GRU Based Time Series Forecast of Oil Temperature in Power Transformer
DOI:
https://doi.org/10.13052/dgaej2156-3306.3822Keywords:
Oil temperature, power transformer, deep learning, LSTM, GRUAbstract
With the continuous progress of the society, the demand for electrical power is urgent. The transformer plays an important role in the power energy transmission. The oil temperature inside transformer effectively could reflect working condition of the transformer, which makes it necessary to monitor and forecast the oil temperature to monitor the operating status of the power transformer. However, the oil temperature time series data generated by the power transformer has the characteristics of being complex and nonlinear. In recent years, long and short time memory networks (LSTM) are often used to predict transformer oil temperature. Gated recurrent unit (GRU) is a new version for LSTM. In the structure of GRU, there exist two gates, which are updating gate and resetting gate, respectively. Compared with LSTM network, The structure of GRU is simpler and its effect is better. A novel predicting method for transformer oil temperature is proposed based on time series theory and GRU in this paper, which is verified on the dataset of the oil temperature of the transformers in the two regions. The experimental results are compared with traditional time series prediction models to demonstrate that the proposed method is effective and feasible.
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