Abstract
In view of some traditional defects, say, incomplete codes, and imperfect criterions on critical values, of IEC 3-ratio-code law in transformer faults diagnosis, a novel transformer faults diagnosis method is proposed based on adaptive neuro-fuzzy inference system (ANFIS) in the paper. The ridge-type distribution functions serve as the fuzzy membership functions in input layer of ANFIS, and Fletcher-Reeves (FR) conjugation gradient algorithm with good global convergence properties acts as the learning algorithm of ANFIS, the learning quality of ANIFS is therefore improved, dramatically. Eventually, the test results in transformer faults diagnosis show the validity of the method.
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Su, H. (2011). Transformer Fault Diagnosis Method Based on Improved ANFIS. In: Lee, J. (eds) Advanced Electrical and Electronics Engineering. Lecture Notes in Electrical Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19712-3_33
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DOI: https://doi.org/10.1007/978-3-642-19712-3_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19711-6
Online ISBN: 978-3-642-19712-3
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