No abstract

Compressor fouling is one of the critical gas path faults of gas turbines, and the fouling process is significantly influenced by the quality of the inlet air coming from the air intake system with filters. The maintenance strategies for compressor fouling mainly consist of online/offline washing and replacement of filters, where optimizing the washing cycles and the replacement of filters is essential for the economy and safety of gas turbine operation as of the trade-off between the performance improvement and the corresponding costs. By considering the filtration effects of the air intake system, the gas path analysis of the gas turbine is carried out to tackle the coupled fouling process, and a hybrid framework is presented to predict the washing cycle (remaining useful life prediction for washing) and detect filter leakage (diagnosis for filter) via integrating the thermodynamic model and long short-term memory (LSTM) neural networks. The proposed scheme is applied in a field dataset and the results show that: (i) a deterioration index based on the thermodynamic model can be used to evaluate the compressor fouling degree, which is independent of ambient conditions and control factors. (ii) A prediction model based on the LSTM-Hankel method demonstrates good performance in long-time washing cycle prediction. (iii) Air filter leakage will significantly increase the degradation rate of compressor efficiency, which can be identified by the diagnosis model to predict the new washing cycle.

Due to the complex and harsh environmental factors, the useful life of the filter in the gas turbine air intake system is usually less than its design life. When the filter is seriously degraded, the power and thermal efficiency of the gas turbine will decrease obviously due to the increase of inlet pressure loss. For evaluating the health condition of filters in the air intake system, this work forms a filter pressure loss model with the defined health index for the filter and five external environmental and control factors. By integrating the gas path component model, the combined model is applied in a real data set and the results show that (i) the proposed health index is efficient in representing the degradation state of the filter, (ii) the influencing factors on the pressure loss are successfully decoupled and their contributions on the pressure are quantitatively estimated, and (iii) the integrated model of filter pressure loss and gas path component can be used to better estimate the deterioration states of the filter as well as the gas turbine performance.

In order to ensure safety and reliability of energy transportation, it is necessary to understand and predict the performance of the gas turbine components. A prediction frame of the gas turbine compressor isentropic efficiency is established using the neural time series theory based on the Dynamic Neural Network. In order to obtain appropriate parameters for the network, a validation set is introduced to generalize the model. The compressor isentropic efficiency can be predicted based on the suggested model which provides an effective technical mean for the early warning of gas turbine performance. The experiment verified that the performance calculation model and the isentropic entropy efficiency prediction model based on the neural time series are effective.

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.