Research On Reversed Modeling Method For Thermal Power Unit

Due to the characteristics of nonlinear, time-varying, multi-variable, multi-loop coupling and different responding speed, the thermal system modeling method of power unit is always one of the challenging projects. The thermal system modeling method is a basic project in the field of the system operation optimization, control, performance evaluation, fault diagnosis, stimulation and etc. To solve the problems of the thermal system model in the application, a new modeling method called reversed modeling method was developed in the paper which aims at overcoming the lower efficiency and lower accuracy of the thermal system model.The basic definition, general modeling mode, requirements, significance and the applications of inversed modeling method were presented in the paper first time. A more integrated method and theory were preliminarily built. Real-time data validation, feature variables extraction, modeling algorithm including neural network, partial least squares algorithm, support vector machines and genetic algorithm, reversed modeling feasibility and model accuracy validation were studied in detail in the paper.Real-time data validation methods were studied in the paper because to ensure the creditability of modeling data is the basis for reversed modeling method. Different measurment parameters have different measuing point numbers, so data were divided into two categories to test. For single and double measuring point data, two real-time data validation methods are studied in the paper. One is based on the rate of adjacent data change and the other is based on curve fitting residual. To avoid the misjudgment of data validation on some special conditions with above two methods, the change rate of a measuring point data associated with the testing data could be taken as the reference data so as to improve the accuracy of model. For multi-point measurement data, Grubbs was recommended for real-time data validation. The results show that the methods can removed and regulate gross error effectively.Feature variables extraction is a very important issue for reversed modeling method, because feature variables have a great impact on the modeling accuracy. According to the exploration on the feature variables extraction methods, gray relational analysis was recommended for feature variables extraction in the paper. Useing a small amount of input variables can improve the model accuracy and speed. The example of main steam flow model demonstrates the effectiveness and availability of the variety selection method suggested in the paper for independent variables.After reversed modeling theory and method are established, reversed modeling accuracy and feasibility are studied in the paper. There are two type of relationship between power plant operation data. The first one is that the relationship between the independent variables and relationship between independent variables and the dependent variable are linear correlation; another one is that the relationships between the independent variables are linear correlation, but the relationship between the independent variables and the dependent variable are less correlation. For the first case, partial least squares algorithm was adopted for modeling. For the second case, neural network and support vector machines algorithm were developed for modeling in the paper. In the paper, the reversed modeling method was applied to build the mathematic model of main stream flow, steam turbine exhaust enthalpy, unburned carbon in fly ash, the high-temperature surface metal temperature and boiler intermediate point temperature of supercritical unit. The research results show that the models built in the paper are useful for solving the problem of the on-line performance computing and the redundancy analysis of key parameters. The modeling examples indicated that reversed modeling method was feasible with high accuracy. Reversed modeling method is a new way for thermal power unit modeling.