Calibration of the NASA-GSFC high energy cosmic ray experiment

Calibration of high energy cosmic ray experimen

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

Digital measurement of lightning impulse parameters using curving fitting algorithms

This paper describes the application of curve fitting algorithms to aid the evaluation of lightning impulse parameters. A number of popular curve fitting algorithms have been evaluated and compared. Investigations using the genetic algorithm and other optimisation methods for the purpose of curve fitting have also been carried out and will be described

COMPTEL solar flare observations

COMPTEL as part of a solar target of opportunity campaign observed the sun during the period of high solar activity from 7-15 Jun. 1991. Major flares were observed on 9 and 11 Jun. Although both flares were large GOES events (greater than or = X10), they were not extraordinary in terms of gamma-ray emission. Only the decay phase of the 15 Jun. flare was observed by COMPTEL. We report the preliminary analysis of data from these flares, including the first spectroscopic measurement of solar flare neutrons. The deuterium formation line at 2.223 MeV was present in both events and for at least the 9 Jun. event, was comparable to the flux in the nuclear line region of 4-8 MeV, consistent with Solar-Maximum Mission (SSM) Observations. A clear neutron signal was present in the flare of 9 Jun. with the spectrum extending up to 80 MeV and consistent in time with the emission of gamma-rays, confirming the utility of COMPTEL in measuring the solar neutron flux at low energies. The neutron flux below 100 MeV appears to be lower than that of the 3 Jun. 1982 flare by more than an order of magnitude. The neutron signal of the 11 Jun. event is under study. Severe dead time effects resulting from the intense thermal x-rays require significant corrections to the measured flux which increase the magnitude of the associated systematic uncertainties

Surface effects on the orbital order in the single layered manganite La0.5Sr1.5MnO4

We report the first observation of `orbital truncation rods' -- the scattering arising from the termination of bulk orbital order at the surface of a crystal. The x-ray measurements, performed on a cleaved, single-layered perovskite, La0.5Sr1.5MnO4, reveal that while the crystallographic surface is atomically smooth, the orbital `surface' is much rougher, with an r.m.s. deviation from the average `surface' of ~0.7nm. The temperature dependence of this scattering shows evidence of a surface-induced second order transition.Comment: 13 pages, 4 figure

COMPTEL Observations of AGN at MeV-Energies

The COMPTEL experiment aboard CGRO, exploring the previously unknown sky at MeV-energies, has so far detected 10 Active Galactic Nuclei (AGN): 9 blazars and the radio galaxy Centaurus A. No Seyfert galaxy has been found yet. With these results COMPTEL has opened the field of extragalactic Gamma-ray astronomy in the MeV-band.Comment: 4 pages, 2 figures including 1 color plot, to appear in the Proceedings of the 3rd INTEGRAL Workshop "The Extreme Universe", held in Taormina, Italy, 14-18 September 199