The method of predicate least squares refinement

Parameters to be determined in a least squares refinement calculation to fit a set of observed data may sometimes usefully be `predicated' to values obtained from some independent source, such as a theoretical calculation. An algorithm for achieving this in a least squares refinement calculation is described, which leaves the operator in full control of the weight that he may wish to attach to the predicate values of the parameters.

Transformers

Review and critical reflection on 'Transformers' as an example of the contemporary blockbuster, exploring questions of genre, aesthetics, representation, and cultural and industrial contexts.

An exploratory survey of Australia's informal venture capitalists : a predicate to theory building in the context of international angel research

In Australia, as it is all over the world, finding and acquiring equity capital is one of the major problems facing entrepreneurs who are starting or growing entrepreneurial ventures. The informal venture capital market, made up of high net worth non-institutional private equity investors (or ‘business Angels’) provides risk capital directly to new and growing businesses and has been shown to be considerably more significant than institutional providers as a source of finance for entrepreneurial businesses. Building upon and comporting with Angel research undertaken overseas, this study generated and evaluated data resulting from an investigation of Australian business Angels which focused upon three primary research questions: (i) Who are Australia's Informal Venture Capitalists (Business Angels)? (ii) How do they behave? (iii) What are their investment criteria? Analysis of answers resulting from a survey of 36 carefully screened respondents produced a descriptive profile, depicted in twelve key graphs, of Australian Angels' identifying characteristics, patterns of investment behaviour and investment criteria. The study initiates Australian Angel research into the developing international continuum of formal Angel research and can serve as the generator of empirically sensible hypotheses for future research and theory development.

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Experimental analysis of harmonic distortions in the excitation currents of double-excited three-phase transformers

This paper shows the results of experimental investigations of three-phase banks composed of single-phase transformers and three-phase three-limb core transformers under simultaneous alternating and direct current excitations, for several winding connections. Harmonic analysis of excitation currents for different de saturation levels is performed.

Distribution transformers modeling with angular displacement: actual values and per unit analysis

In this work a detailed modeling of three-phase distribution transformers aimed at complementing well-known approaches is presented. Thus, incidence of angular displacement and tapping is taken into account in the proposed models, considering both actual values and per unit. The analysis is based on minimal data requirement: solely short-circuit admittance is needed since three-phase transformers are treated as non-magnetically-coupled single-phase transformers. In order to support the proposed methodology, results obtained through laboratory tests are presented.

Using artificial neural networks for identification of electrical losses in transformers during the manufacturing phase

The paper describes a novel neural model to estimate electrical losses in transformer during the manufacturing phase. The network acts as an identifier of structural features on electrical loss process, so that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through experimental data taking into account core losses, copper losses, resistance, current and temperature. The results obtained in the simulations have shown that the developed technique can be used as an alternative tool to make the analysis of electrical losses on distribution transformer more appropriate regarding to manufacturing process. Thus, this research has led to an improvement on the rational use of energy.

A Family of Autoconnected Transformers for 12-and 18-Pulse Converters-Generalization for Delta and Wye Topologies

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)