Design of distribution transformers based on a knowledge-based system and 2D finite elements

Hernández, C.; Arjona, M. A.

doi:10.1016/j.finel.2007.01.004

Cited by 20 publications

(17 citation statements)

References 6 publications

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“…Farinha et al (2005), for example, suggest that we are "far from taking full advantage of knowledge based systems" and proceed to describe three prototype applications in civil engineering. The only other study that uses KBS is by Hernández & Arjona (2007) who combine finite element models and heuristics to develop an aid for designing transformers.…”

Section: Kbs In Designmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Kbs In Designmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…The electrical losses generated within a transformer are: winding (dc and eddy), core and stray losses; the latter are neglected because its small numerical values. A 75 kVA, 13 200/220 V, delta-wye oil-filled transformer was used in this work [16,17]. The induced eddy-current loss was calculated by simulating the transformer load and no-load test that can be achieved by an axi-symmetric FE model [16,18].…”

Section: Electromagnetic Field Modelmentioning

confidence: 99%

“…A 75 kVA, 13 200/220 V, delta-wye oil-filled transformer was used in this work [16,17]. The induced eddy-current loss was calculated by simulating the transformer load and no-load test that can be achieved by an axi-symmetric FE model [16,18]. The governing equation for the axi-symmetric model is given by the diffusion equation (1) [18,19] ∂ ∂z n r…”

Section: Electromagnetic Field Modelmentioning

confidence: 99%

“…Finally, the FE simulation of the no-load test allows determining the core loss, which is also needed in the thermal -fluid model. To compute the no-load condition, the solution of the 2D Cartesian non-linear diffusion equation (3) coupled to an external electric circuit is carried out as explained in [16]. The FE discretisation of (2) and the external circuit equations is expressed by (4)…”

Section: Electromagnetic Field Modelmentioning

confidence: 99%

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Thermal–fluid transient two-dimensional characteristic-based-split finite-element model of a distribution transformer

Arjona

Ovando-Martinez

Hernández

2012

IET Electr. Power Appl.

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This study presents a two-dimensional (2D) finite-element (FE) linear model for predicting the thermal and fluid dynamics of an oil-filled distribution transformer. The model is based on the solution of the Navier-Stokes and Maxwell equations which are weakly coupled. Two electromagnetic FE models are used to calculate the load and no load losses of the transformer. The thermal -fluid equations are solved using first-order 2D FE and the characteristic-based-split (CBS) scheme. The CBS is used for the first time in the thermal fluid modelling of a transformer thereby is the proposed contribution of this study. The CBS algorithm is applicable to all ranges of flow and gives the same results as specialised methods such as the Petrov -Galerkin and Taylor -Galerkin methods. A natural convection analysis for an incompressible Newtonian flow in a distribution transformer is presented. Convection, radiation and symmetry boundary conditions are taken into account in the model. The model predictions are compared against those obtained with a commercial software and good agreement was obtained. The proposed model was programmed in C language.

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“…For instance, Sellgren developed a framework for simulation driven design [9], in which simulation models were extracted based on the CAD-model relationships. Chapman and Pinfold described how to use KBE and FEA for the design automation of a car body [3], and a system was presented by Hernández et al that automatically designs distribution transformers using FEM automatically [4]. The design process of different jet engine components has also been the subject for design automation using KBE (or KEE) integrated with FEA [1,7].…”

Section: Introductionmentioning

confidence: 99%

A Feature and Script Based Integration of CAD and FEA to Support Design of Variant Rich Products

Johansson

2014

Computer-Aided Design and Applications

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The focus of the research presented in this article has been an integration of a CADsystem and a FEA pre-processor to automatically develop a complete FEA-models in order to make simulation based design possible. The article presents a prototype system that was developed to automate the simulation of the behavior of ski-racks mounted on cars during collision. This type of simulations requires mesh models containing structured mesh, an issue solved in the presented system and that is presented in the article. It is also shown how to make it possible to introduce contacts, loads, constraints, and other FEM-properties based on CAD-geometry.

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Design of distribution transformers based on a knowledge-based system and 2D finite elements

Cited by 20 publications

References 6 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Thermal–fluid transient two-dimensional characteristic-based-split finite-element model of a distribution transformer

A Feature and Script Based Integration of CAD and FEA to Support Design of Variant Rich Products

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