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This paper reports on the use of multi-agent system technology to automate the management and analysis of SCADA and digital fault recorder (DFR) data. The multi-agent system, entitled Protection Engineering Diagnostic Agents (PEDA), integrates legacy intelligent systems that analyze SCADA and DFR data to provide data management and online diagnostic information to protection engineers. Since November 2004, PEDA agents have been intelligently interpreting and managing data online at a transmission system operator in the U.K. As the results presented in this paper demonstrate, PEDA supports protection engineers by providing access to interpreted power systems data via the corporate intranet within minutes of the data being received. In this paper, the authors discuss their experience of developing a multi-agent system that is robust enough for continual online use within the power industry. The use of existing agent development toolsets and standards is also discussed. [ABSTRACT FROM AUTHOR]

As an intermediate solution between Glaser's satellite solar power (SSP) and ground-based photovoltaic (PV) panels, this paper examines the collection of solar energy using a high-altitude aerostatic platform. A procedure to calculate the irradiance in the medium/high troposphere, based on experimental data, is described. The results show that here a PV system could collect about four to six times the energy collected by a typical U.K.-based ground installation, and between one-third and half of the total energy the same system would collect if supported by a geostationary satellite (SSP). The concept of the aerostat for solar power generation is then briefly described together with the equations that link its main engineering parameters/variables. A preliminary sizing of a facility stationed at 6 km altitude and its costing, based on realistic values of the input engineering parameters, is then presented. [ABSTRACT FROM AUTHOR]

This paper describes the process of review, design, and delivery of a course in modern embedded systems, an international collaborative teaching project involving the University of New South Wales (Australia), Manchester University, and Imperial College, London University (United Kingdom). This project, being the first of its kind anywhere in the world, provides a learning environment that replicates the current industrial practice in embedded systems design in an easy and comprehensible setting, an environment where the processor, dedicated coprocessors, and software are all integrated to create a functional system such as used in sophisticated electronic devices, including mobile phones, Web phones, televisions, digital cameras, and personal digital assistants. Such collaborations are important in both reducing development costs in developing up-to-date, and increasingly sophisticated, courses and in addressing pedagogical issues that are common between computer and electrical engineering programs in all academic institutions. To assist students' learning experience, the course is supported with purpose built state-of-the-art programmable hardware and software development platforms, carefully planned laboratory experiments, lecture notes, weekly online quizzes, tutorials, and a companion CD-ROM as a learning tool. Since the introduction of this complete package, students' satisfaction, assessment results, and skills obtained through evaluation and assessment methods have improved markedly. [ABSTRACT FROM AUTHOR]

The electricity industry is significantly affected by weather conditions both in terms of the operation of the network infrastructure and electricity consumption. Following privatization and deregulation, the electricity industry in the U.K. has become fragmented and central planning has largely disappeared. In order to maximize profits, the margin of supply has decreased and the network is being run closer to capacity in certain areas. Careful planning is required to manage future electricity demand within the framework of this leaner electricity network. There is evidence that the climate in the U.K. is changing with a possible 3°C average annual temperature increase by 2080. This paper investigates the impact of weather variables on monthly electricity demand in England and Wales. A multiple regression model is developed to forecast monthly electricity demand based on weather variables, gross domestic product, and population growth. The average mean absolute percentage error (MAPE) for the worst model is approximately 2.60% in fitting the monthly electricity demand from 1989 to 1995 and approximately 2.69% in the forecasting over the period 1996 to 2003. This error may reflect the nonlinear dependence of demand on temperature at the hot and cold temperature extremes; however, the inclusion of degree days, enthalpy latent days, and relative humidity in the model improves the demand forecast during the summer months. [ABSTRACT FROM AUTHOR]