基于改进门控循环单元的变压器油中气体浓度预测

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Abstract

Predicting the dissolved gas concentration in oil-immersed power transformer oil can provide important reference for transformer condition assessment and fault diagnosis. In order to further improve the prediction accuracy, an improved gated recurrent unit (GRU) prediction model for gas concentration in transformer oil is proposed by combining RAdam optimizer and GRU. The improved model takes seven typical gas concentrations as input, uses RAdam optimizer to train the model, and predicts the corresponding gas concentration as output. The results show that the improved GRU model can better predict the change trend of dissolved gas concentration in transformer oil and expand the selection range of initial learning rate compared with the ordinary GRU model and long short-term memory (LSTM) model.

Key words： transformer oil dissolved gas in oil improved gated recurrent unit (GRU) con- centration prediction

Received: 06 September 2021

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	Articles by authors
	WEI Yongpeng
	SU Yihui
	WANG Shengli
	ZHANG Xin
	WANG Heng

Cite this article:

WEI Yongpeng,SU Yihui,WANG Shengli等. Prediction of gas concentration in transformer oil based on improved gated recurrent unit[J]. Electrical Engineering, 2022, 23(2): 55-60.

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http://dqjs.cesmedia.cn/EN/Y2022/V23/I2/55

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