Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

作者：Xu, Xiandong^*; Li, Kang; Jia, Hongjie; Yu, Xiaodan; Deng, Jing; Mu, Yunfei

来源：IEEE Transactions on Smart Grid, 2018, 9(2): 1387-1396.

DOI：10.1109/TSG.2016.2590022

• Microturbine
• combined heat and power
• dynamic behavior
• modeling
• system identification

Microturbines (MTs) are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes, which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of MTs. Considering the time-scale difference of various dynamic processes occurring within MTs, the electromechanical subsystem is treated as a fast quasi-linear process, while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 MT show that the proposed modeling method can well capture the system dynamics, and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

• 出版日期2018-3
• 单位天津大学