self-adaptive neighborhood size and a boundary exploration mechanism are embedded to obtain a uniformly distributed Pareto front. Experi-mental results confirm the superiority of the presented model and algorithm.

关键词： Dual-resource constrained flexible job shop scheduling problem Workers ' boredom sensations Lexicographic method Two-stage multi -objective optimization algo rithm particle swarm optimization algorithm

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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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Application of video image processing in sports action recognition based on particle swarm optimization algorithm

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PREVENTIVE MEDICINE 2023年第1期173卷 107592-107592页

作者： Zhang, Youming Hou, Xingchen Mudanjiang Normal Univ Sch Phys Educ & Hlth Sci Mudanjiang 157011 Peoples R China

The existing sports training methods are mainly aimed at the sports field environment. The traditional sports training is only based on the coaches' visual inspection and combined with their own experience to put forward suggestions, which is relatively inefficient, thus limiting the rise of athletes' sports training level to a certain extent. Based on this background, combining traditional physical education teaching methods with video image processing technology, especially using particle swarm optimization algorithm, can promote the application of human motion recognition technology in physical training. This paper mainly investigates the optimization process of particle swarm optimization algorithm and discusses the development of particle swarm optimization algorithm;Secondly, through video decoding, image noise removal, video enhancement and other methods, complete video image processing and establish the structure of the manikin to achieve the collection of key points of the target, and then collect relevant data with experimental methods The results show that the motion recognition system proposed in this paper can effectively detect the changes of athletes' sampling point path, and can be compared with standard movements, which has a good auxiliary role. With the application of video image processing technology in sports training becoming more and more common, athletes can analyze their training videos in a more intuitive way and find out shortcomings, so as to improve the training effect. This paper studies particle swarm optimization algorithm and applies it to the field of video image processing, which promotes the development of sports action recognition technology based on video processing.

关键词： particle swarm optimization algorithm Video image processing Physical education teaching Action recognition

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Research on Enterprise Supply Chain Anti-Disturbance Management Based on Improved particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第8期14卷 927-936页

作者： Dai, Tongqing Huanggang Normal Univ Business Sch Huanggang 438000 Peoples R China

A supply chain that is effective and of the highest caliber boosts customer happiness as well as sales and earnings, increasing the company's competitiveness in the market. It has been discovered that the standard supply chain management technique leaves the supply chain with weak supply chain stability because it has a low ability to withstand the manufacturer's production behaviour. An enterprise supply chain resistance management model is built using the study's proposed particle swarm optimisation technique, which is based on a genetic algorithm with stochastic neighbourhood structure, to solve this issue. The suggested technique outperformed the other two algorithms utilised for comparison in a performance comparison test, with a stable particle swarm fitness value of 0.016 after 800 iterative iterations and the fastest convergence. The proposed model was then empirically examined, and the results revealed that the production team using the model completed the same volume of orders in 32 days while making $460,000 more in profit. With scores of 4.5, 4.5, 4.3, 4.3, 4.2, and 4.2, respectively, the team also had the lowest values of the six forms of employee anti-production conduct, outperforming the comparative management style. In summary, the study proposes an anti-disturbance management model for enterprise supply chains that can rationalise the scheduling of manufacturers' production behaviour and thus improve the stability of the supply chain.

关键词： Supply chain particle swarm optimization algorithm genetic algorithm inverse production behaviour neighbourhood structure

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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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