scholarly journals Application of Particle Swarm Optimization in the Design of an ICT High-Voltage Power Supply with Dummy Primary Winding

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1866
Author(s):  
Can Jiang ◽  
Jun Yang ◽  
Mingwu Fan
Keyword(s):  
Particle Swarm Optimization ◽  
High Voltage ◽  
Power Supply ◽  
Output Voltage ◽  
Circuit Simulation ◽  
Particle Swarm ◽  
Design Parameters ◽  
High Voltage Power Supply ◽  
Swarm Optimization ◽  
Load Regulation

The distribution of disk output voltage is a key factor for the design of an insulated core transformer (ICT) high-voltage power supply. The development of an ICT involves the design and optimization of many parameters, which greatly affect the uniformity of disk output voltage. A new ICT structure with dummy primary windings can compensate for the disk output voltage, which aims to improve uniformity. In this work, an optimization method based on a particle swarm optimization (PSO) algorithm was used to optimize the design parameters of an ICT with dummy primary windings. It achieved an optimal uniformity of disk output voltage and load regulation. The design parameters, including the number of secondary winding turns and the compensation capacitance, were optimized based on the finite-element method (FEM) and Simulink circuit simulation. The results show that the maximum non-uniformity of the disk output voltage is reduced from 11.1% to 4.4% from no-load to a full load for a 200 kV/20 mA HUST-ICT prototype. Moreover, the load regulation is greatly reduced from 14.3% to 9.6%. The method improves the stability and reliability of the ICT high voltage power supply and greatly reduces the design time.

scholarly journals Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (20) ◽  
pp. 9772
Author(s):  
Xueli Shen ◽  
Daniel C. Ihenacho
Keyword(s):  
Particle Swarm Optimization ◽  
Research Effort ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Design Parameters ◽  
Initial Population ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value ◽  
Sensitivity Approach

The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/second.

Adaptive Shewhart Control Charts Under Fuzzy Parameters with Tuned Particle Swarm Optimization Algorithm

2021 ◽  
pp. 1-36
Author(s):  
Amirhossein Amiri ◽  
Ali Salmasnia ◽  
Meraj Zarifi ◽  
Mohammad Reza Maleki
Keyword(s):  
Particle Swarm Optimization ◽  
Control Chart ◽  
Adaptive Sampling ◽  
Control Charts ◽  
Particle Swarm ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Inference System ◽  
Scale Parameters ◽  
Adaptive Control Charts

In recent years, adaptive control charts in which the design parameters depend on the observed samples have been successfully used as efficient alternatives for traditional control charts with constant parameters. In crisp run control rules, the process state may change very sharply from in-control to out-of-control conditions which increase the rate of false alarms. To overcome this drawback, this paper presents an adaptive Shewhart-type control chart, where the design parameters (sample size ([Formula: see text]), sampling interval ([Formula: see text]), and control limit coefficients ([Formula: see text] and [Formula: see text])) are defined with linguistic variables. To accomplish that, the chart parameters are determined based on the location of eight previous chart statistics using a set of fuzzy rules in a continuous environment. In order to improve the sensitivity of the proposed control chart in detecting small shifts in both location and scale parameters, the adaptive procedure is designed by integration of fuzzy Western Electric rules and fuzzy adaptive sampling rules. After designing the control charts using a fuzzy inference system (FIS), in order to provide an economic design of the proposed control chart, a tuned Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal values corresponding to membership functions of the control chart parameters. Finally, using simulation studies, the capability of the proposed control chart is analyzed and compared with common charts in the literature. The results confirm that under different shifts in location and scale parameters, the proposed control chart outperforms other charts in terms of both economic and statistical criteria.

A 6-DOF Ship-Borne Antenna Platform With Large Orientation Workspace

2020 ◽  
Author(s):  
Yuhang He ◽  
Weijia Li ◽  
Yaozhong Wu ◽  
Jinbo Wu ◽  
Zhiyuan Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Weight Ratio ◽  
Stewart Platform ◽  
Design Parameters ◽  
Parallel Mechanisms ◽  
Swarm Optimization ◽  
Pitch Range ◽  
Orientation Workspace ◽  
Load Weight

Abstract Compared with traditional antenna platform with two axes, Stewart platform can search airspace with no tracking blind district. And the advantages of high accuracy, high stiffness and high load-weight ratio also make it be a better solution for antenna platforms. This paper designed a 6-DOF ship-borne antenna platform based on the Stewart platform to overcome the difficulties that to realize a large orientation workspace (azimuth range is from 0° to 360°, pitch range is from 0° to 100°) under the compact dimensions of parallel mechanisms. A novel joint structure has been proposed which can provide a larger rotation angle than common Hooke joints to realize the large orientation workspace without the inter-mechanism interference. In addition, this paper defined the concept of working height and working radius then proposed a trajectory based on that to obtain the complete pose (translation and orientation) of antenna platform by azimuth and pitch angles. After that, the particle swarm optimization algorithm is employed to seek the optimal geometrical design parameters. A prototype of the 6-DOF ship-borne antenna platform adopted the particle swarm optimization results has been constructed. And the results show that it not noly meets the design requirements, but also provides a good performance.

Designing of a High Voltage Power Supply for Electrospinning Apparatus Using a High Voltage Flyback Transformer (HVFBT)

2015 ◽  
Vol 771 ◽  
pp. 145-148 ◽  
Author(s):  
Muhammad Miftahul Munir ◽  
Dian Ahmad Hapidin ◽  
Khairurrijal
Keyword(s):  
High Voltage ◽  
Duty Cycle ◽  
Power Supply ◽  
Pulse Width ◽  
Output Voltage ◽  
Pulse Width Modulation ◽  
Experimental Results ◽  
High Voltage Power Supply ◽  
Electrospinning Technique ◽  
The World

Research on nanofiber materials is actively done around the world today. Various types of nanofibers have been synthesized using an electrospinning technique. The most important component when synthesizing nanofibers using the electrospinning technique is a DC high voltage power supply. Some requirements must be fulfilled by the high voltage power supply, i.e., it must be adjustable and its output voltage reaches tens of kilovolts. This paper discusses the design and development of a high voltage power supply using a diode-split transformer (DST)-type high voltage flyback transformer (HVFBT). The DST HVFBT was chosen because of its simplicity, compactness, inexpensiveness, and easiness of finding it. A pulse-width modulation (PWM) circuit with controlling frequency and duty cycle was fed to the DST HVFBT. The high voltage power supply was characterized by the frequency and duty cycle dependences of its output voltage. Experimental results showed that the frequency and duty cycle affect the output voltage. The output voltage could be set from 1 to 18 kV by changing the duty cycle. Therefore, the nanofibers could be synthesized by employing the developed high voltage power supply.

Application of Particle Swarm Optimization Based on Support Vector Machine in Multi-Objective Structure Optimization

2012 ◽  
Vol 201-202 ◽  
pp. 283-286
Author(s):  
Chen Yang Chang ◽  
Jing Mei Zhai ◽  
Qin Xiang Xia ◽  
Bin Cai
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Structure Optimization ◽  
Design Parameters ◽  
Support Vector ◽  
Swarm Optimization ◽  
Multi Objective

Aiming at addressing optimization problems of complex mathematical model with large amount of calculation, a method based on support vector machine and particle swarm optimization for structure optimization design was proposed. Support Vector Machine (SVM) is a powerful computational tool for problems with nonlinearity and could establish approximate structures model. Grey relational analysis was utilized to calculate the coefficient between target parameters in order to change the multi-objective optimization problem into a single objective one. The reconstructed models were solved by Particle Swam Optimization (PSO) algorithm. A slip cover at medical treatment was adopted as an example to illustrate this methodology. Appropriate design parameters were selected through the orthogonal experiment combined with ANSYS. The results show this methodology is accurate and feasible, which provides an effective strategy to solve complex optimization problems.

Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

2008 ◽  
Vol 12 (3) ◽  
pp. 284-289 ◽  
Author(s):  
Jenn-Long Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Social Learning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Design Parameters ◽  
Search Process ◽  
Swarm Optimization ◽  
Learning Rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

scholarly journals Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xianfu Cheng ◽  
Yuqun Lin
Keyword(s):  
Sensitivity Analysis ◽  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Sensitivity ◽  
Suspension System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
The Mean

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

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