Most of the actions taken within today’s power plants are directed by control systems, which usually are computerised and located in a central control room within the power plant. In normal states, the communication between the control system and the operators is satisfactory, with few alarms occurring infrequently. However, when large disturbances occur, the communication is problematical. Instead of being aided by the messages, the operators become swamped by the amount of information, and often have to make more or less informed guesses of what causes the abnormal situation. It is therefore of great importance if the control system can discriminate between normal and abnormal situations, as well as being less sensitive and giving priority to alarms that must be sent to the operators. In order for the system to make such analyses, processes for diagnosis and decision making regarding the reliability and importance of the information are needed. This paper shows how machine learning algorithms can be combined with decision theory w.r.t. vague and numerically imprecise background information, by using classifiers. An ensemble is a classifier created by combining the predictions of multiple component classifiers. We present a new method for combining classifiers into an ensemble based on a simple estimation of each classifier’s competence. The purpose is to develop a filter for handling complex alarm situations. Decision situations are evaluated using fast algorithms developed particularly for solving these kinds of problems. The presented framework has been developed in co-operation with one of the main actors in the Swedish power plant industry.
Published Date: May 1999
Registration: ISBN 978-1-57735-080-4
Copyright: Published by The AAAI Press, Menlo Park, California.