Abstract
In industrial practice, the representation of the dynamics of nonlinear systems by models linking their different operating variables requires an identification procedure to characterize their behavior from experimental data. This article proposes the identification of the variables of a two-shafts gas turbine based on a decoupled multi-model approach with genetic algorithm. Hence the multi-model is determined in the form of a weighted combination of the decoupled linear local state space sub-models, with optimization of an objective cost function in different modes of operation of this machine. This makes it possible to have robust and reliable models using input / output data collected on the examined system, limiting the influence of errors and identification noises.
Highlights
Monitoring and understanding the behavior of gas turbines is an industrial challenge which has, in recent years, become increasingly important in most industrial sectors that use these rotating machines
The results showed a very good precision in particular for the NGP and NPT sub-models, as it is shown in Figs. 13 and 14
Fig. presents the results of multi-objective optimization of function of cost of NGP for different local models (L2, L3, L4) and Fig. presents the multi-objective optimization results of NPT cost function for the same local models. These results show the decrease in the cost functions for the different sub-models of the MMGA outputs, compared to the cost functions, it is clear that for the cost function of NPT the sub-model L2 were the fastest, but is not the lowest with a small difference compared to the L3 sub-model, for the cost function of NGP the L4 sub-model was the lowest, but in the validation cost it has the highest cost, and this is due to a multi-objective optimization which is the same case with the NSGA II and for the exhaust temperatures T5 and T7 of the turbine the sub-model L2 is the highest with the least cost
Summary
Monitoring and understanding the behavior of gas turbines is an industrial challenge which has, in recent years, become increasingly important in most industrial sectors that use these rotating machines This industrial equipment are on a large scale and represent significant non-linearity's with uncertainties in their modeling. They are subject to several specific faults which violate their observability. This work is oriented in this direction to illustrate and show how, in a monitoring policy, the variables are measured, processed, monitored in the form of sub-models and are used for the development of a global model of a gas turbine. This work raises one of the major problems when looking for a reliable mathematical representation for this type of rotating machine
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