A Synergetic Neural Network-Genetic Scheme for Optimal Transformer Construction

In this paper, a combined neural network and an evolutionary programming scheme is proposed to improve the quality of wound core distribution transformers in an industrial environment by exploiting information derived from both the construction and transformer design phase. In particular, the neural network architecture is responsible for predicting transformer iron losses prior to their assembly, based on several actual core measurements, transformer design parameters and the specific core assembling. A genetic algorithm is applied to estimate the optimal core arrangement, (i.e. the way of core assembling) that yields a set of three-phase transformers of minimal iron losses. The minimization is performed by exploiting information derived from the neural network model resulting in a synergetic neural network-genetic algorithm scheme. After the transformer construction, the prediction accuracy of the neural network model is evaluated. If accuracy is poor, a weight adaptation algorithm is applied to improve the prediction performance. For the weight updating, both the current and the previous network knowledge are taken into account. Application of the proposed neural network-genetic algorithm scheme to our industrial environment indicates a significant reduction in the variation between the actual and the designed transformer iron losses. This further leads to a reduction of the production cost since a smaller safety margin can be used for the transformer design

Design of inductors for power converters operating at intermediate switching frequencies

Magnetic components (i.e. inductors and transformers) are essential elements of modern power converters. However, the design of magnetic components is a complex and iterative process, requiring trade-offs between a large number of parameters and careful consideration of their various interactions. To date, the research that has been conducted in this area has mainly targeted either low or high frequency converter regions of operation, focusing on issues such as material selection, loss modelling and high frequency effects. Consequently, there remains a gap in the body of knowledge regarding optimal design and construction of higher-power (i.e. > 1 kW) magnetic components for converters operating in the intermediate switching frequency range (i.e. 1 kHz-25 kHz). The present-day design methodologies for magnetic components typically provide guidelines for a user to design magnetic components using a basic set of fundamental rules. However, intermediate-frequency magnetic components are more difficult to design because of constraints in the selection of suitable core materials, conductor types and problems of dealing with non-sinusoidal excitation waveform. Strategies which suit other frequency ranges are often used based on a series of assumptions that date back over two decades, many of which are valid only in a lower power higher frequency context. Consequently, direct usage of these techniques for high power intermediate frequency applications can require a significant number of design iterations and even then can result in either an unconstructable design or a poor performance solution. This thesis develops an improved methodology for the design of higher power inductors operating at the intermediate frequency range. It first creates a multivariable optimising type system using an expert system approach that addresses the complexity of the design inter-relationships by iterating and trading-off objectives to achieve a design answer. This stage of the work focuses on the development of a knowledge-based advisory system for design of magnetic components. The second stage is to find the limitations of present design methodologies; it examines why current state-of-the-art design methodologies are not directly applicable to this frequency range by revisiting design principles. The thesis then explores the development of an improved user friendly methodology to suit the development of physically constructible designs for power inductors for converters operating at the intermediate frequency range. The developed strategy uses a 3D graph-based error minimization approach which automatically sweeps across the key design parameters until it converges on the best possible solution. Then it introduces an evaluative comparison between simulation results and experimental implementation in a prototype converter running under full load conditions by performance evaluation of the technique in terms of loss optimisation, temperature rise and the overall dimension of the final design. A significant part of the contributions presented in this thesis have been published in peer reviewed papers, and are identified accordingly as appropriate