Pre‐sizing methodology of DC–AC converters using a virtual prototyping tool applied to sequential multi‐load systems

Abstract

The increasing need for higher efficiency, weight and volume reduction in embedded applications is a starting point of this study which focuses on a pre-sizing methodology of power converters based on virtual prototyping tools to avoid physical prototypes. From a general point of view, the mutualisation of a three-phase inverter with the help of a power sharing system is introduced. The proposed design approach optimises N inverters and a {one inverter + N-power sharing system} topologies based on a generalised modelling and a virtual prototyping tool. The presented results based on a 3 kW-270 V DC bus application show that in addition to the switching frequency and the ambient temperature, the number of sequential loads is also a key of the optimisation parameters. Therefore the proposed methodology based on an optimisation process allows the power designers to compare N inverters and {one inverter + N-power sharing system} as a function of the load number N.

Design methodology for optimising a high insulation voltage insulated gate bipolar transistor gate driver signal transmission function

“…As described in [8], the optimisation process considers a mixed integer programming problem: some variables being continuous and other discrete (n 1 and n 2 ). In the latest references, many applications in the power electronics fields are addressed considering optimisation processes [12][13][14][15][16][17][18][19]. In the case of mixed integer programming problem, GAs are adapted as it was shown in [8,12,13].…”

“…In the latest references, many applications in the power electronics fields are addressed considering optimisation processes [12][13][14][15][16][17][18][19]. In the case of mixed integer programming problem, GAs are adapted as it was shown in [8,12,13]. Therefore the optimisation tool is based on a fast and elitist multi-objective GA: no dominated sorting GA [20].…”

“…The constraints for the optimisation process in the GA are based on those nine variables and are presented in the following list: n 1 , n 2 ɛ{2, 3, 4, 5, 6}; x 1 , x 2 , x 3 , x 4 ɛ [0.2 mm, 1 mm]; C 1 , C 2 ɛ[0.1 nF, 1 nF]; and R out ɛ[1 kΩ, 30 kΩ]. As described in [8], the optimisation process considers a mixed integer programming problem: some variables being continuous and other discrete ( n 1 and n 2 ). In the latest references, many applications in the power electronics fields are addressed considering optimisation processes [12–19]. In the case of mixed integer programming problem, GAs are adapted as it was shown in [8, 12, 13].…”