Multi-level design of an isolation transformer using collaborative optimization

Abstract

Purpose -The purpose of this paper is to present an application of a multidisciplinary multi-level design optimization methodology for the optimal design of a complex device from the field of electrical engineering throughout discipline-based decomposition. The considered benchmark is a single-phase low voltage safety isolation transformer. Design/methodology/approach -The multidisciplinary optimization of a safety isolation transformer is addressed within this paper. The bi-level collaborative optimization (CO) strategy is employed to coordinate the optimization of the different disciplinary analytical models of the transformer (no-load and full-load electromagnetic models and thermal model). The results represent the joint decision of the three distinct disciplinary optimizers involved in the design process, under the coordination of the CO's master optimizer. In order to validate the proposed approach, the results are compared to those obtained using a classical single-level optimization method -sequential quadratic programming -carried out using a multidisciplinary feasible formulation for handling the evaluation of the coupling model of the transformer. Findings -Results show a good convergence of the CO process with the analytical modeling of the transformer, with a reduced number of coordination iterations. However, a relatively important number of disciplinary models evaluations were required by the local optimizers. Originality/value -The CO multi-level methodology represents a new approach in the field of electrical engineering. The advantage of this approach consists in that it integrates decisions from different teams of specialists within the optimal design process of complex systems and all exchanges are managed within a unique coordination process.

Mono and bi-Level optimization architectures for powertrain design

“…Indeed, working on the optimization structure with different formulations can lead to very different results, convergence quality, or calculation time (Cafieri et al 2013) (Berbecea et al 2014). For the system studied, the new proposed architecture consists in optimizing the control and the design together when both objectives are compatible.…”

“…The originality of this work is to confront the natural problem formulation with a new modified structure, in order to understand when and why the problem can be transformed and what the benefits are. Indeed, working on the optimization structure with different formulations can lead to very different results, convergence quality, or calculation time (Cafieri et al 2013) (Berbecea et al 2014). For the system studied, the new proposed architecture consists in optimizing the control and the design together when both objectives are compatible.…”