Turbine set thermodynamic system robust data coordination method and system based on equipment characteristic equation

Abstract

The invention relates to the technical field of steam turbine unit thermodynamic system data processing, and discloses a steam turbine unit thermodynamic system robust data coordination method and system based on an equipment characteristic equation. The method comprises the following steps of: acquiring a measured variable and an unmeasured variable in a steady-state operation process of a thermodynamic system of a turbine set of a power plant; establishing a robust estimation function about measured variables, and establishing constraint conditions about the measured variables and unmeasured variables according to a mass and energy conservation equation of the system and an equipment characteristic equation; and establishing a data coordination model by using the robust estimation function and the constraint condition, and solving the model to obtain a coordination value of a measured variable and an estimated value of an unmeasured variable so as to realize data coordination. According to the invention, the problem that the data coordination result is inaccurate and is easily influenced by significant errors is solved.

Description

Technical Field

[0001] This invention belongs to the technical field of data processing for steam turbine thermal systems, and more specifically, relates to a robust data coordination method and system for steam turbine thermal systems based on equipment characteristic equations. Background Technology

[0002] With continuously growing energy demand and increasingly stringent environmental standards, the efficient and stable operation of power plants is crucial. As the core energy conversion equipment in thermal and nuclear power plants, the performance of the steam turbine unit's thermal system directly affects the overall efficiency and carbon emission levels. Accurate performance monitoring and optimized operation control of the thermal system must be based on highly reliable process data. Therefore, ensuring the accuracy and reliability of thermal system process data has significant practical implications.

[0003] Data reconciliation is a data processing method that utilizes system redundancy and physical constraints to reduce errors in measurement data and estimate unmeasured variables, thereby improving measurement accuracy. However, in the actual operation of thermal systems, measuring instruments are susceptible to random and significant errors due to environmental factors such as high temperature and high pressure. Existing data reconciliation methods lack robustness, and significant errors can easily spread to the overall data, contaminating other data that do not contain significant errors. Furthermore, the number of redundant measurement points in actual thermal systems is limited, and existing data reconciliation methods cannot effectively utilize this measurement redundancy, resulting in reconciliation results that fail to reflect the true operating state of the system. Therefore, a data reconciliation method that can solve the above problems is needed. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a robust data coordination method and system for steam turbine thermal systems based on equipment characteristic equations, solving the problems of inaccurate data coordination results and susceptibility to significant errors.

[0005] To achieve the above objectives, according to one aspect of the present invention, a robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations is provided, the method comprising the following steps: Acquire measured and unmeasured variables during the steady-state operation of the thermal system of a power plant turbine unit; Establish a robust estimation function for the measured variables, and simultaneously establish constraints for the measured and unmeasured variables based on the system's mass and energy conservation equations and equipment characteristic equations. By establishing a data reconciliation model using the robust estimation function and constraints, solving the model yields the reconciled values ​​of the measured variables and the estimated values ​​of the unmeasured variables, thereby achieving data reconciliation.

[0006] More preferably, the measured variables include flow rate, pressure, and temperature.

[0007] More preferably, the formula for the robust estimation function is as follows:

[0008] in, For robust estimation functions, To measure the standard residual, To measure the harmonized value of the variable, For the measured value of the variable, To measure the standard deviation of the variable, is an adjustable parameter of the robust function.

[0009] More preferably, the constraints are as follows:

[0010]

[0011] in, This is a set of constraint equations for the conservation of mass and energy. For the coordinate value vector of the measurement variables, This is a vector of estimated values ​​for the unmeasured variables. The equipment characteristic equations are as follows: This is a vector of device characteristic variables.

[0012] More preferably, the data coordination model is:

[0013] in, For piecewise robust estimation functions, To measure the number of variables, For the first The harmonized values ​​of the measured variables, For the first The measured values ​​of each measurement variable, For the first The standard deviation of each measurement variable For the first The measurement standard residuals of each measurement variable, The robust function has adjustable parameters. This is a set of constraint equations for the conservation of mass and energy. For the coordinate value vector of the measurement variables, This is a vector of estimated values ​​for the unmeasured variables. The equipment characteristic equations are as follows: This is a vector of device characteristic variables.

[0014] More preferably, the mass and energy conservation constraint equations This includes the mass balance equations and energy balance equations of the turbine generator's thermal system, as well as the equipment characteristic equations. This includes the heat transfer characteristic equations for the high-pressure feedwater heater in the equipment.

[0015] More preferably, after obtaining the harmonized values ​​of the measured variables and the estimated values ​​of the unmeasured variables, the effect of harmonization is evaluated according to the following formula:

[0016]

[0017] in, and The first Uncertainty of a measured or unmeasured variable, , is the confidence coefficient Number of operating conditions and For the first The measured variable or unmeasured variable in the th... The coordination value or estimated value under each working condition and For the first The true value of a measured or unmeasured variable.

[0018] More preferably, after obtaining the harmonic value of the measured variable, it is determined whether the measurement standard residual of the measured variable is greater than a preset threshold. If it is greater than the preset threshold, the measured variable is abnormal data containing significant error; otherwise, it is normal data without significant error.

[0019] According to another aspect of the present invention, a robust data coordination system for a steam turbine thermal system based on equipment characteristic equations is provided. The system includes an actuator for performing the robust data coordination method for a steam turbine thermal system based on equipment characteristic equations described above.

[0020] According to another aspect of the present invention, a computer storage medium is provided having a computer program stored thereon, the computer program being used to implement the robust data coordination method for a steam turbine thermal system based on equipment characteristic equations as described above.

[0021] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art: 1. This invention treats data coordination as a constrained optimization problem. Regarding the objective function, it proposes a piecewise estimation function with stronger robustness based on the measured variables. Regarding constraints, it introduces the unit equipment characteristic equation to improve the system's measurement redundancy. Finally, it uses the constructed robust new data coordination model to coordinate the data of the turbine unit's thermal system, obtaining coordinated values ​​for measured variables and estimated values ​​for unmeasured variables. This effectively suppresses the influence of significant errors, making the coordination results closer to the true values ​​and detecting the location of significant errors.

[0022] 2. The robust estimation function of the present invention maintains appropriate weights within different error ranges, which can effectively suppress significant errors and improve the accuracy of data coordination and the ability to detect significant errors.

[0023] 3. This invention addresses the problem of limited redundant measuring points in the thermal system of a steam turbine unit by introducing the characteristic equation of the unit equipment to improve the measurement redundancy of the measurement system, thereby obtaining coordinated results that are closer to the true values.

[0024] 4. The robust data coordination method described in this invention has good overall performance. The coordination results obtained can not only accurately satisfy the system constraint equations, but also reflect the actual operating status of the unit, thereby better guiding the performance monitoring and operation optimization of the unit. Attached Figure Description

[0025] Figure 1 This is a schematic flowchart of a robust data coordination method for a steam turbine thermal system constructed according to a preferred embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of a high-pressure feedwater thermal system for a steam turbine unit constructed according to a preferred embodiment of the present invention.

[0027] Figure 3 This is a comparison graph of the novel robust estimation function constructed according to a preferred embodiment of the present invention with other estimation functions.

[0028] Figure 4 This is a comparison graph of the influence functions of the novel robust estimation function constructed according to a preferred embodiment of the present invention with the influence functions of other estimation functions.

[0029] Figure 5 This is a comparison chart of the uncertainty of mass flow measurement variables obtained after data coordination, constructed according to a preferred embodiment of the present invention, and the uncertainty of mass flow measurement variables obtained by other methods.

[0030] Figure 6 This is a comparison chart of the uncertainty of unmeasured variables obtained after data reconciliation, constructed according to a preferred embodiment of the present invention, and obtained by other methods.

[0031] Figure 7 This is a comparison chart showing the decrease in uncertainty of pressure, temperature, and mass flow rate measurement variables before and after the introduction of the equipment characteristic equation, constructed according to a preferred embodiment of the present invention.

[0032] Figure 8 This is a comparison chart of the uncertainty of temperature measurement variables before and after data coordination, constructed according to a preferred embodiment of the present invention.

[0033] Figure 9 This is a comparison chart of the uncertainty of mass flow measurement variables before and after data coordination, constructed according to a preferred embodiment of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0035] like Figure 1 As shown, the robust data coordination method for a steam turbine thermal system based on equipment characteristic equations provided by this invention specifically includes the following steps: (1) From Figure 2 The steady-state operation measurement data, including mass flow rate, pressure, and temperature, were obtained from the high-pressure feedwater thermal system of the power plant turbine unit. Based on the redundant measurement information of the thermal system, measurement variables participating in the data coordination calculation of the thermal system were selected. , respectively denoted as The measured values ​​are recorded as follows: And select unmeasured variables , respectively denoted as There are 26 measured variables and 8 unmeasured variables. All selected measured variables are reconcilable, all unmeasured variables are estimable, and all variables can be obtained by solving the system constraint equations and some of the measured variables.

[0036] (2) Based on the model of the equipment in the thermal system of the power plant turbine unit, construct a model for the measured variables. and unmeasured variables Mass and energy conservation constraint equations : (1) Further construct the equipment characteristic equation set To improve the measurement redundancy of the turbine unit's thermal system: (2) in, This is a set of constraint equations for the conservation of mass and energy. The equipment characteristic equations are as follows: For the coordinate value vector of the measurement variables, This is a vector of estimated values ​​for the unmeasured variables. This is a vector of equipment characteristic variables, representing the inherent characteristics of the equipment, which remain essentially constant over a short period of time or under steady-state conditions.

[0037] In this embodiment, the main equipment of the turbine unit's thermal system includes a three-stage high-pressure feedwater heater, a feedwater pump, and a deaerator. The equipment characteristic equations are as follows: This treats a high-pressure feedwater heater as a single heat exchange unit, and constructs the overall heat transfer equation based on the overall heat transfer process of the heater, as shown below: (3) in, For the total heat absorbed by the feedwater heater, The overall heat transfer coefficient is... For heat transfer area, The logarithmic mean temperature difference can be specifically expressed as: , This is the difference between the extraction steam temperature and the feedwater outlet temperature. This is the difference between the hydrophobic temperature and the feedwater inlet temperature; due to the increased heat transfer area... Its design and manufacturing process remain unchanged, and its overall heat transfer coefficient is constant. The variation is minimal within a certain load range, therefore it can be considered that the data coordination process... It is a constant value, which is the device characteristic variable in this embodiment.

[0038] The number of unmeasured variables in the system is 8, the number of mass and energy conservation constraint equations is 12, and the number of equipment characteristic equations is 3. Therefore, before and after the introduction of equipment characteristic equations, the redundancy of the system increases from 12-8=4 to 12+3-8=7.

[0039] (3) Existing data reconciliation methods lack robustness, and the reconciliation results are easily affected by significant errors. Based on this, the robust estimation function proposed in this invention serves as the objective function for data reconciliation, possessing a piecewise form and exhibiting stronger robustness: (4) in, For robust estimation functions, To measure the standard residual, To measure the harmonized value of the variable, For the measured value of the variable, To measure the standard deviation of the variable, is an adjustable parameter of the robust function.

[0040] The influence function, as an important evaluation metric for the estimation function, measures the impact of the measurement standard residuals on the data reconciliation model. The influence function of this robust estimation function is as follows: (5) in, For the influence function, The meaning is the same as in equation (4).

[0041] To evaluate the effectiveness of the data reconciliation method based on the novel robust estimation function in suppressing significant errors, the novel robust estimation function is compared with the existing Fair, Xiong, and Luan estimation functions and their influence functions. The Fair, Xiong, and Luan estimation functions can be represented by equations (6)-(8), respectively. The comparison results are as follows: Figure 3 and Figure 4 As shown: Fair estimation function: (6) Xiong's estimation function: (7) Luan estimation function: (8) in, These are the adjustable parameters of the estimation functions for Fair, Xiong, and Luan, respectively, with values ​​as follows: The novel robust estimation function in this embodiment is called the Proposed estimation function, and its adjustable parameter takes the value of .

[0042] from Figure 3 and Figure 4 It can be seen that the Proposed estimation function is bounded and has a small bound value, which is close to the bound values ​​of the Xiong and Luan estimation functions. The Fair influence function eventually converges to a non-zero value, which can weaken the influence of significant errors, but is still affected by large bias significant errors. The influence functions of the other three estimations all converge completely to zero, which can effectively suppress the propagation of large bias significant errors. Compared with Xiong and Luan, the Proposed influence function does not blindly pursue an extremely fast convergence speed, but maintains a slower convergence speed in the region of small bias significant errors, so that the measurement data in this region maintains an appropriate weight. This is extremely effective in improving the detection rate of small bias significant errors and reducing the false positive probability of significant errors.

[0043] (4) Furthermore, the proposed robust data coordination model is expressed as a constrained optimization problem: (9) in, The piecewise robust estimation function and the adjustable parameter of the robust function are shown in equation (4) respectively. In this embodiment, , In this embodiment, to measure the number of variables, , For the first The harmonized values ​​of the measured variables, For the first The measured values ​​of each measurement variable, For the first The standard deviation of each measurement variable For the first The measurement standard residuals of each measurement variable, The meaning is the same as in equations (1) and (2).

[0044] Combined with the measured values ​​of the measurement variables Standard deviation of the measured variable Mass and energy conservation constraint equations Equipment characteristic equations The proposed robust data reconciliation model can be used to solve for the reconciliation values ​​of the measured variables. and estimates of unmeasured variables .

[0045] (5) Use uncertainty to measure the measured variable with unmeasured variables The lower the uncertainty in the data reconciliation effect, the better the data reconciliation effect. (10) (11) in, and The first Uncertainty of a measured or unmeasured variable, , is the confidence coefficient Number of operating conditions and For the first The measured variable or unmeasured variable in the th... The coordination value or estimated value under each working condition and For the first The true values ​​of the measured or unmeasured variables are selected in this embodiment, with a confidence level of 0.95. Number of operating conditions .

[0046] For the Each measurement variable is calculated by reconciling the measurement standard residuals after data adjustment. and the adjustable parameters of the robust function By comparison, the location of significant errors in the measurement data can be detected; if This indicates that the first The measured variable is normal data that does not contain significant error; otherwise, it is considered that the... The measured variables are outlier data containing significant errors.

[0047] Measured variables include flow rate, pressure, and temperature. Unmeasured variables are quantities that are difficult to measure directly. All unmeasured variables can be estimated from constraints and some measured variables.

[0048] The present invention will be further described below with reference to specific embodiments thereof.

[0049] The invention will now be illustrated using the feedwater regeneration system of a 660MW subcritical thermal power plant turbine unit as an example. Figure 2 As shown, the key equipment in the power plant's thermal system includes: high-pressure feedwater heaters (HP1, HP2, HP3), feedwater pumps (FWP), and deaerators (DEA). Appropriate measured and unmeasured variables were selected from these devices, resulting in 26 measured variables and 8 unmeasured variables. The measured variables in this embodiment are... , Unmeasured variables are .

[0050] Based on the mass balance equation, energy balance equation, and equipment characteristic equation of the turbine unit's thermodynamic system, constraint equations for measured and unmeasured variables are established. There are 12 mass and energy conservation constraint equations and 3 equipment characteristic equations. The system constraint equations are shown in Table 1.

[0051]

[0052] In Table 1, q, t, and p represent mass flow rate, temperature, and pressure, respectively. The water supply outlet mass flow rate for primary, secondary, and tertiary high-pressure heaters. The extraction steam mass flow rate for the first, second, and third stage high-pressure heaters. The hydrophobic mass flow rates for primary, secondary, and tertiary hyperbaric heaters are as follows: This refers to the mass flow rates at the deaerator's feedwater outlet, extraction steam, and feedwater inlet. For the feedwater pump's outlet mass flow rate, temperature, and pressure, For the mass flow rate, temperature, and pressure of the water sprayed into the reheater, The feedwater outlet temperature for primary, secondary, and tertiary high-pressure heaters. The extraction steam temperatures for the first, second, and third stage high-pressure heaters. The hydrophobic temperature ranges for primary, secondary, and tertiary high-pressure heaters. The extraction steam and feedwater inlet temperatures of the deaerator. The extraction pressure of the first, second, and third stage high-pressure heaters. The steam extraction and feedwater inlet pressures of the deaerator. To be at a certain temperature and pressure The specific enthalpy of steam or water below. In order to be under certain pressure Enthalpy of saturated water at the following conditions This refers to the total heat absorbed by the first, second, and third stage high-pressure heaters. The total heat transfer coefficients for stage I, II, and III high-pressure heaters. For the heat transfer area of ​​primary, secondary, and tertiary high-pressure heaters, The logarithmic mean temperature difference for Level 1, Level 2, and Level 3 high-pressure heaters.

[0053] In this embodiment, a total of 100 sets of steady-state operation measurement data of the turbine unit's thermal system were acquired, and the mass flow rate of the feedwater outlet of the first-stage high-pressure heater was also measured. Mass flow rate at the outlet of the feed water pump By adding significant errors of different magnitudes, and combining them with the system's constraint equations, the data reconciliation problem is constructed and solved.

[0054] The robust data reconciliation method provided by this invention is compared and analyzed with other robust data reconciliation methods, such as those based on Xiong and Luan estimation functions. The uncertainties of the mass flow measurement variables and unmeasured variables after data reconciliation are calculated for each method, and plotted. Figure 5 and Figure 6 As can be seen, the robust data reconciliation method provided by this invention can obtain reconciliation results with lower uncertainty, indicating that the novel robust data reconciliation method can not only improve the accuracy of measured variables, but also accurately estimate unmeasured variables. The reconciliation results are not affected by significant errors and have strong robustness.

[0055] Figure 7 This paper demonstrates a comparison of the reduction in uncertainty of measured variables before and after data reconciliation using the robust data reconciliation method provided by this invention, with and without the introduction of equipment characteristic equations. As shown in the figure, after introducing the equipment characteristic equations, the uncertainty of temperature and mass flow rate measurements decreases significantly, while the uncertainty of pressure measurement decreases slightly. This indicates that introducing the equipment characteristic equations can greatly improve the data reconciliation effect of temperature and mass flow rate measurements, making them closer to the true values, thereby improving the overall accuracy of the measured variables.

[0056] Figure 8 and Figure 9 This paper demonstrates a comparison of the uncertainties of temperature and mass flow rate measurement variables before and after data reconciliation using the robust data reconciliation method provided by this invention. It can be seen that the uncertainties of several temperature and mass flow rate measurement variables significantly decreased after data reconciliation, particularly the mass flow rates at the outlet of the first-stage high-pressure feedwater system, which contained significant errors. Mass flow rate at the outlet of the feed water pump The uncertainties were reduced by 85.5% and 78.1%, respectively. The standard residuals of the two measured variables were then calculated to be 5.95 and 4.05, respectively, both greater than the adjustable parameter of the robustness function. This indicates that the robust data coordination method provided by the present invention can correctly detect the location of significant errors in the measurement data.

[0057] This invention proposes a novel robust estimation function based on the robust estimation principle and introduces it into the unit equipment characteristic equation, thereby constructing a new robust data coordination method. This method can improve the measurement redundancy of the system, and even if there are significant errors in the measurement data, it can still obtain accurate data coordination results to reflect the true operating state of the system and correctly detect the location of significant errors.

[0058] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations, characterized in that, The method includes the following steps: Acquire measured and unmeasured variables during the steady-state operation of the thermal system of a power plant turbine unit; Establish a robust estimation function for the measured variables, and simultaneously establish constraints for the measured and unmeasured variables based on the system's mass and energy conservation equations and equipment characteristic equations. By establishing a data reconciliation model using the robust estimation function and constraints, solving the model yields the reconciled values ​​of the measured variables and the estimated values ​​of the unmeasured variables, thereby achieving data reconciliation.

2. The robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations as described in claim 1, characterized in that, The measured variables include flow rate, pressure, and temperature.

3. The robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations as described in claim 1, characterized in that, The formula for the robust estimation function is as follows: in, For robust estimation functions, To measure the standard residual, To measure the harmonized value of the variable, For the measured value of the variable, To measure the standard deviation of the variable, is an adjustable parameter of the robust function.

4. The robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations as described in claim 3, characterized in that, The constraints are as follows: in, This is a set of constraint equations for the conservation of mass and energy. For the coordinate value vector of the measurement variables, This is a vector of estimated values ​​for the unmeasured variables. The equipment characteristic equations are as follows: This is a vector of device characteristic variables.

5. A robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations as described in claim 4, characterized in that, The data coordination model is as follows: in, For piecewise robust estimation functions, To measure the number of variables, For the first The harmonized values ​​of the measured variables, For the first The measured values ​​of each measurement variable, For the first The standard deviation of each measurement variable For the first The measurement standard residuals of each measurement variable, The parameters are adjustable for the robust function. This is a set of constraint equations for the conservation of mass and energy. For the coordinate value vector of the measurement variables, This is a vector of estimated values ​​for the unmeasured variables. The equipment characteristic equations are as follows: This is a vector of device characteristic variables.

6. The robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations as described in claim 1, characterized in that, Mass and energy conservation constraint equations This includes the mass balance equations and energy balance equations of the turbine generator's thermal system, as well as the equipment characteristic equations. This includes the heat transfer characteristic equations for the high-pressure feedwater heater in the equipment.

7. The robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations as described in claim 1, characterized in that, After obtaining the harmonized values ​​of the measured variables and the estimated values ​​of the unmeasured variables, the effectiveness of the harmonization is evaluated according to the following formula: in, and The first Uncertainty of a measured or unmeasured variable, , is the confidence coefficient Number of operating conditions and For the first The measured variable or unmeasured variable in the th... The coordination value or estimated value under each working condition and For the first The true value of a measured or unmeasured variable.

8. The robust data coordination method for a steam turbine unit thermal system based on equipment characteristic equations as described in claim 1, characterized in that, After obtaining the harmonic values ​​of the measured variables, it is determined whether the measurement standard residuals of the measured variables are greater than a preset threshold. If they are greater than the preset threshold, the measured variables are abnormal data containing significant errors; otherwise, they are normal data without significant errors.

9. A robust data coordination system for a steam turbine unit thermal system based on equipment characteristic equations, characterized in that, The system includes an actuator for performing a robust data coordination method for a steam turbine thermal system based on equipment characteristic equations, as described in any one of claims 1-8.

10. A computer storage medium having a computer program stored thereon, characterized in that, The computer program is used to implement the robust data coordination method for a steam turbine thermal system based on equipment characteristic equations as described in any one of claims 1-8.

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