Search results

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.

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.

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.

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.

This paper aims to propose a multiobjective branch and bound (MOBB) algorithm with a new criteria for the branching and discarding of nodes based on Pareto dominance and contribution metric.

A multiobjective branch and bound (MOBB) method is presented and applied to the bi-objective combinatorial optimization of a safety transformer. A comparison with exhaustive enumeration and non-dominated sorting genetic algorithm (NSGA2) confirms the solutions.

It appears that MOBB and NSGA2 are both sensitive to their control parameters. The parameters for the MOBB algorithm are the number of starting points and the number of solutions on the relaxed Pareto front. The parameters of NSGA2 are the population size and the number of generations.

The comparison with exhaustive enumeration confirms that the proposed algorithm is able to find the complete set of non-dominated solutions in about 235 times fewer evaluations. As this last method is exact, its confidence level is higher.

The purpose of this paper is to investigate the use of multidisciplinary optimization (MDO) formulations within space‐mapping techniques in order to reduce their computing time.

The aim of this work is to quantify the interest of using MDO formulations within space mapping techniques. A comparison of three MDO formulations is carried out in a short time by using an analytical model of a safety transformer. This comparison reveals the advantage of two formulations in terms of robustness and computing time among the three MDO formulations. Then, the best formulations are investigated within output space mapping, using both analytical and FE models of the transformer.

A major computing time gain equal to 5.5 is achieved using the Individual Disciplinary Feasibility formulation within the output space‐mapping technique in the case of the safety transformer.

The MultiDisciplinary Feasibility formulation is the common formulation used within space‐mapping technique because it is the most conventional way to perform MDO. The originality of this paper is to investigate the Individual Disciplinary Feasibility formulation within output space‐mapping technique in order to allow the parallelization of calculation and to achieve a major reduction of computing time.

The purpose of this paper is to propose a model for assisting in the decision-making process for acquiring a condition monitoring (CM) system for an oil-immersed power transformer in order to improve its maintainability.

The proposed model is based on the analytic hierarchy process. The assessment was performed by pairwise comparisons, and a sensitivity analysis (what-if analysis) was used to identify the implications of changing the criteria weights. In order to select the criteria and alternatives, a search was conducted for the power transformer failure modes, monitored parameters and CM technologies.

The proposed model provides a structured solution for a complex problem: deciding the best combination of technologies for CM of power transformers.

Because the pairwise comparisons were done only by the author, the results may need to be improved with the assessment of more experts. Also, it was done for a specific type of transformer; it might be necessary to customise the alternatives for other cases. Finally, as a future consideration, more levels can be added to the hierarchy to improve the accuracy of the model.

The power transformer is an asset where the most appropriate maintenance strategy for it is condition-based maintenance. In order to improve its maintainability, it is recommendable to improve its testability and diagnosability. For achieving this goal, the maintenance personnel have to decide the best combination of technologies for CM. The methodology developed can assist the decision makers to select the most appropriate cost-benefit strategy.

The paper presents a structured and generic method of selecting the most appropriate CM system for power transformers.

Heavy current bushings passing through steel cover plates and housing walls of power transformers, generators and other large power equipment are thermally hazardous elements of construction and a source of additional power losses. Safety and reliability of such expensive objects and safety of power delivery often depend on the proper design of these elements. In the paper a computer analysis, based on Maxwell equations and analytical representation of electromagnetic field was carried out. Non‐linear permeability of solid steel was considered with the help of analytical approximation. Eddy current losses have been calculated and compared using different methods of calculation and experiments. The method of forecasting possible excessive heating and hot spot with the help of electromagnetic criteria was used. Various constructional means of loss and hot spot reduction were proposed and examined.

This paper compares six reliability-based design optimization (RBDO) approaches dealing with uncertainties for a simple mathematical model and a multidisciplinary optimization problem of a safety transformer to highlight the most effective.

The RBDO and various approaches to calculate the probability of failure are is presented. They are compared in terms of precision and number of evaluations on mathematical and electromagnetic design problems.

The mathematical example shows that the six RBDO approaches have almost the same results except the approximate moment approach that is less accurate. The optimization of the safety transformer highlights that not all the methods can converge to the global solution. Performance measure approach, single-loop approach and sequential optimization and reliability assessment (SORA) method appear to be more stable. Considering both numerical examples, SORA is the most effective method among all RBDO approaches.

The comparison of six RBDO methods on the optimization problem of a safety transformer is achieved for the first time. The comparison in terms of precision and number of evaluations highlights the most effective ones.

This paper aims to present a 3D numerical analysis of the load noise generation associated with large, oil immersed three‐phase power transformers.

After studying the mechanical behavior of the winding structures of transformers, the results of coupled magneto‐mechanical simulations are presented.

An appropriate modeling strategy of the vibratory winding structures of transformers is necessary to reduce complexity and computational resources.

The presented model setup describes a fully transient, 3D coupled magneto‐mechanical simulation of the vibratory winding structure of large power transformers.

The paper describes the simulation of a short circuit of one diode in a three‐phase convertor set connected to a 2 winding transformer. The forcing currents are computed with the circuit simulation method. The circuit — field model is solved with the finite‐element method. In the paper is presented the distribution of flux lines and values of short circuit forces (strains) solved during one period every 15 degrees in the window of the convertor transformer. This approach to dynamic phenomena using the method presented has not yet been applied to short circuit research in transformers.

Life cycle analysis (LCA) is more and more used in the context of electromagnetic product design. But it is often used to check a design solution regarding environmental impacts after technical and economical choices. This paper aims to investigate life cycle impact optimization (LCIO) and compare it with the classical life cycle cost optimization (LCCO).

First, a model of a dry-type transformer using different materials for windings and the magnetic core is presented. LCCO, which is a mixed continuous-discrete, multi-objective technico-economic optimization, is done using both deterministic and genetic algorithms. LCCO results and optimization performances are analyzed, and an LCA is presented for a set of optimal solutions. The final part is dedicated to LCIO, where the paper shows that these optimal solutions are close to those obtained with LCCO.

This paper investigated LCIO using an environmental impacts model that has been introduced in the optimization framework Component Architecture for the Design of Engineering Systems. The paper shows how a mixed continuous-discrete, multi-objective technico-economic optimization can be done using an efficient deterministic optimization algorithm such as Sequential Quadratic Programming. Thanks to the technico-economic-environmental model and the efficient optimization algorithm, both LCCO and LCIO were performed separately and together. It has been shown that optimal solutions are similar, leading to the conclusion that only one modeling is required (economic or environmental) but on the life cycle.

The classical sequential methodology of design is improved here by the use of a model of calculation of the environmental impacts allowing the optimization. This original optimization allowed the authors to show that an analysis of the life cycle from an economic point of view or from an environmental point of view led to quasi-equivalent technical solutions. The key is to take into account the life cycle of the product.