Designing an Optimum Adaptive Filter to Reduce Effects of Destructive Signal in SOFC Modeling

Solid oxide fuel cells (SOFC) have a wide variety of applications from use as auxiliary power units in vehicles to stationary power generation with outputs from 111 W to 2 MW. Dynamic modeling of fuel cells is a primary need for performance assessment studies of real-time and design of controller. SOFC performance modelling is related to the multi-physic processes taking place on the fuel cell surfaces. Some new models of Simulink use artificial intelligence for making model of output graph. Neural network is one of these models. Neural network just uses input – output data that obtained in several experiments and does not need to set all the parameters. The ANN is used for modelling singular cell behavior. In this paper, at the first, the SOFC simulate using Feedforward neural networks. This network trained using different algorithms and the results compared to determine appropriate algorithm for our purpose based on MADM. Then the effects of destructive signal on the neural network is evaluated and by defining a suitable adaptive filter attempt to reduce this effect.

کلیدواژه ها:

ANN ، Adaptive filter ، DG ، MADM ، SOFC