Performance monitoring of steam turbine regenerative system based on extreme learning machine

The performance conditions of steam turbine regenerative system have important influence on the safety and economy of the units. It is of great significance to doing the research on the performance monitoring of the regenerative system to ensure the safe and economical operation of the whole coal-fired power plants. In view of the shortcomings of the complexity of traditional performance monitoring methods, this paper presents a method of performance degradation of steam turbine regenerative system based on Extreme Learning Machine (ELM). The training set is constructed by using the actual running data of steam turbine regenerating system in previous year, besides the input variables and output variables are selected in combination with its operating mechanism analysis to train the ELM model. Then, the model was used to forecast respectively two test set that include previous year's data sample except training set and next year's data sample. The residual line is derived from the discrepancy between the actual values and predicted values of test sets. By comparing the residual line between the previous year's test set and next year's test set, we can see that the heat transfer performance of regenerators has deteriorated. Compared with the actual performance of regenerators, it is verified that the model can effectively monitor the performance of regenerators. In addition, some comparison experiments between ELM, Artificial Neural Networks (ANN) and Elman Regression Neural Networks (ERNN) are taken to compare the performance of each algorithms above mentioned. Through these comparative tests, it is shown that different methods have a certain effect on the performance degradation monitoring of regenerators, but the ELM algorithm is better sensitive and much faster execution than ANN and ERNN.