Transmission planning in a deregulated environment

The worldwide trend for the deregulation of the electricity generation and transmission industries has led to dramatic changes in system operation and planning procedures. The optimum approach to transmission-expansion planning in a deregulated environment is an open problem especially when the responsibilities of the organisations carrying out the planning work need to be addressed. To date there is a consensus that the system operator and network manager perform the expansion planning work in a centralised way. However, with an increasing input from the electricity market, the objectives, constraints and approaches toward transmission planning should be carefully designed to ensure system reliability as well as meeting the market requirements. A market-oriented approach for transmission planning in a deregulated environment is proposed. Case studies using the IEEE 14-bus system and the Australian national electricity market grid are performed. In addition, the proposed method is compared with a traditional planning method to further verify its effectiveness.

Development of a cell culture method for quantal assay of strain I-2 of Newcastle disease virus

Repeated titrations of strains of Newcastle disease virus (NDV) are more conveniently undertaken in cell cultures rather than in embryonated eggs. This is relatively easy with mesogenic and velogenic strains that are cytopathic to various cell lines, but is difficult with avirulent Australian isolates that are poorly cytopathic. Strain V4 for example has been shown to be pathogenic iin vitro only to of chicken embryo liver cells. Strain 1-2 was reported to produce cytopathic effect (CPE) on chicken embryo kidney (CEK) cells. The present studies confirmed this observation and developed a quantal assay. CEK cells infected with strain 1-2 developed CPE characterized by degeneration, rounding, granularity and vacuolation, and the formation of synctia. End points were readily established by microscopic examination of fixed and stained cells. In virus infectivity studies on strain 1-2, where multiple titrations are required and where large numbers of samples are used, titration using CEK cell grown in microtitre plates is recommended. Such studies may not be feasible in embryonated eggs.

A genetic algorithm based method for deregulated electricity market dispatch

In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.

A real options based method for power system planning

A deregulated electricity market is characterized with uncertainties, with both long and short terms. As one of the major long term planning issues, the transmission expansion planning (TEP) is aiming at implementing reliable and secure network support to the market participants. The TEP covers two major issues: technical assessment and financial evaluations. Traditionally, the net present value (NPV) method is the most accepted for financial evaluations, it is simple to conduct and easy to understand. Nevertheless, TEP in a deregulated market needs a more dynamic approach to incorporate a project's management flexibility, or the managerial ability to adapt in response to unpredictable market developments. The real options approach (ROA) is introduced here, which has clear advantage on counting the future course of actions that investors may take, with understandable results in monetary terms. In the case study, a Nordic test system has been testified and several scenarios are given for network expansion planning. Both the technical assessment and financial evaluation have been conducted in the case study.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.

Adaptive Signal Processing for Power Line Communications

This thesis represents a significant part of the research activity conducted during the PhD program in Information Technologies, supported by Selta S.p.A, Cadeo, Italy, focused on the analysis and design of a Power Line Communications (PLC) system. In recent times the PLC technologies have been considered for integration in Smart Grids architectures, as they are used to exploit the existing power line infrastructure for information transmission purposes on low, medium and high voltage lines. The characterization of a reliable PLC system is a current object of research as well as it is the design of modems for communications over the power lines. In this thesis, the focus is on the analysis of a full-duplex PLC modem for communication over high-voltage lines, and, in particular, on the design of the echo canceller device and innovative channel coding schemes.

Jobs, Unemployment, and Macroeconomic Transmission

Measuring Job Openings: Evidence from Swedish Plant Level Data. In modern macroeconomic models “job openings'' are a key component. Thus, when taking these models to the data we need an empirical counterpart to the theoretical concept of job openings. To achieve this, the literature relies on job vacancies measured either in survey or register data. Insofar as this concept captures the concept of job openings well we should see a tight relationship between vacancies and subsequent hires on the micro level. To investigate this, I analyze a new data set of Swedish hires and job vacancies on the plant level covering the period 2001-2012. I find that vacancies contain little power in predicting hires over and above (i) whether the number of vacancies is positive and (ii) plant size. Building on this, I propose an alternative measure of job openings in the economy. This measure (i) better predicts hiring at the plant level and (ii) provides a better fitting aggregate matching function vis-à-vis the traditional vacancy measure. Firm Level Evidence from Two Vacancy Measures. Using firm level survey and register data for both Sweden and Denmark we show systematic mis-measurement in both vacancy measures. While the register-based measure on the aggregate constitutes a quarter of the survey-based measure, the latter is not a super-set of the former. To obtain the full set of unique vacancies in these two databases, the number of survey vacancies should be multiplied by approximately 1.2. Importantly, this adjustment factor varies over time and across firm characteristics. Our findings have implications for both the search-matching literature and policy analysis based on vacancy measures: observed changes in vacancies can be an outcome of changes in mis-measurement, and are not necessarily changes in the actual number of vacancies. Swedish Unemployment Dynamics. We study the contribution of different labor market flows to business cycle variations in unemployment in the context of a dual labor market. To this end, we develop a decomposition method that allows for a distinction between permanent and temporary employment. We also allow for slow convergence to steady state which is characteristic of European labor markets. We apply the method to a new Swedish data set covering the period 1987-2012 and show that the relative contributions of inflows and outflows to/from unemployment are roughly 60/30. The remaining 10\% are due to flows not involving unemployment. Even though temporary contracts only cover 9-11\% of the working age population, variations in flows involving temporary contracts account for 44\% of the variation in unemployment. We also show that the importance of flows involving temporary contracts is likely to be understated if one does not account for non-steady state dynamics. The New Keynesian Transmission Mechanism: A Heterogeneous-Agent Perspective. We argue that a 2-agent version of the standard New Keynesian model---where a ``worker'' receives only labor income and a “capitalist'' only profit income---offers insights about how income inequality affects the monetary transmission mechanism. Under rigid prices, monetary policy affects the distribution of consumption, but it has no effect on output as workers choose not to change their hours worked in response to wage movements. In the corresponding representative-agent model, in contrast, hours do rise after a monetary policy loosening due to a wealth effect on labor supply: profits fall, thus reducing the representative worker's income. If wages are rigid too, however, the monetary transmission mechanism is active and resembles that in the corresponding representative-agent model. Here, workers are not on their labor supply curve and hence respond passively to demand, and profits are procyclical.

Adaptive back-propagation in on-line learning of multilayer networks

An adaptive back-propagation algorithm is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

Using ancillary statistics in on-line learning algorithms

Neural networks are usually curved statistical models. They do not have finite dimensional sufficient statistics, so on-line learning on the model itself inevitably loses information. In this paper we propose a new scheme for training curved models, inspired by the ideas of ancillary statistics and adaptive critics. At each point estimate an auxiliary flat model (exponential family) is built to locally accommodate both the usual statistic (tangent to the model) and an ancillary statistic (normal to the model). The auxiliary model plays a role in determining credit assignment analogous to that played by an adaptive critic in solving temporal problems. The method is illustrated with the Cauchy model and the algorithm is proved to be asymptotically efficient.

Globally optimal parameters for on-line learning in multilayer neural networks

We present a framework for calculating globally optimal parameters, within a given time frame, for on-line learning in multilayer neural networks. We demonstrate the capability of this method by computing optimal learning rates in typical learning scenarios. A similar treatment allows one to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule as well as to compare different training methods.

Globally optimal on-line learning rules for multi-layer neural networks

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This rule maximizes the total reduction in generalization error over the whole learning process. A simple example demonstrates that the locally optimal rule, which maximizes the rate of decrease in generalization error, may perform poorly in comparison.