V94.2 industrial gas turbine compressor fouling detection based on system identification methods, neural networks and experimental data

In this paper, model based fault detection of gas turbine using linear and non-linear methods (multilayer perceptron and radial basis function neural network models) is studied. We contemplate IGV positions and gas flow as input and sensors related to compressor as outputs. Then residual signals will be obtained based on system model. In addition, by these signals and exert the fixed and adaptive thresholds, the fault occurred in the V94.2 gas turbine which is pollution of vane compressor (Fouling detection) has identified and diagnosed. Consequently, by comparing the obtained results from different fault detection methods, we determine the most appropriate signal output that led to better and reliable result. All simulations have been carried out by using real data taken from an V94.2 industrial gas turbine 927 power plant in Fars.