Thermal power generation systems have stringent requirements for water and steam quality

i.e.

condensate water quality is one of the critical issues. In this paper

we designed a two-layer model based on an autoencoder and expert knowledge to achieve the early warning and causal analysis of condensate water quality abnormalities. An early warning model using an autoencoder model is built based on the historical data affecting the condensate water quality. Next

an analytical model of condensate water quality abnormalities was then developed by combining expert knowledge and trend test algorithms. Two different datasets were used to test the proposed model

respectively. The accuracy of the autoencoder model in the short-period test set is 88.83%

which shows that the early warning model can accurately analyze the condensate water quality data and achieve the purpose of early warning. For the long-time period test set

the model can correctly identify each abnormality and simultaneously indicates the cause of the abnormal condensate water quality. The proposed model can correctly identify abnormal working conditions and it is applicable to other thermal power plants.