Picture of a black hole

Strathclyde Open Access research that creates ripples...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of research papers by University of Strathclyde researchers, including by Strathclyde physicists involved in observing gravitational waves and black hole mergers as part of the Laser Interferometer Gravitational-Wave Observatory (LIGO) - but also other internationally significant research from the Department of Physics. Discover why Strathclyde's physics research is making ripples...

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

The role of AI planning as a decision support tool in power substation management

Bell, K.R.W. and Smith, A.J. and Coles, A.I. and Fox, Maria and Long, Derek (2009) The role of AI planning as a decision support tool in power substation management. AI Communications, 22 (1). pp. 37-57. ISSN 0921-7126

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

The management of power substations is a challenging task, with two opposing criteria: to reduce wear-and-tear on equipment and to ensure the voltage remains within a specified range. At present, a two-stage process is used. Voltage targets are determined for each time of day by electrical engineers using a time-consuming and costly manual process. Then, a reactive control system at the substation is used to satisfy these targets. In this article, we present a novel application of AI planning as part of an intelligent automated system for devising voltage targets. Within the system, both the cost and fault-tolerance implications of voltage target decisions are considered, and hence an efficient and effective set of voltage targets is produced. Using AI planning affords a great deal of flexibility and we show how the system can handle known exogenous events for a given day to reduce forecasted operational costs.