An algorithm for dynamic optimal bandwidth allocation in communication networks

We study the problem of optimal bandwidth allocation in communication networks. We consider a queueing model with two queues to which traffic from different competing flows arrive. The queue length at the buffers is observed every T instants of time, on the basis of which a decision on the amount of bandwidth to be allocated to each buffer for the next T instants is made. We consider a class of closed-loop feedback policies for the system and use a twotimescale simultaneous perturbation stochastic approximation(SPSA) algorithm to find an optimal policy within the prescribed class. We study the performance of the proposed algorithm on a numerical setting. Our algorithm is found to exhibit good performance.

On Reducing Dynamic Web Page Construction Times

Many web sites incorporate dynamic web pages to deliver customized contents to their users. However, dynamic pages result in increased user response times due to their construction overheads. In this paper, we consider mechanisms for reducing these overheads by utilizing the excess capacity with which web servers are typically provisioned. Specifically, we present a caching technique that integrates fragment caching with anticipatory page pre-generation in order to deliver dynamic pages faster during normal operating situations. A feedback mechanism is used to tune the page pre-generation process to match the current system load. The experimental results from a detailed simulation study of our technique indicate that, given a fixed cache budget, page construction speedups of more than fifty percent can be consistently achieved as compared to a pure fragment caching approach.

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Genesis of return stroke current evolution at the wavefront

The channel dynamics at the wavefront is quite complex and is basically responsible for the evolution of return stroke current. The physical processes that actually contribute to the current evolution are not very clearly known. The enhancement of channel conductance at the wavefront is necessary for the current evolution and hence, return stroke. With regard to this, several questions arise like: (i) what causes the enhancement of this conductance, (ii) as the channel core temperature and electrical conductance are closely related, does one support the other and (iii) is the increase in core temperature on the nascent section of the channel is the result of free burning arc of the wavefront just below. These questions are investigated in detail in this work with appropriate transient thermal analysis and a macroscopic physical model for the lightning return stroke. Results clearly indicate that the contribution from the thermal field of the wavefront region to the adjacent nascent channel section is negligible as compared to the field enhancement brought in by the same. In other words, the whole process of return stroke evolution is dependent on the local heat generation at the nascent section caused by the enhancement of the electric field due to the arrival of the wavefront.

A Model for the Temperature Distribution in Resin Impregnated Paper Bushings

Resin impregnated paper (RIP) is a relatively new insulation system recommended for the use in transformer bushings. In the recent past, RIP has acquired prominence as insulation in bushings, over conventional oil impregnated paper (OIP), in view of its overwhelming advantages the more important among them being low dielectric loss and possibility for positioning the bushing at any desired angle over the transformer. In addition, the fact that such systems do not pose problems of fire hazard is counted as a very important consideration. The disadvantage of RIP compared to OIP, however, is its much higher cost and involved manufacturing process. The temperature rise in RIP bushings under normal operating conditions is seen to be a difficult parameter to control in view of the limited options for effective cooling. It is therefore essential to take serious note of this aspect, to arrest rapid deterioration of bushing. The degradation of dry-type insulation such as RIP is often due to thermal stress. The long time performance thereof, depends strongly, on the maximum operating temperature. With this in view, the Authors have developed a theoretical model and computational method to study the temperature distribution in the body of insulation. The Authors consider that the basis for the model as being the temperature and electric stress aided AC conductivity. The ensuing heat balance (continuity) equations in 2-D cylindrical geometry are treated as a Dirichelet-Neumann boundary value problem.

Statistical Methods for Accelerated Life Testing with Particular Reference to Resin Impregnated Paper Bushings

Electrical failure of insulation is known to be an extremal random process wherein nominally identical pro-rated specimens of equipment insulation, at constant stress fail at inordinately different times even under laboratory test conditions. In order to be able to estimate the life of power equipment, it is necessary to run long duration ageing experiments under accelerated stresses, to acquire and analyze insulation specific failure data. In the present work, Resin Impregnated Paper (RIP) a relatively new insulation system of choice used in transformer bushings, is taken as an example. The failure data has been processed using proven statistical methods, both graphical and analytical. The physical model governing insulation failure at constant accelerated stress has been assumed to be based on temperature dependent inverse power law model.

A STUDY INTO THE BEHAVIOR OF AN ALUMINUM FOAM UNDER COMPRESSION

The characterization of a closed-cell aluminum foam with the trade name Alporas is carried out here under compression loading for a nominal cross-head speed of 1 mm/min. Foam samples in the form of cubes are tested in a UTM and the average stress-strain behavior is obtained which clearly displays a plateau strength of approximately 2 MPa. It is noted that the specific energy absorption capacity of the foam can be high despite its low strength which makes it attractive as a material for certain energy-absorbing countermeasures. The mechanical behavior of the present Alporas foam is simulated using cellular (i.e. so-called microstructure-based) and solid element-based finite element models. The efficacy of the cellular approach is shown, perhaps for the first time in published literature, in terms of prediction of both stress-strain response and inclined fold formation during axial crush under compression loading. Keeping in mind future applications under impact loads, limited results are presented when foam samples are subjected to low velocity impact in a drop-weight test set-up.