MACHINE LEARNING AND KNOWLEDGE BUILDING FOR FAULT-DIAGNOSIS IN DISTRIBUTION NETWORK

A fault diagnosis system for distribution networks using machine learn ing of fault patterns is described The learning process can be impleme nted during fault condition on the actual network or through a distrib ution network simulator. Knowledge acquisition is almost automated. Th e proposed system can be integrated into the existing SCADA system to provide a guide for engineers during fault conditions.