Modelling of induction motors for system faults and transient stability studies

The research carried out in this thesis was mainly concerned with the effects of large induction motors and their transient performance in power systems. Computer packages using the three phase co-ordinate frame of reference were developed to simulate the induction motor transient performance. A technique using matrix algebra was developed to allow extension of the three phase co-ordinate method to analyse asymmetrical and symmetrical faults on both sides of the three phase delta-star transformer which is usually required when connecting large induction motors to the supply system. System simulation, applying these two techniques, was used to study the transient stability of a power system. The response of a typical system, loaded with a group of large induction motors, two three-phase delta-star transformers, a synchronous generator and an infinite system was analysed. The computer software developed to study this system has the advantage that different types of fault at different locations can be studied by simple changes in input data. The research also involved investigating the possibility of using different integrating routines such as Runge-Kutta-Gill, RungeKutta-Fehlberg and the Predictor-Corrector methods. The investigation enables the reduction of computation time, which is necessary when solving the induction motor equations expressed in terms of the three phase variables. The outcome of this investigation was utilised in analysing an introductory model (containing only minimal control action) of an isolated system having a significant induction motor load compared to the size of the generator energising the system.

Iterative subspace analysis based on feature line distance

Nearest feature line-based subspace analysis is first proposed in this paper. Compared with conventional methods, the newly proposed one brings better generalization performance and incremental analysis. The projection point and feature line distance are expressed as a function of a subspace, which is obtained by minimizing the mean square feature line distance. Moreover, by adopting stochastic approximation rule to minimize the objective function in a gradient manner, the new method can be performed in an incremental mode, which makes it working well upon future data. Experimental results on the FERET face database and the UCI satellite image database demonstrate the effectiveness.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

Conformational analysis and dielectric relaxation of polycarbosilanes and related materials

The conformational characteristics of poly(dimethylsilmethylene), poly(dimethylsilethene), poly(dimethylsilethane) and a related material, poly(2,2,5,5-tetramethyl-1-oxa-2,5-disilapentane), have been investigated using the method of molecular mechanics. In this method, a quantitative analysis of the factors affecting the nature and magnitude of the bond rotation potentials governing their conformational behaviour has been undertaken. Along with their structural data, the results obtained were employed to calculate a variety of conformationally-dependent properties for these polymers, including the characteristic ratio, the dipole moment ratio and the mean-square radius of gyration. In addition, the dielectric relaxation behaviour of two samples of poly(2,2,5,5-tetramethyl-1-oxa-2,5-disilapentane) with molar masses Mw = 28000 and Mw = 46000 respectively, have been studied as a function of temperature (179K-205K) and frequency (100-105Hz). Activation energies for the α-relaxation process and Davidson-Cole empirical distribution factors have been calculated.

Statnote 25: Stepwise multiple regression

An investigator may also wish to select a small subset of the X variables which give the best prediction of the Y variable. In this case, the question is how many variables should the regression equation include? One method would be to calculate the regression of Y on every subset of the X variables and choose the subset that gives the smallest mean square deviation from the regression. Most investigators, however, prefer to use a ‘stepwise multiple regression’ procedure. There are two forms of this analysis called the ‘step-up’ (or ‘forward’) method and the ‘step-down’ (or ‘backward’) method. This Statnote illustrates the use of stepwise multiple regression with reference to the scenario introduced in Statnote 24, viz., the influence of climatic variables on the growth of the crustose lichen Rhizocarpon geographicum (L.)DC.

Two-section fiber optic Raman polarizer

We report on a theoretical study of polarization impairments in periodically spun fiber Raman amplifiers. Based on the Stochastic Generator approach we have derived averaged equations to calculate polarization dependent gain and mean-square gain fluctuations. We show that periodically spun fiber can work as a Raman polarizer but it suffers from increased polarization dependent gain and gain fluctuations. Unlike this, application of a depolarizer can result in suppression of polarization dependent gain and gain fluctuations. We demonstrate that it is possible to design a new fiber Raman polarizer by combining a short fiber without spin and properly chosen parameters and a long periodically spun fiber. This polarizer provides almost the same polarization pulling for all input signal states of polarization and so has very small polarization dependent gain.

Analysis of carrier phase extraction methods in 112-Gbit/s NRZ-PDM-QPSK coherent transmission system

We present a comparative analysis on three carrier phase extraction approaches, including a one-tap normalized least mean square method, a block-average method, and a Viterbi-Viterbi method, in coherent transmission system considering equalization enhanced phase noise. © OSA 2012.

Two-section fiber optic Raman polarizer for high-speed transmission systems

We report on a theoretical study of polarization impairments in periodically spun fiber Raman amplifiers. Based on the Stochastic Generator approach we have derived equations for polarization dependent gain and mean-square gain fluctuations. We show that periodically spun fiber can work as a Raman polarizer but it suffers from increased polarization dependent gain and gain fluctuations. Unlike this, application of a depolarizer can result in suppression of polarization dependent gain and gain fluctuations. We demonstrate that it is possible to design a new fiber Raman polarizer by combining a short fiber without spin and properly chosen parameters and a long periodically spun fiber. This polarizer provides almost the same polarization pulling for all input signal states of polarization and so have very small polarization dependent gain. The obtained results can be used in high-speed fiber optic communication for design of quasi-isotropic spatially and spectrally transparent media with increased Raman gain. © 2011 IEEE.