Knowledge Extraction from Intelligent Electronic Devices

Most substations today contain a large number of Intelligent Electronic Devices (IEDs), each of which captures and stores locally measured analogue signals, and monitors the operating status of plant items. A key issue for substation data analysis is the adequacy of our knowledge available to describe certain concepts of power system states. It may happen sometimes that these concepts cannot be classified crisply based on the data/information collected in a substation. The paper therefore describes a relatively new theory based on rough sets to overcome the problem of overwhelming events received at a substation that cannot be crisply defined and for detecting superfluous, conflicting, irrelevant and unnecessary data generated by microprocessor IEDs. It identifies the most significant and meaningful data patterns and presents this concise information to a network or regionally based analysis system for decision support. The operators or engineers can make use of the summary of report to operate and maintain the power system within an appropriate time. The analysis is based on time-dependent event datasets generated from a PSCAD/EMTDC simulation. A 132/11 kV substation network has been simulated and various tests have been performed with a realistic number of variables being logged to evaluate the algorithms.