Accuracy of SIS output data based on IMMS

In order to give full play to the upper-level application functions of the plant-level monitoring information system (SIS) of thermal power plants based on the integrated model development platform (IMMS), it is necessary not only to complete the calculation of unit parameter variables, but also to analyze the factors affecting the accuracy of parameter variables. The accuracy of SIS output data is subject to the measurement of DCS data source and the calculation of process algorithm model. According to the model calculation principle, considering data measurement, reference frequency and other factors, the accuracy of conventional thermal power unit index values ​​is graded.

1. SIS Development Principles

As shown in Figure 1, the SIS development based on IMMS adopts a modular approach, with the database as the core, and consists of a model development platform, a user terminal, etc. The operating data of each unit is input into the database through the network communication software. With the help of IMMS, the online modular model development, debugging, and operation are realized according to the basic energy and mass conservation laws in accordance with the method of system decomposition first and then integration. The process model is based on the algorithm library, calling the algorithm modules in the algorithm library. According to the data association relationship of the system, the data transmission is realized by connecting the input and output variables of each module. The data required for model calculation is read from the database through the communication software, and the calculation results are stored in the database by the communication software. Some basic data information such as equipment and fuel required by the functional model can be entered into the database by the user terminal.

The process parameters required for SIS functional model calculation mainly come from DCS, and the accuracy of DCS data will directly affect the calculation results of the model.

2. Data Source Accuracy

The generator set system has a large structure, and some of the measured parameters of its thermal system, such as temperature, pressure, flow, etc., fluctuate greatly during operation. If the collected data does not meet the accuracy requirements of SIS, its performance calculation, unit load optimization, equipment status monitoring and other functions will not achieve the expected results.

To improve the accuracy of collected data, the measurement technology needs to be improved in hardware, and the improvement of measurement technology is to a certain extent limited by the characteristics of the measuring points; the collected data needs to be screened in software, that is, first of all, significant errors need to be detected and identified, and then the process measurement data needs to be correlated with important parameters based on redundancy checks and elimination of random errors, and various effective data correction preprocessing methods need to be adopted.

The measurement parameters of the generator set mainly include temperature, pressure, feed water flow, steam flow, air flow and electrical signals. Among them, the measurement accuracy of temperature and pressure is relatively high, but the measurement accuracy of feed water flow, steam flow and air flow, which are important operating parameters, needs to be further improved.

3. Selection of mathematical model

In addition to relying on a high-precision original database, the realization of SIS upper-level functions also requires the support of process mathematical models. In order to make the overall modeling and solution not too complicated and have a certain degree of feasibility, the thermal system of the unit is usually simplified for analysis. For example: in order to effectively analyze the dynamic characteristics of the boiler, the temperature, pressure, etc. in the evaporation system, deaerator and surface heater are treated as centralized parameters, and the relationship between the parameters and the space is ignored; in order to facilitate the calculation of the gas turbine output power model, the heat storage and mass storage of the metal pipe are ignored; the waste heat boiler system is nonlinear, but in dynamic research, a small deviation linearization method under a certain stable state is often used.

4. Output result accuracy classification

The output data can be roughly divided into three categories: basic measurement parameters, performance index values, and consumption difference analysis values. Basic measurement parameters can be directly obtained through precise measurement equipment, and the accuracy is directly subject to the measurement point layout, measurement method and measurement technology; performance index values ​​are obtained through soft measurement, which is an intermediate process quantity and can be formed by one calculation; consumption difference analysis values ​​are based on the original performance index values, taking into account the changes in the unit's operating conditions, and are obtained through multiple calculations.

The fundamental factor affecting data accuracy is the measurement technology of basic parameters. Therefore, firstly, according to the characteristics of different measuring points, a simple accuracy level division is made for the main type of parameters (Table 1), and then the accuracy analysis of the measured type of parameters is carried out according to the index algorithm model. The accuracy related to the electrical signal is the highest, followed by the accuracy of the variables involving temperature, pressure and composition analysis, and the accuracy related to the working fluid flow is the lowest (Table 2, Table 3). The consumption difference analysis mainly includes thermal deviation method, conventional thermodynamic analysis and equivalent drop method. The influence of changes in the main operating parameters of the boiler side (exhaust gas temperature, exhaust gas oxygen content, fly ash carbon content, etc.) on coal consumption is analyzed by the thermal deviation method; the changes in the initial parameters including main steam temperature and pressure, superheated steam temperature, etc. are mainly attributed to the influence of changes in turbine cylinder efficiency on coal consumption, and the conventional thermodynamic method is used for analysis; the influence of changes in non-turbine side parameters (superheater, reheater desuperheating water flow, heater end difference, shaft seal leakage, etc.) on coal consumption is analyzed by the equivalent drop method. When applying conventional thermodynamic methods to analyze the consumption difference caused by changes in main steam temperature and pressure, the changes in the work done by the medium and low pressure cylinders of the turbine are ignored, and only the impact on the ideal specific heat drop of the high pressure cylinder is considered, which results in low accuracy.

In addition, different parameters have different importance and accuracy requirements in unit operation optimization. Resources should be reasonably allocated according to actual conditions, and relevant measurement points should be added or calibrated in a targeted manner.

V. Conclusion

(1) The calculation accuracy of the SIS functional model is mainly affected by the DCS acquisition data and algorithm model. To improve the output data accuracy, reasonable redundant measurement point layout, precise measurement technology and real-time data preprocessing are required.

(2) The fundamental factor affecting data accuracy is measurement technology. The accuracy of different types of data sources is classified, and the credibility of the unit's main performance and consumption difference is graded according to the data acquisition method and reference frequency.

(3) The accuracy classification of parameters varies with the unit operating conditions. Therefore, it is still necessary to analyze the measurement characteristics and algorithm principles to further explore the accuracy of SIS output data.