Neural Network Supervisor for Hybrid Fuel Cell/Gas Turbine Power Plants

The neural network (NN) supervisor is developed for online estimation of optimal feedforward (FF) control inputs and setpoints for hybrid fuel cell/gas turbine power systems. The approach consists of determining a neural network structure suitable for predicting FF control inputs and setpoints based on optimal operating trajectories. The optimal trajectories were obtained in a previous study via nonlinear dynamic optimization based on a dynamic power plant model. Determination of the NN structure involves an a priori decision of the type of NN, the overall topology of input/output pairing, definition of a training epoch, as well as an identification of the number of hidden layer neurons and the number of iterations for the training epochs. This allows for straightforward training of the NN using the global training method, which includes all power profiles to define an epoch. In addition to training the NN with all available data, the network´s prediction capabilities were tested by training it with all but one dataset and then determining the prediction results based on the untrained dataset. Results from eighteen case studies show that the developed NN supervisor is capable of predicting the optimized FF and setpoint trajectories satisfactorily.