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This paper describes a methodology that aims to extract information to enable the detection and diagnosis of faults in surge arresters, using the thermovision technique. Thermovision is a non-destructive technique used in diverse services of maintenance, having the advantage not to demand the disconnection of the equipment. The methodology uses a digital image processing algorithm based on the Watershed Transform to get the segmentation of the surge arresters. By applying the methodology is possible to classify surge arresters operative condition in: faulty, normal, light, and suspicious. The computational system generated train its neuro-fuzzy network by using a historical thermovision data. During the train phase, a heuristic is proposed in order to set the number of networks in the diagnosis system. This system was validated by a database with a hundreds of different faulty sceneries. The validation error of the set of neuro-fuzzy and the automatic digital thermovision image processing was about 10%t. The diagnosis system described has been successfully used by Electric Energy Research Center as a decision making tool for surge arresters fault diagnosis.
Date of Publication: April 2009