文章基本信息

标题：Study on Fuel Utilization Dynamic model of a SOFC-GT Hybrid System Based on Deep Learning Technique
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作者：Jinwei Chen ; Yao Chen ; Huisheng Zhang 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2019
卷号：113
页码：1-6
DOI：10.1051/e3sconf/201911302010
出版社：EDP Sciences
摘要：In order to perform operation management tasks, including state monitoring and control strategy optimization, of a solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system, a data-driven dynamic model based on deep learning technique of long short term memory (LSTM) network is developed to predict the behaviours of fuel utilization. In addition, a LSTM model with unsupervised deep auto-encoder (DAE) method was developed to extract the feature from input data. The comparison performance between the common LSTM model and DAE-LSTM model was investigated. The results show that the DAE-LSTM model can enhance the prediction performance. Moreover, the effect of data size was investigated. The results demonstrate that the unsupervised DAE-LSTM model trained by large data size can further improve the prediction performance. The maximum error is only 0.00529, and average error decreases to 0.00025. In conclusions, the unsupervised DAE-LSTM model is an effective approach to predict dynamic behaviours.
其他摘要：In order to perform operation management tasks, including state monitoring and control strategy optimization, of a solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system, a data-driven dynamic model based on deep learning technique of long short term memory (LSTM) network is developed to predict the behaviours of fuel utilization. In addition, a LSTM model with unsupervised deep auto-encoder (DAE) method was developed to extract the feature from input data. The comparison performance between the common LSTM model and DAE-LSTM model was investigated. The results show that the DAE-LSTM model can enhance the prediction performance. Moreover, the effect of data size was investigated. The results demonstrate that the unsupervised DAE-LSTM model trained by large data size can further improve the prediction performance. The maximum error is only 0.00529, and average error decreases to 0.00025. In conclusions, the unsupervised DAE-LSTM model is an effective approach to predict dynamic behaviours.