Abstract
The goal of this investigation is to determine the advantages of using genetic algorithms in computer-aided design as applied to inductors. These advantages are exploited in design problems with a number of specifications and constraints, as encountered in power electronics during practical inductor design. The design tool should be able to select components, such as cores and wires, from databases of available components, and evaluate these choices based on the components’ characteristic data read from a database of manufacturers’ data-sheets. The proposed design must always be practically realizable, as close to the desired specifications as possible and within any specified constraints.
Highlights
6.2.1 Genetic algorithmThe genetic algorithm’s goal is to improve a population of solutions, usually for the purpose of optimisation.Advantages of genetic algorithms above other iterative or mathematical techniques are: the problem may be unpredictable, it may be discontinuous, the problem may be time variant.6.2.2 Inductor designMagnetic components for power electronic applications, like inductors and transformers are rarely available as complete components, but are constructed as required, by winding a number of turns of a certain conductor size on a core.When an inductor is required in a power electronic circuit it must be designed to achieve the inductance, resistance, operating frequency and current capability for the specific application
The iterative nature of this process is clear and the design solution provided in this way is still not optimised in any way. Such optimisation to minimize losses for example will require further iterations. From this background the use of genetic algorithms is attractive because of their ability to optimise for multiple constraints
When genetic algorithms are used in practical applications, the process starts by generating random solutions to the problem
Summary
Genetiese algoritmes is ’n relatief jong programmeringstegniek, wat onder die kategorie van evolusiegebaseerde programmeringstegnieke val. Ontwerpsprobleme soos effektiewe optimalisering onder meervoudige beperkings raak wiskundig ingewikkeld as nielineêre en diskontinue komponentkarakteristieke in ag geneem word. Dikwels word die ervaring van die menslike ontwerper hier benodig en kan die ontwerpsproses nie direk geoutomatiseer word nie. Met die bogenoemde probleme in ag geneem moet die rekenaarsteun-programmatuur met ’n ontwerp vorendag kom wat nie net werk nie, maar ook die beste voldoen aan die verlangde spesifikasies binne gegewe begrensings. Die genetiese algoritme se doel is om ontwerpvoorstelle te genereer wat deur ’n kostefunksie getoets word teen die spesifikasies en beperkings waaraan die ontwerp moet voldoen. Die kostefunksie vervat al die relevante wiskundige en heuristiese reëls benodig vir die evaluering van die ontwerpvoorstelle
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
