Knowledge discovery from on-line cable condition monitoring systems

Detection and diagnosis of partial discharge (PD) activity has been widely adopted in electrical plant condition monitoring. For many years incipient partial discharge faults in power cables have been identified through off-line investigation techniques. With the development of measurement technology, more recently, continuous on-line monitoring systems are being installed, because in comparison with off-line measurement, it owns more advantages such as low cost, easy set-up etc. This has been instigated with the aim of reducing unexpected failures. Unfortunately, due to a lack of knowledge rules which can be applied to the data detected from on-line PD condition monitoring, this technology has not shown its full potential so far. This paper presents work on the analysis and development of a knowledge acquisition system based on rough set (RS) theory. Results prove that the proposed algorithm can successfully discover the hidden correlations between cable faults and PD measurement data and improve the effectiveness of on-line condition monitoring systems.