Study on Fuel Utilization Dynamic model of a SOFC-GT Hybrid System Based on Deep Learning Technique

• Main Authors: Chen Jinwei, Chen Yao, Zhang Huisheng
• Format: Article
• Language: English
• Published: EDP Sciences 2019-01-01
• Series: E3S Web of Conferences
• Online Access: https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/39/e3sconf_supehr18_02010.pdf

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.