Particle swarm optimization of a multi-coil transverse flux induction heating system

2010 ◽  
Author(s):  
P. Alotto ◽  
A. Spagnolo ◽  
B. Paya
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Heating ◽  
Particle Swarm ◽  
Heating System ◽  
Swarm Optimization ◽  
Transverse Flux ◽  
Induction Heating System

scholarly journals Velocity-Controlled Particle Swarm Optimization (PSO) and Its Application to the Optimization of Transverse Flux Induction Heating Apparatus

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 487 ◽  
Author(s):  
Youhua Wang ◽  
Bin Li ◽  
Liuxia Yin ◽  
Jiancheng Wu ◽  
Shipu Wu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Temperature Distribution ◽  
Induction Heating ◽  
Particle Swarm ◽  
Support Vector ◽  
Uniform Temperature ◽  
Swarm Optimization ◽  
Uniform Temperature Distribution ◽  
Transverse Flux ◽  
Better Than

The main disadvantage of transverse flux induction heating (TFIH) is its resulting non-uniform temperature distribution on the surface of the strip at the inductor outlet. For obtaining a uniform temperature distribution, an improved particle swarm optimization (PSO) named velocity-controlled PSO (VCPSO) is proposed and applied to optimize this problem. Support vector machine (SVM) is adopted to establish a regression model to replace the complex and time-consuming coupling calculation process involved in TFIH problem. Simulation results of several test functions show that VCPSO performs much better than standard PSO (SPSO). Moreover, based on the existing research and experiments, the application of VCPSO combined with SVM to the TFIH problem achieves satisfactory results.

Particle Swarm Optimization in Image Processing of Power Flow Learning Distribution

2020 ◽  
Author(s):  
Chuanchao Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Power Flow ◽  
Integrated Control ◽  
Particle Swarm ◽  
Heating System ◽  
Swarm Optimization ◽  
Power Adjustment ◽  
The Mathematical Model ◽  
Tie Line

Abstract In order to realize the coordination and integration optimization of the power system itself, this paper constructed the mathematical model of the hybrid power system and solved the multi-objective optimization problem of the heating system through the optimized particle swarm optimization algorithm. Based on the back-to-back VSC-HVDC grid-connected composite system, this paper studied the integrated control strategy of the device to achieve the simultaneous parallel and tie line currents. At the same time, this paper simplified and improved the proposed disassembly criteria for grid-connected composite devices and integrated them into the grid-connected composite device. In addition, on this basis, the integrated control of the three functions of de-listing, juxtaposition and tie line power adjustment of the same device was further studied. Simulation studies show that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.

scholarly journals Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Meiping Wang ◽  
Qi Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Correlation Analysis ◽  
Support Vector Regression ◽  
Influencing Factors ◽  
Heat Supply ◽  
Particle Swarm ◽  
Heating System ◽  
Support Vector ◽  
Swarm Optimization ◽  
Input Variables

We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

scholarly journals Particle swarm optimization in image processing of power flow learning distribution

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Chuanchao Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Power Flow ◽  
Integrated Control ◽  
Particle Swarm ◽  
Heating System ◽  
Swarm Optimization ◽  
Power Adjustment ◽  
The Mathematical Model ◽  
Tie Line

AbstractIn order to realize the coordination and integration optimization of the power system itself, this paper constructed the mathematical model of the hybrid power system and solved the multi-objective optimization problem of the heating system through the optimized particle swarm optimization algorithm. Based on the back-to-back VSC-HVDC grid-connected composite system, this paper studied the integrated control strategy of the device to achieve the simultaneous parallel and tie line currents. At the same time, this paper simplified and improved the proposed disassembly criteria for grid-connected composite devices and integrated them into the grid-connected composite device. In addition, on this basis, the integrated control of the three functions of de-listing, juxtaposition and tie line power adjustment of the same device was further studied. Simulation studies show that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.

scholarly journals Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Rong-Jiang Ma ◽  
Nan-Yang Yu ◽  
Jun-Yi Hu
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Life Cycle ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Heating System ◽  
Pso Algorithm ◽  
System Planning ◽  
Swarm Optimization

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

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