Abstract
This paper presents a novel approach based on the back-propagation neural network (BPNN) for the insulation diagnosis of power transformers. Four epoxy-resin power transformers with typical insulation defects are purposely made by a manufacturer. These transformers are used as the experimental models of partial discharge (PD) examination. Then, a precious PD detector is used to measure the 3-D (φ-Q-N) PD signals of these four experimental models in a shielded laboratory. This work has established a database containing 160 sets of 3-D PD patterns. The database is used as the training data to train a BPNN. The training-accomplished neural network can be a good diagnosis system for the practical insulation diagnosis of epoxy-resin power transformers. The proposed BPNN approach is successfully applied to practical power transformers field experiments. Experimental results indicate the attractive properties of the BPNN approach, namely, a high recognition rate and good noise elimination ability.
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Chen, PH., Chen, HC. (2007). Application of Back-Propagation Neural Network to Power Transformer Insulation Diagnosis. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_4
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DOI: https://doi.org/10.1007/978-3-540-72395-0_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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