Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model

A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil. In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging...

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Veröffentlicht in:	Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-5
Hauptverfasser:	Ding, Yu, Liu, Qiang
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms CAE Computer aided engineering Design optimization Dissolved gases Fault diagnosis Fuzzy logic International conferences Kriging Linearity Mathematical models Methods Model accuracy Neural networks Set theory Stochastic models Transformers
Online-Zugang:	Volltext
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