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- W2385879266 abstract "The key point of analyzing dissolved gas in transformer to diagnose transformer fault is to find out the non-linear relationship between dissolved gas content and the fault type.Using subdivision method,but not based on Fourier transformer to create a new wavelet function is put forward to deal with the problem which diagnosis accuracy is not high using traditional analysis method.The novel wavelet function combines with feed-forward neural network which learning rate is optimized to build a weighted wavelet neural network for fault diagnosis of transformers.Using weighted wavelet neural network to analyze actual fault data proves that novel neural network has higher diagnostic accuracy,and higher calculation speed under the same condition,thus can increase the efficiency of the transformer fault diagnosis." @default.
- W2385879266 created "2016-06-24" @default.
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- W2385879266 date "2010-01-01" @default.
- W2385879266 modified "2023-09-25" @default.
- W2385879266 title "Fault diagnosis of oil-immerse power transformer based on weighted wavelet neural network" @default.
- W2385879266 hasPublicationYear "2010" @default.
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