Prediction method of dissolved gas in transformer oil based on modal decomposition and hybrid CNN-GRUT
In order to prevent power transformers from under-or over-O&M,it is important to evaluate their operat-ing status and predict potential faults.DGA technology is an effective method to evaluate the state of transformers,and the mechanical vibration and oil temperature of transformers will cause the dissolved gas signal in oil to show a nonlinear trend and unstable characteristics.As a result,prediction difficulties increase,and even the lack of daily measurement gas data makes it impossible for online monitoring systems based on DGA technology to monitor trans-former status.In view of the above problems,this paper applies EEMD to decompose the gas concentration signal set,and the high-frequency eigenmode function generated by EEMD will increase the prediction difficulty and affect the prediction accuracy,and use WPD to further decompose the sub-signal modal function,aiming at the problem that machine learning cannot separate and analyze the temporal correlation and hidden characteristics between con-centration signals in the past,this paper proposes a hybrid CNN-GRUT prediction model to separate the hidden characteristics in the gas concentration sub-signal.The time correlation characteristics in the gas concentration sub-signal set were deeply analyzed,and the predicted value of the dissolved gas concentration signal in oil was ob-tained by iterative sub-signal recombination.The experimental results show that compared with BP,Elman and oth-er hybrid prediction models,the average absolute error of CMD-CNN-GRUT prediction is reduced by 22.44%and 30.9%,and the availability of the suggested prediction model is proved by experiments.
dissolved gases in oilmodal decompositionconvolutional neural networksgated recurrent neural networksforecast
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