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A Novel Power Losses Reduction Method Based on a particle swarm optimization algorithm Using STATCOM

ENERGIES 2018年第10期11卷 2851-2851页

作者： Tuzikova, Valeriya Tlusty, Josef Muller, Zdenek Czech Tech Univ Dept Elect Power Engn Prague 16627 Czech Republic Czech Tech Univ Fac Biomed Engn Kladno 27201 Czech Republic

In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of smart energy, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using particle swarm optimization algorithms. All calculations were performed in MATLAB.

关键词： STATCOM FACTS power compensation particle swarm optimization algorithm power losses stability reliability optimal power flow

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Random perturbation particle swarm optimization algorithm

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Jisuanji Gongcheng/Computer Engineering 2006年第12期32卷 189-190+276页

作者： Yu, Binghui Yuan, Xiaohui Wang, Jinwen Quan, Xianzhang College of Hydropower and Digitalization Engineering Central China University of Science and Technology Wuhan 430074 China

A novel particle swarm optimization algorithm-random perturbation particle swarm optimization algorithm(RP-PSO) based on independence of population structure is proposed. To retain diversity of population and avoid being plunged to local optimum, it initializes the worst individual in population over again, at the same time, the best previous particle of each individual is randomly perturbed after evolutionary computation every time to improve its running efficiency and precision of over all optimization searching. Test results of complex functions demonstrate RAPSO is superior to basic particle swarm optimization in quality and efficiency.

关键词： Diversity of evolution population particle swarm optimization algorithm Random perturbation

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Multi_objective Model of Mixed Continuous/Batch Plants Planning Problem and Its particle swarm optimization algorithm

Multi_objective Model of Mixed Continuous/Batch Plants Plann...

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21st Chinese Control and Decision Conference

作者： Xu Xinli Dai Xinhua Hao Ping Wang Wanliang Zhejiang Univ Technol Coll Informat Engn Hangzhou 310014 Zhejiang Peoples R China Zhejiang Univ Technol Software Coll Hangzhou 310014 Zhejiang Peoples R China

ISBN: (纸本)9781424427239

A multi-objective, multi-level, multi-product, multi-constraint batch planning problem is abstracted from production of diaphragm caustic soda, and the problem is formulated as a mathematic optimization model with constraints involved resources, work manufacture processes and production capacity etc. Some sub_objectives were considered in the problem model, such as total profit amount, profit margin, total energy wastage and wastage per ten thousand RMB.A modified particle swarm optimization algorithm with a percent-coding and dynamic-bounds coding scheme is proposed for the problem. The validity and flexibility of the model and algorithm are verified by calculating the data from production practices numerically.

关键词： Medium-term Planning Problem particle swarm optimization algorithm Multi_objective

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Effects of Random Values for particle swarm optimization algorithm

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algorithmS 2018年第2期11卷 23-23页

作者： Dai, Hou-Ping Chen, Dong-Dong Zheng, Zhou-Shun Cent South Univ Sch Math & Stat Changsha 410083 Hunan Peoples R China Jishou Univ Sch Math & Stat Jishou 416000 Peoples R China State Key Lab High Performance Complex Mfg Changsha 410083 Hunan Peoples R China

particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [1, 1], and Gauss distribution with mean 0 and variance 1 (U [0, 1], U [1, 1] and G (0, 1)), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [1, 1] or G (0, 1) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [1, 1] or G (0, 1) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

关键词： particle swarm optimization algorithm random values uniform distribution gauss distribution

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An optimized particle swarm optimization algorithm for turbine torque and generator load parameter identification

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Procedia Computer Science 2024年 241卷 391-396页

作者： Zhangbin Yang Xiaokun Li Xiang Liao Daixiao Peng Hang Cai Xuemin He China Three Gorges Construction Engineering Corporation Chengdu 610095 P.R. China Three Gorges Intelligent Industrial Control Technology Co. Ltd Wuhan 430070 P.R. China

Hydraulic turbine control system is a complex system with strong nonlinearity and multiple variables. Therefore, in order to better control the turbine system, it is necessary to obtain the parameters of key components. Aiming at the limitation of the traditional particle swarm optimization algorithm in global search ability, mutation operator and dynamic inertia weight coefficient are introduced to enhance the search ability of the algorithm. In addition, in order to further improve the global search performance of the algorithm, this paper combines the optimized particle swarm optimization algorithm with genetic algorithm to form a hybrid parameter identification algorithm. The hybrid algorithm not only uses the fast convergence of particle swarm optimization (PSO), but also uses the global search advantage of genetic algorithm (GA) to realize efficient and accurate identification of turbine torque and load parameters. Through MATLAB2021a/Simulink simulation experiments, the application effect of the algorithm in the identification of turbine torque and generator load parameters is verified. The simulation results show that the optimized particle swarm optimization algorithm has significant advantages in the accuracy and robustness of parameter identification, and the identified parameters have a high degree of fitting with the actual measured torque and speed. This study not only provides a new optimization strategy for the parameter identification of turbine regulation system, but also provides an effective intelligent algorithm solution for the parameter identification of nonlinear system.

关键词： particle swarm optimization algorithm Parameter identification Turbine torque Genetic algorithm Dynamic inertia weight Mutation operator

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Gridless Net Routing of Integrate Circuit with particle swarm optimization algorithm

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Journal of Energy and Power Engineering 2011年第9期5卷 899-904页

作者： X.C. Huang Power Dispatching ＆ Communication Center Power Grid of Henan Zhengzhou 450000 China

particle swarm optimization algorithm is presented for the layout of ＂Integrate Circuit （IC）＂ design. particle swarm optimization based on swarm intelligence is a new evolutionary computational tool and is successfully applied in function optimization, neural network design, classification, pattern recognition, signal processing and robot technology and so on. A modified algorithm is presented and applied to the layout of IC design. For a given layout plane, first of all, this algorithm generates the corresponding grid group by barriers and nets＇ ports with the thought ofgridless net routing, establishes initialization fuzzy matrix, then utilizes the global optimization character to find out the best layout route only if it exits. The results of model simulation indicate that PSO algorithm is feasible and efficient in IC layout design.

关键词： particle swarm optimization algorithm gridless net routing layout optimization prufer number.

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A precise ultra-wideband ranging method using pre-corrected strategy and particle swarm optimization algorithm

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MEASUREMENT 2022年 194卷 110966-110966页

作者： Li, Di Wang, Xianglong Chen, Dongdong Zhang, Qidong Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China

In order to improve the ultra-wideband (UWB) ranging accuracy, a precise UWB ranging method is developed using pre-corrected strategy and particle swarm optimization (PSO) algorithm. In the pre-corrected strategy, the antenna delay function is established to correct the antenna parameters. In addition, the parameters of Kalman filter function are optimized by PSO algorithm to further improve the UWB ranging accuracy. The experimental results show that the maximum and average errors of the developed method are 7.45 and 3.82 cm, which are much smaller than those of the traditional ranging method (18 and 9.75 cm). Therefore, the developed UWB ranging method can be used in the precise ranging of electric devices.

关键词： Ultra-Wideband (UWB) Ranging method Pre-corrected strategy particle swarm optimization algorithm

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Research on a Distribution Center Location Model Based on a particle swarm optimization algorithm

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International Journal of Plant Engineering and Management 2009年第3期14卷 151-157页

作者： WANG Fei 1,2,HU Xin-bu3,JIA Tao41 School of Economy and Management,Chang’an University,Xi’an 710064,P.R.China 2 Institute of Human Geography,Xi’an International Studies University,Xi’an 710061,P.R.China 3 Architecture Engineering Department,Engineering College of Armed Police Forces,Xi’an 710086,P.R.China 4 School of Management,Xi’an Jiaotong University,Xi’an 710069,P.R.China School of Economy and Management Chang'an UniversityXi'an 710064P.R.China Institute of Human Geography Xi'an International Studies UniversityXi'an 710061P.R.China Architecture Engineering Department Engineering College of Armed Police ForcesXi'an 710086P.R.China School of Management Xi'an Jiaotong UniversityXi'an 710069P.R.China

Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity. Transportation costs, distribution center construction costs, ordering costs, safe inventory costs and inventory holding costs are the important parts of the total logistics costs. In this paper, based on the research results of LMRP（ location model of risk pooling） location with fixed construction cost, the LMRPVCC （ location model of risk pooling based on variable construction cost） will be introduced. Applying particle swarm optimization to several computational instances, the authors find the suboptimum solution of the model.

关键词： location-inventory particle swarm optimization algorithm variable cost of construction

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A Two-Stage particle swarm optimization algorithm for Wireless Sensor Nodes Localization in Concave Regions

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INFORMATION 2020年第10期11卷 488页

作者： Meng, Yinghui Zhi, Qianying Zhang, Qiuwen Yao, Ni Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou 450002 Peoples R China

At present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity and so on. To solve these problems, this paper proposes a two-stage PSO (particle swarm optimization) algorithm for wireless sensor nodes localization in "concave regions". In the first stage, it proposes a method of distance measuring based on similar path search and intersection ratio, and completes the initial localization of unknown nodes based on maximum likelihood estimation. In the second stage, the improved PSO algorithm is used to optimize the initial localization results in the previous stage. The experimental result shows that the localization error of this algorithm is always within 10% and the execution time is maintained at about 20 s when the communication radius and beacon node ratio is changing. Therefore, the algorithm can obtain high localization accuracy in wireless sensor network with "concave regions", requiring low computing power for nodes, and energy consumption. Given this, it can greatly extend the service life of sensor nodes.

关键词： nodes localization concave region intersection ratio similar path particle swarm optimization algorithm

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Inverse Calculation for Thermal Conductivity of Soil particles Based on particle swarm optimization algorithm

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ELECTRONIC JOURNAL OF GEOTECHNICAL ENGINEERING 2016年第14期21卷 4613-4622页

作者： Jiang, Ping Xiao, Jingping Lu, Xilei Zeng, Hao Zhang, Fang Wang, Wei Shaoxing Univ Dept Bldg Engn Shaoxing Peoples R China Huahui Engn Design Grp Co Ltd Shaoxing Peoples R China Shaoxing Univ Dept Engn Management Shaoxing Peoples R China

Thermal conductivity is a significant parameter for studying the temperature effect of soil mechanical behaviours, and the thermal conductivity of soil particles has important influence on the calculation of soil thermal conductivity. As it is difficult to obtain the thermal conductivity of soil particles directly from test, it is usually obtained indirectly based on the inversion calculation of soil thermal conductivity. According to the characteristic of series parallel calculation prediction model of soil thermal conductivity, based on particle swarm optimization algorithm, the thermal conductivity of soil particles was inversely calculated by using the thermal conductivity of dry soil with different porosity;and the determination method of interpolation coefficient was also verified. The thermal conductivity of four kind's soil particles of peat silty clay, silty clay, gravel and gravel soil was inversely calculated to verify the accuracy of this model. The calculation results show that this prediction model can accurately determine the thermal conductivity of soil particles with a wide range of engineering applications

关键词： thermal conductivity series parallel model parametric inversion particle swarm optimization algorithm

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