Intelligent Thermographic Diagnostic Applied to Surge Arresters: A New Approach

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 discon...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2009-04, Vol.24 (2), p.751-757
Main Authors: Laurentys Almeida, C.A., Braga, A.P., Nascimento, S., Paiva, V., Martins, H.J.A., Torres, R., Caminhas, W.M.
Format: Article
Language:English
Subjects:
Applied sciences
Arresters
Computer networks
Connection and protection apparatus
Data mining
Diagnosis
Digital
Digital image processing
Digital images
Electrical engineering. Electrical power engineering
Exact sciences and technology
Fault detection
Fault diagnosis
Fuzzy neural networks
Fuzzy systems
Image databases
Image processing
Image segmentation
Methodology
Neural networks
neuro-fuzzy networks
Surge arresters
Surges
Testing. Reliability. Quality control
Trains
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Summary: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.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2009.2013375