Research on Method of State Evaluation and Fault Analysis of Dry-Type Power Transformer Based on Self-Organizing Neural Network

Abstract:

Dry-type power transformer was used widely because of its advantages. But unplanned outage effect to construct a strong intelligent power grid because of various stress. Dry-type power transformer’s fault repair time is long and impossible to repair. So it is very important to realize state maintains of dry-type transformer through state monitor and diagnosis. Based on current diagnostic methods, this paper proposed using self-organizing neural network to realize dry-type power transformer the key point temperature parameters of grading evaluation and then to realize the real-time state evaluation and analysis of failure causes. Study results to prolong the dry-type power transformer life and its design production provide theoretical guidance, in order to reduce and avoid dry-type power transformer failure.

• Periodical: Applied Mechanics and Materials (Volumes 303-306)
• Pages: 562-566
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.562
• Online since: February 2013
• Authors: Chen Hu Yuan, Mu Zhang, Sheng Wei Gao, Wei Wang, Xing Tao Sun
• Keywords: Dry-Type Transformer, Fault Analysis, Self-Organizing Neural Network , State Evaluation

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