Closed-loop structural stability for linear-quadratic optimal system

This paper contains an explicit parameterization of a subclass of linear constant gain feedback maps that will not destabilize an originally open-loop stable system. These results can then be used to obtain several new structural stability results for multi-input linear-quadratic feedback optimal designs

A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Early warning fault detection using artificial intelligent methods

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper

Monte Carlo simulation of implant free InGaAs MOSFET

The performance potential of n-type implant free In0.25Ga0.75As MOSFETs with high-κ dielectric is investigated using ensemble Monte Carlo device simulations. The implant free MOSFET concept takes advantage of the high mobility in III-V materials to allow operation at very high speed and low power. A 100 nm gate length implant free In0.25Ga0.75As MOSFET with a layer structure derived from heterojunction transistors may deliver a drive current of 1800 A/m and transconductance up to 1342 mS/mm. This implant free transistor is then scaled in the both lateral and vertical dimensions to gate lengths of 70 and 50 nm. The scaled devices exhibit continuous improvement in the drive current up to 2600 A/m and 3259 A/m and transconductance of 2076 mS/mm and 3192 mS/mm, respectively. This demonstrates the excellent scaling potential of the implant free MOSFET concept

Today's problems with the evaluation methods of full lightning impulse parameters as described in IEC 60060-1

In this paper the present problems with the evaluation methods for lightning impulse parameters, as defined in IEC 60060-1, are described. Also the current practice of evaluation in many laboratories world-wide, that is obtained by a questionnaire, is presented. Some of the work performed up the present time and the initial conclusions are reported, then some recommendations are made for future work

Elliptic Flow from a Transversally Thermalized Fireball

The agreement of elliptic flow data at RHIC at central rapidity with the hydrodynamic model has led to the conclusion of very rapid thermalization. This conclusion is based on the intuitive argument that hydrodynamics, which assumes instantaneous local thermalization, produces the largest possible elliptic flow values and that the data seem to saturate this limit. We here investigate the question whether incompletely thermalized viscous systems may actually produce more elliptic flow than ideal hydrodynamics. Motivated by the extremely fast primordial longitudinal expansion of the reaction zone, we investigate a toy model which exhibits thermalization only in the transverse directions but undergoes collisionless free-streaming expansion in the longitudinal direction. For collisions at RHIC energies, elliptic flow results from the model are compared with those from hydrodynamics. With the final particle yield and \kt-distribution fixed, the transversally thermalized model is shown not to be able to produce the measured amount of elliptic flow. This investigation provides further support for very rapid local kinetic equilibration at RHIC. It also yields interesting novel results for the elliptic flow of massless particles such as direct photons.Comment: revtex4, 15 pages + 10 embedded EPS figure

On certain regular graphs of girth 5

Let f(v,5) be the number of vertices of a (v,5)-cage (v≥3). We give an upper bound for f(v,5) which is considerably better than the previously known upper bounds. In particular, when v=7, it coincides with the well-known Hoffman- Singleton graph