Power Electronic Converters in Low-Voltage Direct Current Distribution – Analysis and Implementation

Over the recent years, smart grids have received great public attention. Many proposed functionalities rely on power electronics, which play a key role in the smart grid, together with the communication network. However, “smartness” is not the driver that alone motivates the research towards distribution networks based on power electronics; the network vulnerability to natural hazards has resulted in tightening requirements for the supply security, set both by electricity end-users and authorities. Because of the favorable price development and advancements in the field, direct current (DC) distribution has become an attractive alternative for distribution networks. In this doctoral dissertation, power electronic converters for a low-voltage DC (LVDC) distribution system are investigated. These include the rectifier located at the beginning of the LVDC network and the customer-end inverter (CEI) on the customer premises. Rectifier topologies are introduced, and according to the LVDC system requirements, topologies are chosen for the analysis. Similarly, suitable CEI topologies are addressed and selected for study. Application of power electronics into electricity distribution poses some new challenges. Because the electricity end-user is supplied with the CEI, it is responsible for the end-user voltage quality, but it also has to be able to supply adequate current in all operating conditions, including a short-circuit, to ensure the electrical safety. Supplying short-circuit current with power electronics requires additional measures, and therefore, the short-circuit behavior is described and methods to overcome the high-current supply to the fault are proposed. Power electronic converters also produce common-mode (CM) and radio-frequency (RF) electromagnetic interferences (EMI), which are not present in AC distribution. Hence, their magnitudes are investigated. To enable comprehensive research on the LVDC distribution field, a research site was built into a public low-voltage distribution network. The implementation was a joint task by the LVDC research team of Lappeenranta University of Technology and a power company Suur-Savon S¨ahk¨o Oy. Now, the measurements could be conducted in an actual environment. This is important especially for the EMI studies. The main results of the work concern the short-circuit operation of the CEI and the EMI issues. The applicability of the power electronic converters to electricity distribution is demonstrated, and suggestions for future research are proposed.

Opportunity Analysis and Category Management as a Base for Effective Indirect Sourcing

Hankintojen johtamisen kirjallisuus korostaa tehokkaan hankinnan olevan käypä keino tehostaa organisaation tulosta kokonaisvaltaisesti. Myös kasvava tietoisuus erityisesti epäsuorista hankintamenetelmistä ja työkaluista toimivat kannustimina tälle tutkimukselle. Tämän Pro Gradu -tutkimuksen päätarkoituksena on rakentaa kokonaisvaltainen ymmärrys epäsuorasta hankinnasta sekä löytää keinoja sen tehostamiseksi. Tutkimuksen tavoitteena on selvittää, miten globaali, monikansal- linen organisaatio voi parantaa kannattavuuttaan epäsuorissa hankinnoissa, sekä mitkä tekijät hankintastrategiassa vaikuttavat siihen. Tutkimus toteutettiin yksittäisenä tapaustutkimuksena suuren globaalin, monikan- sallisen yrityksen työntekijän näkökulmasta, Pääosa datasta pohjautuu vuonna 2015 toteutettuun Opportunity -analyysi projektiin, joka toteutettiin yhteistyössä ulkoisen konsulttifirman kanssa. Osa datasta pohjautuu puolistrukturoituihin haas- tatteluihin organisaation hankintajohtajan kanssa. Datan keruussa hyödynnettiin lisäksi henkilökohtaista havainnointia ja sekundääristä aineistoa organisaatiosta. Tämä Pro Gradu tutkimus on toteutettu kvalitatiivisella otteella, sisältäen joitakin kvantitatiivisia metodin piirteitä.

Modified statistical methods to calculate rare gas interaction potentials

New density functionals representing the exchange and correlation energies (per electron) are employed, based on the electron gas model, to calculate interaction potentials of noble gas systems X2 and XY, where X (and Y) are He,Ne,Ar and Kr, and of hydrogen atomrare gas systems H-X. The exchange energy density functional is that recommended by Handler and the correlation energy density functional is a rational function involving two parameters which were optimized to reproduce the correlation energy of He atom. Application of the two parameter function to other rare gas atoms shows that it is "universal"; i. e. ,accurate for the systems considered. The potentials obtained in this work compare well with recent experimental results and are a significant improvement over those from competing statistical modelS.

Probabilistic Assignments of Identical Indivisible Objects and Uniform Probabilistic Rules.

We consider a probabilistic approach to the problem of assigning k indivisible identical objects to a set of agents with single-peaked preferences. Using the ordinal extension of preferences, we characterize the class of uniform probabilistic rules by Pareto efficiency, strategy-proofness, and no-envy. We also show that in this characterization no-envy cannot be replaced by anonymity. When agents are strictly risk averse von-Neumann-Morgenstern utility maximizers, then we reduce the problem of assigning k identical objects to a problem of allocating the amount k of an infinitely divisible commodity.

Risk Aversion, Intertemporal Substitution, and Option Pricing

This paper develops a general stochastic framework and an equilibrium asset pricing model that make clear how attitudes towards intertemporal substitution and risk matter for option pricing. In particular, we show under which statistical conditions option pricing formulas are not preference-free, in other words, when preferences are not hidden in the stock and bond prices as they are in the standard Black and Scholes (BS) or Hull and White (HW) pricing formulas. The dependence of option prices on preference parameters comes from several instantaneous causality effects such as the so-called leverage effect. We also emphasize that the most standard asset pricing models (CAPM for the stock and BS or HW preference-free option pricing) are valid under the same stochastic setting (typically the absence of leverage effect), regardless of preference parameter values. Even though we propose a general non-preference-free option pricing formula, we always keep in mind that the BS formula is dominant both as a theoretical reference model and as a tool for practitioners. Another contribution of the paper is to characterize why the BS formula is such a benchmark. We show that, as soon as we are ready to accept a basic property of option prices, namely their homogeneity of degree one with respect to the pair formed by the underlying stock price and the strike price, the necessary statistical hypotheses for homogeneity provide BS-shaped option prices in equilibrium. This BS-shaped option-pricing formula allows us to derive interesting characterizations of the volatility smile, that is, the pattern of BS implicit volatilities as a function of the option moneyness. First, the asymmetry of the smile is shown to be equivalent to a particular form of asymmetry of the equivalent martingale measure. Second, this asymmetry appears precisely when there is either a premium on an instantaneous interest rate risk or on a generalized leverage effect or both, in other words, whenever the option pricing formula is not preference-free. Therefore, the main conclusion of our analysis for practitioners should be that an asymmetric smile is indicative of the relevance of preference parameters to price options.

Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors

We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables methods. The validity of the procedures is unaffected by the presence of identification problems or \"weak instruments\", so no detection of such problems is required. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Anderson and Rubin (1949) and Fuller (1987) for different (although related) problems, while the second one is based on splitting the sample. The instrument substitution method uses the instruments directly, instead of generated regressors, in order to test hypotheses about the \"structural parameters\" of interest and build confidence sets. The second approach relies on \"generated regressors\", which allows a gain in degrees of freedom, and a sample split technique. For inference about general possibly nonlinear transformations of model parameters, projection techniques are proposed. A distributional theory is obtained under the assumptions of Gaussian errors and strictly exogenous regressors. We show that the various tests and confidence sets proposed are (locally) \"asymptotically valid\" under much weaker assumptions. The properties of the tests proposed are examined in simulation experiments. In general, they outperform the usual asymptotic inference methods in terms of both reliability and power. Finally, the techniques suggested are applied to a model of Tobin’s q and to a model of academic performance.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Methods to Estimate Dynamic Stochastic General Equilibrium Models

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1 ) Maximum Likelihood, with and without measurement errors and incorporating Bayesian priors, 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) Indirect Inference. Monte Carlo analysis indicates that all procedures deliver reasonably good estimates under the null hypothesis. However, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. GMM and SMM appear to be more robust to misspecification than the alternative procedures. The implications of the stochastic singularity of DSGE models for each estimation method are fully discussed.

Bayesian Analysis for a Theory of Random Consumer Demand: The Case of Indivisible Goods

McCausland (2004a) describes a new theory of random consumer demand. Theoretically consistent random demand can be represented by a \"regular\" \"L-utility\" function on the consumption set X. The present paper is about Bayesian inference for regular L-utility functions. We express prior and posterior uncertainty in terms of distributions over the indefinite-dimensional parameter set of a flexible functional form. We propose a class of proper priors on the parameter set. The priors are flexible, in the sense that they put positive probability in the neighborhood of any L-utility function that is regular on a large subset bar(X) of X; and regular, in the sense that they assign zero probability to the set of L-utility functions that are irregular on bar(X). We propose methods of Bayesian inference for an environment with indivisible goods, leaving the more difficult case of indefinitely divisible goods for another paper. We analyse individual choice data from a consumer experiment described in Harbaugh et al. (2001).

Parametric and Nonparametric Approaches to Price and Tax Reform.

In the analysis of tax reform, when equity is traded off against efficiency, the measurement of the latter requires us to know how tax-induced price changes affect quantities supplied and demanded. in this paper, we present various econometric procedures for estimating how taxes affect demand.

Statistical methods for insurance fraud detection

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Characteristics of Upper Troposphere and Lower Stratosphere in Relation to Asian Summer Monsoon

The present work is an attempt to understand the characteristics of the upper troposphere and lower stratosphere over the Asian summer monsoon region, more specifically over the Indian subcontinent. Mainly three important parameters are taken such as zonal wind, temperature and ozone over the UT/LS of the Asian summer monsoon region. It made a detailed study of its interannual variability and characteristics of theses parameters during the Indian summer monsoon period. Monthly values of zonal wind and temperature from the NCEP/NCAR reanalysis for the period 1960-2002 are used for the present study. Also the daily overpass total ozone data for the 12 Indian stations (from low latitude to high latitudes) from the TOMS Nimbus 7 satellite for the period 1979 to 1992 were also used to understand the total ozone variation over the Indian region. The study reveals that if QBO phases in the stratosphere is easterly or weak westerly then the respective monsoon is found to be DRY or below Normal . On the other hand, if the phase is westerly or weak easterly the respective Indian summer monsoon is noted as a WET year. This connection of stratospheric QBO phases and Indian summer monsoon gives more insight in to the long-term predictions of Indian summer monsoon rainfall. Wavelet analysis and EOF methods are the two advanced statistical techniques used in the present study to explore more information of the zonal wind that from the smaller scale to higher scale variability over the Asian summer monsoon region. The interannual variability of temperature for different stratospheric and tropospheric levels over the Asian summer monsoon region have been studied. An attempt has been made to understand the total ozone characteristics and its interannual variablilty over 12 Indian stations spread from south latitudes to north latitudes. Finally it found that the upper troposphere and lower stratosphere contribute significantly to monsoon variability and climate changes. It is also observed that there exists a link between the stratospheric QBO and Indian summer monsoon

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

Spectral and statistical characteristics of shoaling waves of Alleppey West coast of India

Some investigations on the spectral and statistical characteristics of deep water waves are available for Indian waters. But practically no systematic investigation on the shallow water wave spectral and probabilistic characteristics is made for any part of the Indian coast except for a few restricted studies. Hence a comprehensive study of the shallow water wave climate and their spectral and statistical characteristics for a location (Alleppey) along the southwest coast of India is undertaken based on recorded data. The results of the investigation are presented in this thesis.The thesis comprises of seven chapters

Analysis and Control of Robot Manipulators with Kinematic Redundancy

A closed-form solution formula for the kinematic control of manipulators with redundancy is derived, using the Lagrangian multiplier method. Differential relationship equivalent to the Resolved Motion Method has been also derived. The proposed method is proved to provide with the exact equilibrium state for the Resolved Motion Method. This exactness in the proposed method fixes the repeatability problem in the Resolved Motion Method, and establishes a fixed transformation from workspace to the joint space. Also the method, owing to the exactness, is demonstrated to give more accurate trajectories than the Resolved Motion Method. In addition, a new performance measure for redundancy control has been developed. This measure, if used with kinematic control methods, helps achieve dexterous movements including singularity avoidance. Compared to other measures such as the manipulability measure and the condition number, this measure tends to give superior performances in terms of preserving the repeatability property and providing with smoother joint velocity trajectories. Using the fixed transformation property, Taylor's Bounded Deviation Paths Algorithm has been extended to the redundant manipulators.