Time series prediction of transformer oil chromatography based on
Attention-PSO-GRU model
Abstract
The content of dissolved gas in transformer oil is an important
characteristic to reflect the operating condition of transformers. To
overcome the problems that the traditional transformer oil
chromatography gas prediction model cannot effectively use the older
transformer oil chromatography data and it is difficult to track the
transformer oil chromatography data under abnormal conditions and it can
only rely on experience to adjust the model parameters, we introduce
Gated Recurrent Unit (GRU), Attention Mechanism (APM) and Particle Swarm
Optimization (PSO) to construct the Attention-PSO-GRU prediction model.
The experimental results show that the Attention-PSO-GRU model can reach
97.9% accuracy in predicting the normal transformer oil chromatography
data and has a better tracking ability for the abnormal transformer oil
chromatography data. Therefore, the Attention-PSO-GRU model proposed in
this paper can effectively improve the accuracy of transformer oil
chromatography gas prediction, which has practical significance for
preventing transformer faults and ensuring the safe and stable operation
of power systems.