Power Transformers Fault Diagnosis Based on DRNN

Hui Da Duan; Qiao Song Li

doi:10.4028/www.scientific.net/AMR.960-961.700

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A Novel Method of Core Saturation Suppression for the AC+DC Distribution Network Transformer
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Low-Frequency Nanotesla Resolution of Magnetic Field Detection in Metglas/Magnetostrictive/Piezoelectric Laminates
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 960-961Power Transformers Fault Diagnosis Based on DRNN

Power Transformers Fault Diagnosis Based on DRNN

Abstract:

In recent years, improved three-ratio is an effective method for transformer fault diagnosis based on Dissolved Gas Analysis (DGA). In this paper, diagonal recurrent neural network (DRNN) is used to resolve the online fault diagnosis problems for oil-filled power transformer based on DGA. To overcome disadvantages of BP algorithm, a new recursive prediction error algorithm (RPE) is used in this paper.In addition, to demonstrate the effectiveness and veracity of the proposed method, some cases are used in the simulation. The simulation results are satisfactory.

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Periodical:

Advanced Materials Research (Volumes 960-961)

Pages:

700-703

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.960-961.700

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Online since:

June 2014

Authors:

Hui Da Duan*, Qiao Song Li

Keywords:

Diagonal Recurrent Neural Network, Dissolved Gas Analysis, Fault Diagnosis, Recursive Prediction Error Algorithm

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* - Corresponding Author

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