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It is extremely important to monitor the status of gas turbine to ensure its safe and reliable operation. In this work, the variation trend of isentropic efficiency of compressor is analysed based on the measured data of F-class heavy-duty gas turbine in practical industrial application. The actual measured data of F-class heavy-duty gas turbine includes the data under start-stop and unstable working conditions, which cannot be directly used for calculation and analysis. To solve this problem, the data selection rules are designed and determined according to the operating conditions of gas turbine to select the data under effective working state. The isentropic efficiency of compressor is calculated based on the selected data. Then the forecasting effects of four forecasting methods on the variation trend of isentropic efficiency of compressor are studied. Four indexes, namely, symmetric mean absolute percentage error (SMAPE), mean absolute percentage error (MAPE), root mean square error (RMSE), and similarity (SIM) values are utilized to evaluate the forecasting accuracy. The research results indicate that the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has better forecasting effect than Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR) and Nonlinear Autoregression Neural Network (NARNN) for this F-class heavy-duty gas turbine. Through the ANFIS method, the SIM up to 96.77%, the SMAPE and MAPE are less than 0.1, and the RMSE is only 0.1157. Therefore, the ANFIS method is suitable for forecasting the isentropic efficiency of this F-class heavy-duty gas turbine compressor.

Comparative analysis and forecasting of isentropic efficiency of gas turbine compressor with ARIMA, VAR, NARNN and ANFIS approaches