System identification: theory for the user
System identification: theory for the user
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Solid Oxide Fuel Cells (SOFCs) are high temperature fuel cells with a strong potential for stationary power house applications. However, considerable challenges are to be overcome to connect these cells to the power grid. The cells have to satisfy the changing demand of the grid without sacrificing their efficiencies and without causing any structural or material damage. Such an operation, coupled with fast and highly nonlinear transients of the transport variables, leads to a very challenging control problem. This requires an efficient and robust controller. For synthesizing such a controller, a well-validated dynamic model is essential. In this work, a dynamic model is validated by using experimental data from an industrial cell. The data are generated over a broad range of cell temperatures, reactant flow rates, DC polarizations, and amplitudes of step. In the process of validation, it is identified that the Knudsen diffusion and an extended active area for the electrochemical reactions play key roles in determining the current transients of the cell. The dynamic model is used for identification of reduced order models that can be solved in real time for implementation in the MPC framework. Several linear and nonlinear models are considered and the best model is chosen according to the AIC values of the models. Both SISO and MIMO models are identified. For the MIMO model, voltage and H2 flow are considered as inputs. Power and utilization factors are considered as outputs. A linear model such as ARX model is found to be satisfactory for most SISO cases. However, a nonlinear model such as NAARX model with more cross terms is found to improve the model performance significantly for the MIMO case. All through this work, efforts have been made to synthesize the simplest, yet representative model that can be used for real-time applications.