Particle Swarm Optimization of a Multi-Coil Transverse Flux Induction Heating System

Abstract

In this paper the optimization of a transverse flux induction heating device by means of a parameter-free, self-adaptive particle swarm optimizer is presented. The novelty of this work is represented by the fact that concentric multi-coil induction heaters have never been optimized before in their full three-dimensional complexity. The main technical challenge of the problem consists in designing a single configuration capable of heating strips of different widths while satisfying geometrical, electrical and heating process constraints. It will be shown that a system consisting of three coils can successfully heat strips of widths between 600 mm and 800 mm with the required uniformity

Decoupling of Currents in Travelling Wave Induction Heating

“…Shorting the idle coil changes the values slightly, but only by 1% -2% up to 40 kHz. The figure also illustrates the mutual inductance defined by Equation (6), where j L is the inductance of each coil and k is the coupling between them, which is approximately 20%. In the real setup, however, the coupling factor is significantly dependent on the resonant frequency.…”

“…These solutions can be divided into zone-control induction heating (ZCIH) and travelling wave induction heating (TWIH). Significant research effort is spent on coupled induction heating systems with the focus on system modelling; [1] [2], power elec-tronics; [3]- [5] as well as electromagnetic design and control, [6] [7] etc.…”

Simulation of induction heating technology for the production of seamless large diameter tees

“…Maximum temperature is 1150 °C and Δ = 100 °C (maximum temperature difference between the outer and the inner surfaces of the pipe) according to the terms of reference. More uniform temperature distribution can be achieved by increasing the heating time [22][23][24].…”

Study on the Uniformity of Temperature Distribution of Transverse Flux Induction Heating Based on a New Magnetic Pole

“…The research process of TFIH technology involves using existing or improved optimization algorithms (such as genetic algorithm, velocity-controlled particle swarm optimization algorithm, etc.) to optimize the design parameters of the TFIH device [14][15][16]. None of the above papers deal with the study of magnetic poles in TFIH.…”