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