Research on soft sensing modeling method of gas turbine's difficult-to-measure parameters

During the operation of a gas turbine, there are many key parameters that are difficult to directly measure or to ensure measurement accuracy, which can only be measured by offline analysis methods. However, the data obtained by offline analysis has a large time lag, and it is difficult to realize real-time monitoring, control and optimization of gas turbines. In recent years, with the widespread application of data-driven methods, data-driven soft sensing technology has become a breakthrough method for online prediction of difficult-to-measure variables. Due to the time-varying nature of the gas turbine operation process, the predictive performance of the offline modeling method will inevitably degrade over time. Therefore, an adaptive soft-sensing multi-level modeling method based on the combination of the just in time learning and the ensemble learning is proposed in this paper. Taking compressor inlet air flow and turbine inlet temperature as examples, the research is carried out and verified by actual operating data. The results verify the effectiveness of the method.

Automated summary

This article proposes an adaptive soft-sensing multi-level modeling method based on the combination of just in time learning and ensemble learning, which can effectively predict difficult-to-measure variables of gas turbines. The method is validated through actual operating data, confirming its effectiveness.