Research on consumption characteristic model of heating generator set

*: 2002-01 1 Charm: 4 change" (9e) Men's Mountain Institute Vg. It is the basis of the vxnki.netbookmark2 work of the electric power system, but the working condition map of the heating steam turbine is more complicated and its practicability is poor. Based on the analysis of the characteristics of steam turbine and boiler consumption characteristics, using the auxiliary curve and the least square method to curve fitting, a simple, accurate and practical mathematical model can be established, which can be used between power units and power systems. Economic dispatch.

With the increase of the installed capacity of China's power system and the gradual networking of various power systems, the single-unit capacity of the thermal power units in the power system is also increased accordingly. Especially in the further deepening reform of the power system, from the perspective of reducing power generation costs, improving the overall efficiency of power plants, and complying with environmental protection requirements, China will gradually phase out 50MW and below generator sets in recent years, so In the main power system of the unit, the 100MW unit has gradually assumed the task of peaking the system. In systems with large system load peak-to-valley differences, even 200MW heating generator sets in power plants near large cities have already participated in peak shaving, and their stability, safety, reliability, and economy during operation will be affected. A certain impact, the economics of the unit operation when it can basically guarantee other conditions has become a key issue for all power plants.

The consumption characteristics of the heating unit are composed of the consumption characteristics of the steam turbine (represented by the available heating condition map) and the consumption characteristics of the boiler. The model can be built separately or a synthetic model can be made according to different needs. The model is economic dispatching. Foundation. Since the working condition diagram of the heating steam turbine generally uses a parallel linear family of different slopes to form a characteristic relationship between the power and the steam flow, there are some problems in the economic dispatching and analysis using the working condition map. It is mainly necessary to re-execute the thermal characteristic test after the unit is overhauled, and correct the original working condition map accordingly, and the practicality when storing the picture in the computer is very poor. The boiler's consumption characteristic curve is relatively simple and relatively easy to handle. In this paper, the characteristics of turbine consumption and boiler consumption characteristics of 200MW and above unit heating generators are analyzed, and corresponding mathematical models are made.

1 Consumption characteristics model of heating steam turbines 1.1 Characteristics of consumption characteristics of heating steam turbines The heating turbines are shown by the approximate parallel linear family under constant heating pressure.

In the figure: Dk = C is an equal condensation line; De =0 is a pure condensate condition line; De = C is an equal extraction line.

=C constitutes a group of parallel linear lines, that is, the slope of the straight line under different extraction amounts is the same as the slope of the De=0 line. When the steam extraction amount C is an arbitrary value, the exact position of the straight line should be determined by applying the interpolation method according to the linear family relationship, and the relationship with De should be directly made in the mathematical model.

The =C line indicates that when the amount of steam discharged into the condenser is constant, the change in electric power is achieved by changing the amount of steam entering and the amount of steam extracted. The processing method in the mathematical model also needs to make an interpolation relationship of any C value. .

1.2 Treatment method of working condition diagram Model In the working condition diagram of the heating steam turbine, the D=0=min line of the De =0 line and the minimum amount of steam discharged into the condenser is the most basic curve, which can be determined by the thermal characteristic test. According to this, the line family corresponding to the two straight lines can be obtained.

De=0 line can take two points from the curve. The line equation is determined by the undetermined coefficient method. The slope of the parallel line family De=C is the same as that of C02. The straight line of the steam extraction quantity can be taken from the figure. 1) Find the intercept and thus the auxiliary curve of the linear family intercept relationship (that is, the relationship between c0i and the extraction amount De), which can form a straight line under any extraction amount. The relationship is obtained by the square method as shown.

Table 1 pure condensing steam condition line data and fitting error table 2 steam extraction and linear family intercept relationship data and fitting error table 3 discharged into the condenser steam amount Dk = min data and fitting error due to the curve is not Linear curve, and its characteristics are basically convex and monotonously rising. In the economic dispatch of power system, curve fitting is generally adopted in the form of n-degree polynomial. The accuracy of fitting with quadratic curve is not high enough, but it is used four times. The accuracy of the above curve fitting is almost incomparable compared with the cubic curve, and it is cumbersome in practical use. Therefore, for the sake of simplicity of application, a cubic polynomial fit can be used, in the form of coefficients of the characteristic curve.

In order to ensure the accuracy of the fitting, at least 8 points of data can be taken in the effective interval shown on the curve, and the cubic polynomial fitting can be performed by the least square method. Set the number of m test points = 1, 2, ..., m). The equations formed according to the least squares method are any ones that solve the linear equations, and the unknowns c21, c22, c23, c24, *S(8) in equation (8) can be easily solved. The relationship between the number of test points and the coefficient is not one-to-one correspondence. The more test points, the more accurate the fit. The fitting curve should be analyzed for error. According to the statistical method, the following indicators should be analyzed: the index.

The indicator of the accuracy of the fitting.

3 Calculation example The following example shows how to build the model with a 200MW heating unit as an example. The steam turbine is of type KC200/150*12.7/535/535, and the boiler is of type DG670/13.7-8. The data of the example are obtained from the relevant data of the steam turbine and boiler thermal characteristics test report.

There are more data on pure condensing condition test. See Table 1 for the least squares method. The fitting error is also listed in the table. Calculated Q=282. The relationship between the extraction volume and the linear family intercept and the fitting error are shown in Table 2. The data of the Q-inflow condenser steam and the linear family intercept and the fitting error are shown in Table 4. Q=5.49X10-6, S=1.66X*3, R2=1. In general, the smaller the values ​​of Q and S, the better, but there is no specific standard (6): cu(Dk). The closer the coefficient is to 1, the better the general use of this as the toe 413 surplus with 0- interesting one 31 coal mine consumption characteristics data and fitting error are shown in Table 5, can be calculated as Q = Table 4 discharged into the condenser and linear family intercept Data and fitting error Table 5 boiler consumption characteristic data and fitting error serial number error /%, the accuracy can be seen in the example. The whole model can be used for economic dispatch between units in a power plant with a few simple analytical formulas, as well as for economic dispatching of power systems, and because the model is very simple and can even be used in real-time scheduling.

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