Thermal modeling of a solid oxide fuel cell and micro gas turbine hybrid power system based on modified LS-SVM
✍ Scribed by Xiao-Juan Wu; Qi Huang; Xin-Jian Zhu
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 321 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0360-3199
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✦ Synopsis
MGT) Least squares support vector machine (LS-SVM) Particle swarm optimization (PSO) a b s t r a c t For a solid oxide fuel cell (SOFC) integrated into a micro gas turbine (MGT) hybrid power system, SOFC operating temperature and turbine inlet temperature are the key parameters, which affect the performance of the hybrid system. Thus, a least squares support vector machine (LS-SVM) identification model based on an improved particle swarm optimization (PSO) algorithm is proposed to describe the nonlinear temperature dynamic properties of the SOFC/MGT hybrid system in this paper. During the process of modeling, an improved PSO algorithm is employed to optimize the parameters of the LS-SVM. In order to obtain the training and prediction data to identify the modified LS-SVM model, a SOFC/MGT physical model is established via Simulink toolbox of MATLAB6.5. Compared to the conventional BP neural network and the standard LS-SVM, the simulation results show that the modified LS-SVM model can efficiently reflect the temperature response of the SOFC/MGT hybrid system.