A general design method for electric machines using magnetic circuit model considering the flux saturation problem

This paper proposes an analytical approach that is generalized for the design of various types of electric machines based on a physical magnetic circuit model. Conventional approaches have been used to predict the behavior of electric machines but have limitations in accurate flux saturation analysis and hence machine dimensioning at the initial design stage. In particular, magnetic saturation is generally ignored or compensated by correction factors in simplified models since it is difficult to determine the flux in each stator tooth for machines with any slot-pole combinations. In this paper, the flux produced by stator winding currents can be calculated accurately and rapidly for each stator tooth using the developed model, taking saturation into account. This aids machine dimensioning without the need for a computationally expensive finite element analysis (FEA). A 48-slot machine operated in induction and doubly-fed modes is used to demonstrate the proposed model. FEA is employed for verification.

High Frequency Acousto-electric Single Photon Source

We propose a single optical photon source for quantum cryptography based on the acousto-electric effect. Surface acoustic waves (SAWs) propagating through a quasi-one-dimensional channel have been shown to produce packets of electrons which reside in the SAW minima and travel at the velocity of sound. In our scheme these electron packets are injected into a p-type region, resulting in photon emission. Since the number of electrons in each packet can be controlled down to a single electron, a stream of single (or N) photon states, with a creation time strongly correlated with the driving acoustic field, should be generated.

High temperature direct double side cooled inverter module for hybrid electric vehicle application

In this paper a novel approach to the design and fabrication of a high temperature inverter module for hybrid electrical vehicles is presented. Firstly, SiC power electronic devices are considered in place of the conventional Si devices. Use of SiC raises the maximum practical operating junction temperature to well over 200°C, giving much greater thermal headroom between the chips and the coolant. In the first fabrication, a SiC Schottky barrier diode (SBD) replaces the Si pin diode and is paired with a Si-IGBT. Secondly, double-sided cooling is employed, in which the semiconductor chips are sandwiched between two substrate tiles. The tiles provide electrical connections to the top and the bottom of the chips, thus replacing the conventional wire bonded interconnect. Each tile assembly supports two IGBTs and two SBDs in a half-bridge configuration. Both sides of the assembly are cooled directly using a high-performance liquid impingement system. Specific features of the design ensure that thermo-mechanical stresses are controlled so as to achieve long thermal cycling life. A prototype 10 kW inverter module is described incorporating three half-bridge sandwich assemblies, gate drives, dc-link capacitance and two heat-exchangers. This achieves a volumetric power density of 30W/cm3.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.