Multidimensional minimization training algorithms for steam boiler drum level trip using artificial intelligent monitoring system

This paper deals with the Fault Detection and Diagnosis of steam boiler using developed artificial Neural networks model. Water low level trip of steam boiler is artificially monitored and analyzed in this study, using two different interpretation algorithms. The Broyden-Fletcher-Goldfarb-Shanno quasi-Newton and Levenberg-Marquart are adopted as training algorithms of the developed neural network model. Real site data is captured from a coal-fired thermal power plant in Perak state - Malaysia. Among three power units in the plant, the boiler drum data of unit3 was considered. The selection of the relevant variables to train and validate the neural networks is based on the merging between the theoretical base and the operators experience and the procedure is described in the paper. Results are obtained from one hidden layer and two hidden layers neural network structures for both adopted algorithms. Detailed comparisons have been made based on the Root Mean Square Error. The results are demonstrating that the one hidden layer with one neuron using BFGS training algorithm provides the best optimum NN structure.