A series-dynamic-resistor-based converter protection scheme for doubly-fed induction generator during various fault conditions

This paper proposes a new converter protection method, primarily based on a series dynamic resistor (SDR) that avoids the doubly-fed induction generator (DFIG) control being disabled by crowbar protection during fault conditions. A combined converter protection scheme based on the proposed SDR and conventional crowbar is analyzed and discussed. The main protection advantages are due to the series topology when compared with crowbar and dc-chopper protection. Various fault overcurrent conditions (both symmetrical and asymmetrical) are analyzed and used to design the protection in detail, including the switching strategy and coordination with crowbar, and resistance value calculations. PSCAD/EMTDC simulation results show that the proposed method is advantageous for fault overcurrent protection, especially for asymmetrical faults, in which the traditional crowbar protection may malfunction.

Model evaluation and case series data

We discuss aggregation of data from neuropsychological patients and the process of evaluating models using data from a series of patients. We argue that aggregation can be misleading but not aggregating can also result in information loss. The basis for combining data needs to be theoretically defined, and the particular method of aggregation depends on the theoretical question and characteristics of the data. We present examples, often drawn from our own research, to illustrate these points. We also argue that statistical models and formal methods of model selection are a useful way to test theoretical accounts using data from several patients in multiple-case studies or case series. Statistical models can often measure fit in a way that explicitly captures what a theory allows; the parameter values that result from model fitting often measure theoretically important dimensions and can lead to more constrained theories or new predictions; and model selection allows the strength of evidence for models to be quantified without forcing this into the artificial binary choice that characterizes hypothesis testing methods. Methods that aggregate and then formally model patient data, however, are not automatically preferred to other methods. Which method is preferred depends on the question to be addressed, characteristics of the data, and practical issues like availability of suitable patients, but case series, multiple-case studies, single-case studies, statistical models, and process models should be complementary methods when guided by theory development.

Simultaneous ultrafast optical pulse train bursts generation and shaping based on Fourier series developments using superimposed fiber Bragg gratings

We propose an all-fiber method for the generation of ultrafast shaped pulse train bursts from a single pulse based on Fourier Series Developments (FDSs). The implementation of the FSD based filter only requires the use of a very simple non apodized Superimposed Fiber Bragg Grating (S-FBG) for the generation of the Shaped Output Pulse Train Burst (SOPTB). In this approach, the shape, the period and the temporal length of the generated SOPTB have no dependency on the input pulse rate.

Fracture toughness of a series of high chromium abrasion resistant alloys

The fracture properties of a series of alloys containing 15% chromium and 0.8 to 3.4% carbon are investigated using strain fracture toughness testing techniques. The object of the work is to apply a quantitative method of measuring toughness to abrasion resistant materials, which have previously been assessed on an empirical basis; and to examine the relationship between microstructure and K10 in an attempt to improve the toughness of inherently brittle materials. A review of the relevant literature includes discussion of the background to the alloy series under investigation, a survey of the development of fracture mechanics and the emergence of K10 as a toughness parameter. Metallurgical variables such as composition, heat treatment, grain size, and hot working are ???? to relate microstructure to toughness, and fractographic evidence is used to substantiate the findings. The results are applied to a model correlating ductile fracture with plastic strain instability, and the nucleation of voids. Strain induced martensite formation in austenitic structures is analysed in terms of the plastic energy dissipation mechanisms operating at the crack tip. Emphasis is placed on the lower carbon alloys in the series, and a composition put forward to optimise wear resistance and toughness. The properties of established competitive materials are compared to the proposed alloy on a toughness and cost basis.

A variational conjugate gradient method for determining the fluid velocity of a slow viscous flow

The problem considered is that of determining the fluid velocity for linear hydrostatics Stokes flow of slow viscous fluids from measured velocity and fluid stress force on a part of the boundary of a bounded domain. A variational conjugate gradient iterative procedure is proposed based on solving a series of mixed well-posed boundary value problems for the Stokes operator and its adjoint. In order to stabilize the Cauchy problem, the iterations are ceased according to an optimal order discrepancy principle stopping criterion. Numerical results obtained using the boundary element method confirm that the procedure produces a convergent and stable numerical solution.

An iterative method for a Cauchy problem for the heat equation

An iterative method for reconstruction of the solution to a parabolic initial boundary value problem of second order from Cauchy data is presented. The data are given on a part of the boundary. At each iteration step, a series of well-posed mixed boundary value problems are solved for the parabolic operator and its adjoint. The convergence proof of this method in a weighted L2-space is included.

An iterative method for reconstruction of temperature

An iterative method for the reconstruction of a stationary three-dimensional temperature field, from Cauchy data given on a part of the boundary, is presented. At each iteration step, a series of mixed well-posed boundary value problems are solved for the heat operator and its adjoint. A convergence proof of this method in a weighted L 2-space is include

Evaluation of the activity and stability of alkali-doped metal oxide catalysts for application to an intensified method of biodiesel production

A series of alkali-doped metal oxide catalysts were prepared and evaluated for activity in the transesterification of rapeseed oil to biodiesel. Of those evaluated, LiNO3/CaO, NaNO3/CaO, KNO3/CaO and LiNO3/MgO exhibited >90% conversion in a standard 3 h test. There was a clear correlation between base strength and activity. These catalysts appeared to be promising candidates to replace conventional homogeneous catalysts for biodiesel production as the reaction times are low enough to be practical in continuous processes and the preparations are neither prohibitively difficult nor costly. However, metal leaching from the catalyst was detected, and this resulted in some homogeneous activity. This would have to be resolved before these catalysts would be viable for large-scale biodiesel production facilities.

Detection of Logical-and-Probabilistic Correlation in Time Series

An application of the heterogeneous variables system prediction method to solving the time series analysis problem with respect to the sample size is considered in this work. It is created a logical-and-probabilistic correlation from the logical decision function class. Two ways is considered. When the information about event is kept safe in the process, and when it is kept safe in depending process.

Extreme Situations Prediction by MultidimenSional Heterogeneous Time Series Using Logical Decision Functions

* The work is supported by RFBR, grant 04-01-00858-a

About New Pattern Recognition Method for the Universal Program System “Recognition”

In this work the new pattern recognition method based on the unification of algebraic and statistical approaches is described. The main point of the method is the voting procedure upon the statistically weighted regularities, which are linear separators in two-dimensional projections of feature space. The report contains brief description of the theoretical foundations of the method, description of its software realization and the results of series of experiments proving its usefulness in practical tasks.

Series in Mittag-Leffler Functions: Inequalities and Convergent Theorems

MSC 2010: 30A10, 30B10, 30B30, 30B50, 30D15, 33E12

Extraction of Fraud Schemes from Trade Series

2000 Mathematics Subject Classification: 62H30, 62M10, 62M20, 62P20, 94A13.

On a Generalization of a Theorem due to Larcombe on the Sum of a 3F2 Series

MSC 2010: 33C20

Improving the predictability of time series by chaotic parameter estimation

Limited literature regarding parameter estimation of dynamic systems has been identified as the central-most reason for not having parametric bounds in chaotic time series. However, literature suggests that a chaotic system displays a sensitive dependence on initial conditions, and our study reveals that the behavior of chaotic system: is also sensitive to changes in parameter values. Therefore, parameter estimation technique could make it possible to establish parametric bounds on a nonlinear dynamic system underlying a given time series, which in turn can improve predictability. By extracting the relationship between parametric bounds and predictability, we implemented chaos-based models for improving prediction in time series. ^ This study describes work done to establish bounds on a set of unknown parameters. Our research results reveal that by establishing parametric bounds, it is possible to improve the predictability of any time series, although the dynamics or the mathematical model of that series is not known apriori. In our attempt to improve the predictability of various time series, we have established the bounds for a set of unknown parameters. These are: (i) the embedding dimension to unfold a set of observation in the phase space, (ii) the time delay to use for a series, (iii) the number of neighborhood points to use for avoiding detection of false neighborhood and, (iv) the local polynomial to build numerical interpolation functions from one region to another. Using these bounds, we are able to get better predictability in chaotic time series than previously reported. In addition, the developments of this dissertation can establish a theoretical framework to investigate predictability in time series from the system-dynamics point of view. ^ In closing, our procedure significantly reduces the computer resource usage, as the search method is refined and efficient. Finally, the uniqueness of our method lies in its ability to extract chaotic dynamics inherent in non-linear time series by observing its values. ^