Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling

Abstract

An accurate gas turbine performance model is important for system performance evaluation. To minimize the simulated performance error, an adaption method using coefficients of scaling factors is applied to tune the component characteristic maps and make the gas turbine model meet measurements of a few randomly sampled points. However, the field data are non-homogeneously distributed. In this situation, randomly selecting a few sampling may lead to the inappropriate correction of the component characteristic maps and lower the prediction accuracy of model. Firstly, the coefficients of scaling factors are introduced to construct the performance adaption optimization problem of the gas turbine model. Secondly, the k-means clustering algorithm is applied to divide the 146 field data points into 10 different categories, and then the points closest to the cluster centers are selected to form the sampling set. Thirdly, a particle swarm optimization algorithm is used to search the optimal scaling factors. As a result, the model error decreases from 2.947% to 0.610%. Finally, the proposed method is validated with the remaining field data of a real E-class gas turbine. The average predicted error is 0.466%. Compared with the performance results obtained by random sample, the model based on cluster sampling shows a better accuracy.