Condition monitoring of 11 KV paper insulated cables using self-organising maps

This paper concerns the feasibility of using Self-Organising Feature Maps for the insulation assessment of paper insulated cables. This class of neural networks is able to isolate different clusters within the discharge activity obtained throughout a degradation process. However, once trained, they are incapable of identifying novel states in the insulation of the sample. As a possible solution of this problem, the authors present a variation of the SOM based on the expansion of the trained map. With this modification, SOM can be used for the condition monitoring of the cables and the prediction of incipient faults.