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

Rapidly reconfigurable slow-light system based on off-resonant Raman absorption

We present a slow-light system based on dual Raman absorption resonances in warm rubidium vapor. Each Raman absorption resonance is produced by a control beam in an off-resonant Λ system. This system combines all optical control of the Raman absorption and the low-dispersion broadening properties of the double Lorentzian absorption slow light. The bandwidth, group delay, and central frequency of the slow-light system can all be tuned dynamically by changing the properties of the control beam. We demonstrate multiple pulse delays with low distortion and show that such a system has fast switching dynamics and thus fast reconfiguration rates

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

The implementation of a radiographic reporting service for trauma examinations of the skeletal system in 4 NHS trusts. NHS Executive South Thames funded research project

Executive Summary The implementation of a Radiographic Reporting Service for trauma examinations of the skeletal system, in 4 National Health Service Trusts Project Reference: SPGS 438 Project Dates: 1 September 1997 (project start) 30 November 1998 (project end) 31 March 1999: Date of report submission Project Leader: Mr Keith Piper, Senior Lecturer and Programme Director, PgD Clinical Reporting * Research Assistant: Ms Carol Ryan, Department of Radiography * Project Supervisor: Mrs Audrey Paterson, Dean of Faculty of Health and Sciences and Head of Department of Radiography * * Canterbury Christ Church University College Main Research Objectives The purpose of the study was to evaluate the implementation of a Radiographic Reporting Service (RRS) in four NHS Trusts in the United Kingdom with specific reference to the reporting by radiographers of musculo-skeletal trauma examinations. The research investigated the accuracy of radiographers’ written reports in terms of sensitivity and specificity; the impact on patient care and management as measured by the volume of reporting activity undertaken and the speed with which reports became available; costs related to the implementation of an RRR, and satisfaction of the users of the service. Methodology and Sample Size A longitudinal study design was used to measure the productivity and effectiveness of radiographic reporting in four NHS Trusts and five clinical sites in England. Data were collected by direct measure, report pro-forma, semi-structured questionnaires and interviews. A series of base line measurements were made at the commencement of the project. These were the volume of reporting activity prior to implementation of an RRS and the speed with which the reports became available. The satisfaction of the users of the reporting service prior to the implementation of an RRS was also gauged. Three measures (volume, speed, satisfaction of users) were repeated after the RRS had been implemented. Longitudinal data on the accuracy of the radiographers’ reports in terms of sensitivity and specificity were also collected at each site. Finally, some cost information related to the introduction and provision of an RRS was gathered. Four NHS Trusts and 10 radiographers participated in the study. Radiographers completed 10275 reports and 7179 were used to assess accuracy, sensitivity and specificity. Volume and speed data were obtained from the normal workload in each Trust. Four radiology services managers provided the cost data, while 26 staff took part in the initial survey and 12 in the final survey. Problems The major problem with this study was the fact that it was investigating the implementation into practice of a new and controversial service. It was beset, therefore, with the difficulties of aligning a research project to practice and this was only possible imperfectly. Points of implementation of the new service varied considerably, workload of key staff made verification of reports difficult and information systems within Trusts proved problematic. Findings Radiographers’ reports were accurate and consistently so over time. Significant improvements in the volume of reporting activity were found post-implementation at 2 of the 4 clinical sites in which this was measured. Additionally, the speed with which reports became available was shown to have improved significantly in all 4 NHS Trusts (but not at one clinical site). Cost data was not considered to be reliable and more evaluation of costs is required. Users of the radiographic reporting services were extremely or very satisfied with the quality of reports produced by the radiographers and also satisfied with the nature of the service implemented. Finally, a range of organisational issues were seen to affect the implementation of these services, at times quite inappropriately. Conclusion NHS Trusts that are unable to provide a full and/or timely musculo-skeletal trauma reporting service should implement a radiographic reporting service but must ensure that this is properly planned, funded, implemented and managed. Monitoring of service effectiveness and auditing of reporting standards should take place periodically. Acknowledgements The four collaborating NHS Trusts and their staff; Expert panel members; Members of the Steering Group; Colleagues at Canterbury Christ Church University College, and the Research and Development Directorate at South Thames Regional Office (NHSE)