A method and system for measuring steam turbine efficiency

Abstract

This invention discloses a method and system for measuring steam turbine efficiency, relating to the field of steam turbine efficiency measurement technology. The method includes the following steps: calibrating multiple sets of measurement data under different steady-state operating conditions of the steam turbine to obtain corresponding corrected measurement data values; obtaining the corresponding uncertainty of the steam turbine efficiency through the covariance matrix between the corrected measurement data values; inputting multiple sets of measurement data under dynamic operating conditions and the standard steam turbine efficiency as system state inputs to an extended Kalman filter to obtain the corrected steam turbine efficiency and corresponding uncertainty under dynamic operating conditions; fusing the steam turbine efficiency and uncertainty under any steady-state operating condition with the steam turbine efficiency and uncertainty under the corresponding dynamic operating condition to obtain the optimal estimate of the steam turbine efficiency. This invention obtains more accurate measurement data by combining steady-state and dynamic measurement data, thus improving the accuracy of steam turbine efficiency estimation.

Description

Technical Field

[0001] This invention relates to the field of steam turbine efficiency measurement technology, and in particular to a steam turbine efficiency measurement method and system. Background Technology

[0002] With societal development, human demand for electricity continues to grow. As an indispensable core component of modern power systems, the operational stability and reliability of steam turbines are of paramount importance. Currently, steam turbine operation is no longer limited to stable operation under rated conditions; for peak-shaving needs, steam turbines require load regulation. When a steam turbine operates under varying conditions, its efficiency also changes. Therefore, accurately measuring steam turbine efficiency is crucial for its design and operation.

[0003] The efficiency of a steam turbine can be determined by analyzing its thermodynamic parameters. Current methods directly measure these parameters using sensors, reading directly from instruments placed on-site. However, this approach has several drawbacks. First, the results from a single sensor are susceptible to environmental and other factors, inevitably leading to errors. Second, a steam turbine is a large rotating machine, and the sensors, limited by their placement, cannot accurately measure the overall real-time parameters. Furthermore, the turbine's dynamic changes and its immeasurable thermal inertia further complicate the measurement process. Therefore, existing methods yield poor-quality data, reducing the accuracy of steam turbine efficiency measurements. Summary of the Invention

[0004] In view of the defects of the prior art, the present invention provides a method and system for measuring turbine efficiency, which solves the problem that the measurement data obtained by the existing methods is of poor quality and reduces the accuracy of turbine efficiency measurement.

[0005] The present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for measuring the efficiency of a steam turbine, comprising the following steps:

[0007] Multiple sets of measurement data for different parameters of the steam turbine were collected under different steady-state and dynamic operating conditions.

[0008] Multiple sets of measurement data under different steady-state operating conditions are calibrated to obtain corresponding measurement data correction values; the measurement data correction values ​​are input into the mapping formula between turbine efficiency and steady-state measurement data to obtain the turbine efficiency under different steady-state operating conditions; the uncertainty corresponding to the turbine efficiency under different steady-state operating conditions is obtained through the covariance matrix between the measurement data correction values.

[0009] Multiple sets of measurement data under dynamic operating conditions and the standard turbine efficiency are used as system state inputs to the extended Kalman filter to obtain the corrected turbine efficiency and corresponding uncertainty under dynamic operating conditions. The standard turbine efficiency is the design value under standard operating conditions, and the operating range of the dynamic operating conditions is the same as the total operating range of multiple steady-state operating conditions.

[0010] By fusing the turbine efficiency and corresponding uncertainty under different steady-state operating conditions with the corrected turbine efficiency and corresponding uncertainty under the corresponding dynamic operating conditions, the best estimate of the turbine efficiency is obtained.

[0011] Preferably, the step of collecting multiple sets of measurement data of different measurement parameters of the steam turbine under different steady-state and dynamic operating conditions to construct a steady-state measurement dataset and a dynamic measurement dataset includes:

[0012] Sensors are installed at different locations on the steam turbine, including temperature sensors, pressure sensors, flow sensors, and power sensors; the different measured parameters include temperature, pressure, flow rate, and power.

[0013] The turbine operating conditions were changed multiple times until the turbine reached a steady-state condition. The steady-state measurement data of multiple sensors under each steady-state condition were read and used as a steady-state measurement dataset.

[0014] By changing the operating conditions of the steam turbine, real-time measurement data from multiple sensors under dynamic conditions are read and used as a dynamic measurement dataset.

[0015] Preferably, the step of inputting the measurement data correction value into the mapping formula between turbine efficiency and steady-state measurement data includes the following steps:

[0016] Based on the work done by water vapor inside the steam turbine, the thermodynamic relationships of the steam turbine are obtained:

[0017] h0-h1=η1(h0-h 1s );

[0018] In the formula, h0 is the inlet enthalpy of the steam turbine, h1 is the exhaust enthalpy of the steam turbine, η1 is the steam turbine efficiency under different steady-state operating conditions, and h 1s This represents the ideal exhaust enthalpy value for the steam turbine.

[0019] The steam enthalpy value of the steam turbine is obtained by querying the steam property database:

[0020] H = p(x);

[0021] In the formula, H is the enthalpy vector, p is the water vapor property function, and x is the set of measurement data;

[0022] A mapping formula between turbine efficiency and measured data is established based on the thermodynamic relationship of the steam turbine and the steam enthalpy of the steam turbine:

[0023] η1 = y(x);

[0024] In the formula, y represents the mapping relationship.

[0025] Preferably, the calibration of multiple sets of measurement data under different steady-state operating conditions includes the following steps:

[0026] Add the uncertainty of the corresponding measuring instrument to the measurement data under any steady-state condition in the steady-state measurement dataset, and obtain the covariance matrix between the measurement data under any steady-state condition through the uncertainty of the measuring instrument;

[0027] A mathematical optimization problem is constructed by using the covariance matrix between the measurement data. The mathematical optimization problem is solved to obtain the correction values ​​of multiple sets of measurement data under different steady-state conditions and the unknown parameters of the steam turbine system.

[0028] The mathematical optimization problem is as follows:

[0029]

[0030] In the formula, x * Here, ξ(x) represents the correction value for the measured data, x represents the measured data, and ξ(x) represents the correction value for the measured data. * To optimize the objective function, S x Let f be the covariance matrix between the measured data, f be the steady-state thermal balance equation of the system, u be the unknown parameter, and T be the transpose.

[0031] Preferably, obtaining the uncertainty of the corresponding turbine efficiency through the covariance matrix between the corrected values ​​of the measured data specifically includes the following steps:

[0032] The covariance matrix between the corrected values ​​of the measurement data is obtained as follows:

[0033]

[0034] In the formula, Let x be the covariance matrix between the corrected values ​​of the measured data. * S is the correction value for the measurement data. x Let x be the covariance matrix between the measurement data, where x represents the measurement data and T is the transpose.

[0035] Obtain the covariance matrix of the unknown parameter based on the covariance matrix between the correction values ​​of the measurement data:

[0036]

[0037] In the formula, Su Let S be the covariance matrix of the unknown parameter u. u It includes the uncertainty of turbine efficiency.

[0038] Preferably, the step of inputting multiple sets of measurement data under dynamic operating conditions and the standard turbine efficiency as system state inputs to the extended Kalman filter to obtain the corrected turbine efficiency and corresponding uncertainty under dynamic operating conditions includes the following steps:

[0039] A variable operating condition model is constructed based on multiple sets of measurement data under dynamic operating conditions. The system state parameters of the variable operating condition model include multiple measurement parameters and turbine efficiency.

[0040] Taking any set of measurement data under dynamic operating conditions and the standard turbine efficiency as the initial system state, denoted as d0, and the initial system state parameter estimation covariance matrix as P0, the prior state estimation of the system state at the next time step is performed successively based on the system state at the previous time step:

[0041]

[0042] In the formula, and Let g be the prior estimates of the system state parameters of the steam turbine system at time m and its covariance matrix, respectively; g is the system dynamic characteristic model; and d is the system state parameter estimate. m-1 Let P be the system state at time m-1. m-1 Let u be the system state covariance matrix at time m-1. m-1 For the other input parameters of the system at time m-1, Let be the Jacobian matrix of the system dynamic characteristics with respect to the system state parameters, and Q be the covariance matrix of the system dynamic characteristic model.

[0043] The system measurement model is constructed as follows:

[0044] z m =h(d m );

[0045] Among them, z m Let m be the system's measured value at time m, and h be the system's measurement model function;

[0046] Calculate the Kalman gain matrix K of the system at time m. m for:

[0047]

[0048] in, R is the Jacobian matrix of the system dynamic characteristics with respect to the system state parameters, and R is the covariance matrix of the system measurement model.

[0049] Calculate the posterior estimates of the system state parameters and the posterior estimate of the covariance matrix using the following formula:

[0050]

[0051] In the formula, I is the identity matrix, and d m Let P be the posterior estimate of the system state parameters at time m. m The covariance matrix of the system state parameters is the posterior estimate.

[0052] Through iterative calculation, the posterior estimates of the system state parameters and the posterior estimates of the system state parameter covariance matrix at each time point are obtained.

[0053] The turbine efficiency η for any operating condition under dynamic conditions is obtained by extracting the posterior estimate of the system state parameters. d,i The variance corresponding to the turbine efficiency is extracted from the posterior estimate of the system state parameter covariance matrix.

[0054] Preferably, the step of fusing the turbine efficiency and corresponding uncertainty under different steady-state operating conditions with the corrected turbine efficiency and corresponding uncertainty under the corresponding dynamic operating conditions includes the following steps:

[0055] For the i-th dynamic operating condition, the following estimation equation is established:

[0056] Z = η s,i +K(η d,i -η s,i );

[0057] in,

[0058]

[0059] In the formula, Z is the best estimate of the turbine efficiency, K is the fusion coefficient, and η s,i Let be the turbine efficiency under the i-th steady-state condition. Let η be the uncertainty of the turbine efficiency under the i-th steady-state condition. d,i The corrected turbine efficiency is defined for the i-th dynamic operating condition. Let be the uncertainty of the corrected turbine efficiency under the i-th dynamic operating condition, s be the steady-state operating condition, and d be the dynamic operating condition.

[0060] Secondly, the present invention provides a steam turbine efficiency measurement system, comprising:

[0061] The data acquisition module is used to collect multiple sets of measurement data of different measurement parameters of the steam turbine under different steady-state and dynamic operating conditions.

[0062] The steady-state correction module is used to correct multiple sets of measurement data under different steady-state operating conditions to obtain corresponding measurement data correction values; the measurement data correction values ​​are input into the mapping formula between turbine efficiency and steady-state measurement data to obtain the turbine efficiency under different steady-state operating conditions; the uncertainty corresponding to the turbine efficiency under different steady-state operating conditions is obtained through the covariance matrix between the measurement data correction values.

[0063] The dynamic correction module is used to input multiple sets of measurement data under dynamic operating conditions and the standard turbine efficiency as system state inputs to the extended Kalman filter to obtain the corrected turbine efficiency and corresponding uncertainty under dynamic operating conditions. The standard turbine efficiency is the design value under standard operating conditions, and the operating range of the dynamic operating conditions is the same as the total operating range of multiple steady-state operating conditions.

[0064] The fusion module is used to fuse the turbine efficiency and corresponding uncertainty under different steady-state operating conditions with the corrected turbine efficiency and corresponding uncertainty under the corresponding dynamic operating conditions to obtain the best estimate of the turbine efficiency.

[0065] Compared with the prior art, the above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects:

[0066] This invention first collects multiple sets of measurement data for different parameters of a steam turbine under various steady-state and dynamic operating conditions. Then, it corrects these measurement data under different steady-state conditions to obtain corresponding corrected values, resulting in high-quality steady-state measurement data. The turbine efficiency and corresponding uncertainty under steady-state conditions are obtained using these corrected values. Finally, an extended Kalman filter is used to correct the turbine efficiency under dynamic conditions, yielding the corrected turbine efficiency and corresponding uncertainty under dynamic conditions.

[0067] This invention leverages the high data quality of steady-state measurements by correcting measurement data under different steady-state conditions to obtain the variation of turbine efficiency values ​​under various steady-state conditions. Through data fusion, it effectively utilizes data from multiple sensors under different test conditions, integrating the results. This overcomes the limitations of single-sensor accuracy and data fluctuations caused by measurement interference. By combining steady-state and dynamic measurement data, more accurate measurement data is obtained, improving the accuracy of turbine efficiency estimation. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 This is a flowchart of a steam turbine efficiency measurement method according to the present invention;

[0070] Figure 2 This is a schematic diagram of the turbine efficiency measurement method of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] In order to solve the problems existing in the current technology, referring to Figure 1 This invention provides a method for measuring the efficiency of a steam turbine, specifically a method for measuring the peak-shaving operation efficiency of a steam turbine based on data fusion, comprising the following steps:

[0073] S1: Measurement of steady-state operating condition data.

[0074] Reference Figure 2 After the temperature, pressure, flow, and power sensors in the device of this invention are prepared and checked to ensure that all parts are set correctly, the turbine measuring device is started. Once the measuring device enters a steady-state operating condition, measurement data is read. The data acquisition time for a certain steady-state condition is recorded as 10 to 30 minutes, and the average data for this period is denoted as vector x1. The turbine operating conditions are then varied until a steady state is reached. This variation operation is repeated k-1 times, ensuring that the steady-state conditions cover the main operating range of the turbine. All the data obtained is denoted as the dataset, which can be described as follows:

[0075] X = (x1, x2, ..., x) k (1);

[0076] S2: Dynamic operating condition data measurement.

[0077] After the temperature, pressure, flow, and power sensors in the measuring device are prepared and checked to ensure all settings are normal, the turbine measuring device is started, ensuring the dynamic process range matches the steady-state operating range. Real-time measurement data of the turbine during this operating process are recorded. The total measurement time period can be discretized into m time points, denoted as t1, t2, ..., t... m Since this device has n measuring points, the measurement data can be recorded as follows:

[0078]

[0079] In the formula, d nm (t m () refers to the measurement data of the nth measuring point at time m.

[0080] S3: Thermodynamic model construction.

[0081] The expansion of steam within the turbine, which does work, can be considered an isentropic process. The turbine efficiency measures the ratio of the true enthalpy drop to the ideal enthalpy drop. For the turbine in this operation, the following thermodynamic relationship applies:

[0082] h0-h1=η1(h0-h 1s (3);

[0083] Where h0 is the inlet enthalpy of the steam turbine, h1 is the exhaust enthalpy of the steam turbine, η1 is the steam turbine efficiency under different steady-state conditions, and h 1s This is the ideal exhaust enthalpy of the steam turbine. Since the enthalpy of steam in the steam turbine (including inlet enthalpy, exhaust enthalpy, and ideal exhaust enthalpy) cannot be directly measured by instruments, it needs to be obtained by looking up other measurement data in a steam property database.

[0084] H = p(x) (4);

[0085] Where H is the enthalpy vector, p is the water vapor property function, and a is the measured data set, with dataset X for steady-state conditions and dataset D for dynamic conditions. For steady-state conditions, the turbine efficiency can be calculated from the measured data. The above process for calculating turbine efficiency can be defined as a function:

[0086] η1=y(x) (5);

[0087] In this embodiment, the turbine efficiency can be calculated from the enthalpy value to obtain equation (3), and the enthalpy value can be obtained from the physical properties of the measured data to obtain equation (4), thus establishing a mapping formula (5) from the measured data to the turbine efficiency.

[0088] S4: Steady-state measurement data correction calculation.

[0089] In measurement, different instruments have different uncertainties. Therefore, the range of fluctuation in measurement data can be represented by adding uncertainty to the measured value, which can be written in the following format:

[0090] X r =(x1+δx1,x2+δx2,,x n +δx n (6);

[0091] Where δx represents the instrument's uncertainty. When the measured values ​​follow a normal distribution with a 95% confidence interval, the following relationship holds:

[0092]

[0093] Based on this, the covariance matrix between the measurement data of different measuring instruments can be calculated:

[0094]

[0095] In the formula, For the measured value x i The variance, r i,j For the measured value x i and measured value x j The empirical (estimated) correlation coefficient between them. Based on the data reconciliation method, the following mathematical optimization problem can be established:

[0096]

[0097] Where, x * Let x be the correction value for the measured data, u be parameters obtained indirectly, including enthalpy and turbine efficiency from the thermodynamic model, and f be the steady-state thermal balance equation of the system. Solving this optimization problem yields the correction value x. * and the unknown parameter u.

[0098] Then the correction value x of the measurement data * covariance matrix for:

[0099]

[0100] The covariance matrix S of the unknown parameter u can be calculated using the following formula. u This includes the uncertainty of turbine efficiency:

[0101]

[0102] By summarizing the above results, we can obtain the turbine efficiency η under the i-th steady-state condition. s,i Its corresponding uncertainty

[0103] S5: Construct a dynamic model based on multiple sets of measurement data under dynamic operating conditions.

[0104] The fundamental physical equations of the steam turbine system need to be established. For a running steam turbine, in addition to the aforementioned steady-state thermodynamic model, the following variable-condition model can be established for its dynamic process:

[0105]

[0106] in, In the above symbols, G represents the turbine flow rate, T represents the temperature, q represents the rotational speed, and π represents the expansion ratio. The subscript 1 indicates the turbine inlet, the subscript 0 indicates the rated state, and G0 represents the turbine flow rate at the rated state.

[0107] The efficiency characteristics of a steam turbine can be calculated using the following relationship:

[0108]

[0109] In the formula, η2 is the turbine efficiency under dynamic operating conditions, t1 is an empirical coefficient, and n is the rotational speed.

[0110] S6: Use an extended Kalman filter to correct the system's dynamic parameters.

[0111] After establishing the system dynamic model, the extended Kalman filter method is used for system state estimation. Let all measured system parameters and turbine efficiency be denoted as system state d. First, prior state estimation is performed: Let the initial system state be d0 (where all measured system parameters are taken from their corresponding measured values, and the turbine efficiency is taken from the design value under standard operating conditions, which can be obtained from design data). The initial system state parameter estimation covariance matrix is ​​P0, which can be successively calculated based on the state d from the previous time step. m-1 The state d at the next moment m Perform prior state estimation:

[0112]

[0113] in, and Let P be the prior estimates of the system's state parameters and their covariance matrix at time m, g be the system state transition model given by the variable operating condition model in the previous step, and P be the system state transition model. m-1 Let u be the system state covariance matrix at time m-1. m-1 For the other input parameters of the system at time m-1, Let be the Jacobian matrix of the system dynamic characteristics with respect to the system state parameters, and Q be the covariance matrix of the system dynamic characteristic model.

[0114] Based on the system's measurement value z at time m mThis can improve the aforementioned prior estimation distribution. First, based on the system's steady-state and dynamic characteristic models, the system measurement model is constructed as follows:

[0115] z m =h(d m (16);

[0116] Where h is the system measurement model function, reflecting the correspondence between system measurements and system state variables under error-free conditions. For the current system, the system state variable is defined as the sum of all system measurement parameters and turbine efficiency; therefore, the measured value z... m In fact, it is the system state variable d. m The result after removing the turbine efficiency.

[0117] Real-world measurement systems always contain errors. Let the covariance matrix of the system measurement model be R. Using the extended Kalman filter method, first calculate the Kalman gain matrix K of the system at time m. m for:

[0118]

[0119] in, Let be the Jacobian matrix of the system's dynamic characteristics with respect to the state parameters. Then, the posterior estimates of the system's state parameters are calculated using the following formula:

[0120]

[0121] Where, d m With P m Let be the prior estimates of the system state parameters at time m and their covariance matrix, respectively, and I be the identity matrix. Given initial values ​​based on engineering experience, the posterior estimates of the system state parameters at each time point can be obtained through iterative calculations. Summarizing the above results, the turbine efficiency η for any operating condition under dynamic conditions can be extracted from the posterior estimates of the system state parameters. d,i The variance corresponding to the turbine efficiency is extracted from the posterior estimate of the system state parameter covariance matrix.

[0122] S7: Data fusion processing of steady-state and dynamic turbine efficiency.

[0123] Using the steady-state operating condition results as a reference, the dynamic operating condition measurements of the turbine efficiency are obtained. It should be noted that the steady-state and dynamic operating condition results must correspond one-to-one. The following estimation equation is established:

[0124] Z = η s,i +K(η d,i -η s,i (20);

[0125] Where Z is the fused estimate of the turbine efficiency, and K is the fusion coefficient. The variance of the fused estimate can be written as:

[0126]

[0127] The above formula, obtained by transformation using the previous calculation results, is as follows:

[0128]

[0129] To minimize the previous variance, we need to differentiate the variance of the fusion estimate with respect to zero.

[0130]

[0131] After processing, the fusion coefficient is obtained as follows:

[0132]

[0133] Therefore, the measured turbine efficiency fusion estimate is:

[0134]

[0135] At this point, the best estimate of the turbine efficiency can be obtained.

[0136] Based on the same concept, the present invention also provides a steam turbine efficiency measurement system, including an acquisition module, a steady-state correction module, a dynamic correction module and a fusion module.

[0137] The acquisition module is used to collect multiple sets of measurement data of different measurement parameters of the steam turbine under different steady-state and dynamic operating conditions.

[0138] The steady-state correction module is used to correct multiple sets of measurement data under different steady-state operating conditions to obtain the corresponding measurement data correction values; the measurement data correction values ​​are input into the mapping formula between turbine efficiency and steady-state measurement data to obtain the turbine efficiency under different steady-state operating conditions; the uncertainty corresponding to the turbine efficiency under different steady-state operating conditions is obtained through the covariance matrix between the measurement data correction values.

[0139] The dynamic correction module is used to input multiple sets of measurement data under dynamic operating conditions and the standard turbine efficiency as system state inputs to the extended Kalman filter to obtain the corrected turbine efficiency and corresponding uncertainty under dynamic operating conditions. The standard turbine efficiency is the design value under standard operating conditions, and the operating range of the dynamic operating conditions is the same as the total operating range of multiple steady-state operating conditions.

[0140] The fusion module is used to fuse the turbine efficiency and corresponding uncertainty under different steady-state operating conditions with the corrected turbine efficiency and corresponding uncertainty under the corresponding dynamic operating conditions to obtain the best estimate of the turbine efficiency.

[0141] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0142] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for measuring the efficiency of a steam turbine, characterized in that, Includes the following steps: Multiple sets of measurement data for different parameters of the steam turbine were collected under different steady-state and dynamic operating conditions. Multiple sets of measurement data under different steady-state operating conditions are calibrated to obtain corresponding measurement data correction values; the measurement data correction values ​​are input into the mapping formula between turbine efficiency and steady-state measurement data to obtain the turbine efficiency under different steady-state operating conditions; the uncertainty corresponding to the turbine efficiency under different steady-state operating conditions is obtained through the covariance matrix between the measurement data correction values. Multiple sets of measurement data under dynamic operating conditions and the standard turbine efficiency are used as system state inputs to the extended Kalman filter to obtain the corrected turbine efficiency and corresponding uncertainty under dynamic operating conditions. The standard turbine efficiency is the design value under standard operating conditions, and the operating range of the dynamic operating conditions is the same as the total operating range of multiple steady-state operating conditions. By fusing the turbine efficiency and corresponding uncertainty under different steady-state operating conditions with the corrected turbine efficiency and corresponding uncertainty under the corresponding dynamic operating conditions, the best estimate of the turbine efficiency is obtained.

2. The method for measuring turbine efficiency as described in claim 1, characterized in that, The process involves collecting multiple sets of measurement data for different parameters of the steam turbine under different steady-state and dynamic operating conditions, respectively, to construct a steady-state measurement dataset and a dynamic measurement dataset, including: Sensors are installed at different locations on the steam turbine, including temperature sensors, pressure sensors, flow sensors, and power sensors; the different measured parameters include temperature, pressure, flow rate, and power. The turbine operating conditions were changed multiple times until the turbine reached a steady-state condition. The steady-state measurement data of multiple sensors under each steady-state condition were read and used as a steady-state measurement dataset. By changing the operating conditions of the steam turbine, real-time measurement data from multiple sensors under dynamic conditions are read and used as a dynamic measurement dataset.

3. The method for measuring turbine efficiency as described in claim 1, characterized in that, The step of inputting the measurement data correction value into the mapping formula between turbine efficiency and steady-state measurement data includes the following steps: Based on the work done by water vapor inside the steam turbine, the thermodynamic relationships of the steam turbine are obtained: h0-h1=η1(h0-h 1s ); In the formula, h0 is the inlet enthalpy of the steam turbine, h1 is the exhaust enthalpy of the steam turbine, η1 is the steam turbine efficiency under different steady-state operating conditions, and h 1s This represents the ideal exhaust enthalpy value for the steam turbine. The steam enthalpy value of the steam turbine is obtained by querying the steam property database: H = p(x); In the formula, H is the enthalpy vector, p is the water vapor property function, and x is the set of measurement data; A mapping formula between turbine efficiency and measured data is established based on the thermodynamic relationship of the steam turbine and the steam enthalpy of the steam turbine: η1 = y(x); In the formula, y represents the mapping relationship.

4. The method for measuring turbine efficiency as described in claim 1, characterized in that, The calibration of multiple sets of measurement data under different steady-state operating conditions includes the following steps: Add the uncertainty of the corresponding measuring instrument to the measurement data under any steady-state condition in the steady-state measurement dataset, and obtain the covariance matrix between the measurement data under any steady-state condition through the uncertainty of the measuring instrument; A mathematical optimization problem is constructed by using the covariance matrix between the measurement data. The mathematical optimization problem is solved to obtain the correction values ​​of multiple sets of measurement data under different steady-state conditions and the unknown parameters of the steam turbine system. The mathematical optimization problem is as follows: In the formula, x * Here, ξ(x) represents the correction value for the measured data, x represents the measured data, and ξ(x) represents the correction value for the measured data. * To optimize the objective function, S x Let f be the covariance matrix between the measured data, f be the steady-state thermal balance equation of the system, u be the unknown parameter, and T be the transpose.

5. The method for measuring turbine efficiency as described in claim 4, characterized in that, The process of obtaining the uncertainty of the turbine efficiency by means of the covariance matrix between the corrected values ​​of the measured data specifically includes the following steps: The covariance matrix between the corrected values ​​of the measurement data is obtained as follows: In the formula, Let x be the covariance matrix between the corrected values ​​of the measured data. * S is the correction value for the measurement data. x Let x be the covariance matrix between the measurement data, where x represents the measurement data and T is the transpose. Obtain the covariance matrix of the unknown parameter based on the covariance matrix between the correction values ​​of the measurement data: In the formula, S u S is the covariance matrix of the unknown parameter u. u It includes the uncertainty of turbine efficiency.

6. The method for measuring turbine efficiency as described in claim 1, characterized in that, The process of inputting multiple sets of measurement data under dynamic operating conditions and the standard turbine efficiency as system state inputs to an extended Kalman filter to obtain the corrected turbine efficiency and corresponding uncertainty under dynamic operating conditions includes the following steps: A variable operating condition model is constructed based on multiple sets of measurement data under dynamic operating conditions. The system state parameters of the variable operating condition model include multiple measurement parameters and turbine efficiency. Taking any set of measurement data under dynamic operating conditions and the standard turbine efficiency as the initial system state, denoted as d0, and the initial system state parameter estimation covariance matrix as P0, the prior state estimation of the system state at the next time step is performed successively based on the system state at the previous time step: In the formula, and Let g be the prior estimates of the system state parameters of the steam turbine system at time m and its covariance matrix, respectively; g is the system dynamic characteristic model; and d is the system state parameter estimate. m-1 Let P be the system state at time m-1. m-1 Let u be the system state covariance matrix at time m-1. m-1 For the other input parameters of the system at time m-1, Let be the Jacobian matrix of the system dynamic characteristics with respect to the system state parameters, and Q be the covariance matrix of the system dynamic characteristic model. The system measurement model is constructed as follows: z m =h(d m ); Among them, z m Let m be the system's measured value at time m, and h be the system's measurement model function; Calculate the Kalman gain matrix K of the system at time m. m for: in, R is the Jacobian matrix of the system dynamic characteristics with respect to the system state parameters, and R is the covariance matrix of the system measurement model. Calculate the posterior estimates of the system state parameters and the posterior estimate of the covariance matrix using the following formula: In the formula, I is the identity matrix, and d m Let P be the posterior estimate of the system state parameters at time m. m The covariance matrix of the system state parameters is the posterior estimate. Through iterative calculation, the posterior estimates of the system state parameters and the posterior estimates of the system state parameter covariance matrix at each time point are obtained. The turbine efficiency η for any operating condition under dynamic conditions is obtained by extracting the posterior estimate of the system state parameters. d,i The variance corresponding to the turbine efficiency is extracted from the posterior estimate of the system state parameter covariance matrix.

7. The method for measuring turbine efficiency as described in claim 1, characterized in that, The process of fusing the turbine efficiency and corresponding uncertainty under different steady-state operating conditions with the corrected turbine efficiency and corresponding uncertainty under the corresponding dynamic operating conditions includes the following steps: For the i-th dynamic operating condition, the following estimation equation is established: Z=η s,i +K(n d,i -or s,i ); in, In the formula, Z is the best estimate of the turbine efficiency, K is the fusion coefficient, and η s,i Let be the turbine efficiency under the i-th steady-state condition. Let η be the uncertainty of the turbine efficiency under the i-th steady-state condition. d,i The corrected turbine efficiency is defined for the i-th dynamic operating condition. Let be the uncertainty of the corrected turbine efficiency under the i-th dynamic operating condition, s be the steady-state operating condition, and d be the dynamic operating condition.

8. A steam turbine efficiency measurement system, characterized in that, include: The data acquisition module is used to collect multiple sets of measurement data of different measurement parameters of the steam turbine under different steady-state and dynamic operating conditions. The steady-state correction module is used to correct multiple sets of measurement data under different steady-state operating conditions to obtain corresponding measurement data correction values; the measurement data correction values ​​are input into the mapping formula between turbine efficiency and steady-state measurement data to obtain the turbine efficiency under different steady-state operating conditions; the uncertainty corresponding to the turbine efficiency under different steady-state operating conditions is obtained through the covariance matrix between the measurement data correction values. The dynamic correction module is used to input multiple sets of measurement data under dynamic operating conditions and the standard turbine efficiency as system state inputs to the extended Kalman filter to obtain the corrected turbine efficiency and corresponding uncertainty under dynamic operating conditions. The standard turbine efficiency is the design value under standard operating conditions, and the operating range of the dynamic operating conditions is the same as the total operating range of multiple steady-state operating conditions. The fusion module is used to fuse the turbine efficiency and corresponding uncertainty under different steady-state operating conditions with the corrected turbine efficiency and corresponding uncertainty under the corresponding dynamic operating conditions to obtain the best estimate of the turbine efficiency.

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