Starting scheme for load commutated inverter-fed wound field synchronous machine using an auxiliary low-power voltage source inverter

Conventional thyristor-based load commutated inverter (LCI)-fed wound field synchronous machine operates only above a minimum speed that is necessary to develop enough back emf to ensure commutation. The drive is started and brought up to a speed of around 10-15% by a complex `dc link current pulsing' technique. During this process, the drive have problems such as pulsating torque, insufficient average starting torque, longer starting time, etc. In this regard a simple starting and low-speed operation scheme, by employing an auxiliary low-power voltage source inverter (VSI) between the LCI and the machine terminals, is presented in this study. The drive is started and brought up to a low speed of around 15% using the VSI alone with field oriented control. The complete control is then smoothly and dynamically transferred to the conventional LCI control. After the control transfer, the VSI is turned off and physically disconnected from the main circuit. The advantages of this scheme are smooth starting, complete control of torque and flux at starting and low speeds, less starting time, stable operation, etc. The voltage rating of the required VSI is very low of the order of 10-15%, whereas the current rating is dependent on the starting torque requirement of the load. The experimental results from a 15.8 hp LCI-fed wound field synchronous machine are given to demonstrate the scheme.

Variable-speed wind power generation using doubly fed wound rotor induction machine - A comparison with alternative schemes

In this paper, a wind energy conversion system (WECS) using grid-connected wound rotor induction machine controlled from the rotor side is compared with both fixed speed and variable speed systems using cage rotor induction machine. The comparison is done on the basis of (I) major hardware components required, (II) operating region, and (III) energy output due to a defined wind function using the characteristics of a practical wind turbine. Although a fixed speed system is more simple and reliable, it severely limits the energy output of a wind turbine. In case of variable speed systems, comparison shows that using a wound rotor induction machine of similar rating can significantly enhance energy capture. This comes about due to the ability to operate with rated torque even at supersynchronous speeds; power is then generated out of the rotor as well as the stator. Moreover, with rotor side control, the voltage rating of the power devices and dc bus capacitor bank is reduced. The size of the line side inductor also decreasesd. Results are presented to show the substantial advantages of the doubly fed system.

A model for tool-use traditions in primates: implications for the coevolution of culture and cognition

Inspired by the demonstration that tool-use variants among wild chimpanzees and orangutans qualify as traditions (or cultures), we developed a formal model to predict the incidence of these acquired specializations among wild primates and to examine the evolution of their underlying abilities. We assumed that the acquisition of the skill by an individual in a social unit is crucially controlled by three main factors, namely probability of innovation, probability of socially biased learning, and the prevailing social conditions (sociability, or number of potential experts at close proximity). The model reconfirms the restriction of customary tool use in wild primates to the most intelligent radiation, great apes; the greater incidence of tool use in more sociable populations of orangutans and chimpanzees; and tendencies toward tool manufacture among the most sociable monkeys. However, it also indicates that sociable gregariousness is far more likely to produce the maintenance of invented skills in a population than solitary life, where the mother is the only accessible expert. We therefore used the model to explore the evolution of the three key parameters. The most likely evolutionary scenario is that where complex skills contribute to fitness, sociability and/or the capacity for socially biased learning increase, whereas innovative abilities (i.e., intelligence) follow indirectly. We suggest that the evolution of high intelligence will often be a byproduct of selection on abilities for socially biased learning that are needed to acquire important skills, and hence that high intelligence should be most common in sociable rather than solitary organisms. Evidence for increased sociability during hominin evolution is consistent with this new hypothesis. (C) 2003 Elsevier Science Ltd. All rights reserved.

Comparison of CVD and MOCVD-grown Al2O3 coatings in the performance of cemented carbide cutting tool inserts

Low-pressure MOCVD, with tris(2,4 pentanedionato)aluminum(III) as the precursor, was used in the present investigation to coat alumina on to cemented carbide cutting tools. To evaluate the MOCVD process, the efficiency in cutting operations of MOCVD-coated tools was compared with that of tools coated using the industry-standard CVD process.Three multilayer cemented carbide cutting tool inserts, viz., TiN/TiC/WC, CVD-coated Al2O3 on TiN/TiC/WC, and MOCVD-coated Al2O3 on TiN/TiC/WC, were compared in the dry turning of mild steel. Turning tests were conducted for cutting speeds ranging from 14 to 47 m/min, for a depth of cut from 0.25 to 1 mm, at the constant feed rate of 0.2 mm/min. The axial, tangential, and radial forces were measured using a lathe tool dynamometer for different cutting parameters, and the machined work pieces were tested for surface roughness. The results indicate that, in most of the cases examined, the MOCVD-coated inserts produced a smoother surface finish, while requiring lower cutting forces, indicating that MOCVD produces the best-performing insert, followed by the CVD-coated one. The superior performance of MOCVD-alumina is attributed to the co-deposition of carbon with the oxide, due to the very nature of the precursor used, leading to enhanced mechanical properties for cutting applications in harsh environment.

Interference Alignment as a Tool in Network Coding as Applied to Distributed Storage

In this paper, we outline an approach to the task of designing network codes in a non-multicast setting. Our approach makes use of the concept of interference alignment. As an example, we consider the distributed storage problem where the data is stored across the network in n nodes and where a data collector can recover the data by connecting to any k of the n nodes and where furthermore, upon failure of a node, a new node can replicate the data stored in the failed node while minimizing the repair bandwidth.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Multiclass Core Vector Machine

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we propose a Multiclass Core Vector Machine(MCVM). Here we formulate the multiclass SVM problem as a Quadratic Programming(QP) problem defining an SVM with vector valued output. This QP problem is then solved using the CVM technique to achieve scalability to handle large data sets. Experiments done with several large synthetic and real world data sets show that the proposed MCVM technique gives good generalization performance as that of SVM at a much lesser computational expense. Further, it is observed that MCVM scales well with the size of the data set.

INTACTE: An Interconnect Area, Delay, and Energy Estimation Tool for Microarchitectural Explorations

Prior work on modeling interconnects has focused on optimizing the wire and repeater design for trading off energy and delay, and is largely based on low level circuit parameters. Hence these models are hard to use directly to make high level microarchitectural trade-offs in the initial exploration phase of a design. In this paper, we propose INTACTE, a tool that can be used by architects toget reasonably accurate interconnect area, delay, and power estimates based on a few architecture level parameters for the interconnect such as length, width (in number of bits), frequency, and latency for a specified technology and voltage. The tool uses well known models of interconnect delay and energy taking into account the wire pitch, repeater size, and spacing for a range of voltages and technologies.It then solves an optimization problem of finding the lowest energy interconnect design in terms of the low level circuit parameters, which meets the architectural constraintsgiven as inputs. In addition, the tool also provides the area, energy, and delay for a range of supply voltages and degrees of pipelining, which can be used for micro-architectural exploration of a chip. The delay and energy models used by the tool have been validated against low level circuit simulations. We discuss several potential applications of the tool and present an example of optimizing interconnect design in the context of clustered VLIW architectures. Copyright 2007 ACM.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.