By combining with dissolvedgas analysis,timeseriesprediction of dissolvedgascontent in oil provides a basis for transformer fault diagnose and early warning. In the view of that,a predictionmodelbased on long s...
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By combining with dissolvedgas analysis,timeseriesprediction of dissolvedgascontent in oil provides a basis for transformer fault diagnose and early warning. In the view of that,a predictionmodelbased on long short time memory(lstm) network for timeseries of dissolvedgascontent in oil is proposed,which takes advantage of lstm network's ability to deal with long-sequence prediction problems. Five characteristic gas concentrations are used as input to the model,and the hyper parameters of the model is optimized by Bayesian optimization algorithm to further improve prediction accuracy,then a lstmpredictionmodel is constructed. By case study,it is verified that the proposed model can precisely predict timeseries of dissolvedgascontent. Compared with gray model,BP neural network and support vector machine,the proposed model has higher prediction accuracy and can better track the trend of timeseries of dissolvedgascontent in oil.
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