Turbine blade temperature online calculation method and system

Abstract

The application relates to a steam turbine blade temperature online calculation method, which comprises the following steps: collecting operation parameters of a steam turbine and temperature data of blade fixed measuring points, determining blade heating power according to the operation parameters, obtaining an a priori estimation value of the blade temperature based on the blade heating power and a state transition matrix, obtaining a predicted covariance matrix according to the state transition matrix and a covariance matrix at the last moment, calculating a fusion weight based on the predicted covariance matrix, a measurement noise covariance and an observation matrix, correcting the a priori estimation value of the blade temperature according to the fusion weight, the observation matrix and the temperature data of the fixed measuring points, and obtaining a posteriori estimation value of the blade temperature. Through the application, the problem of inaccurate estimation of the steam turbine blade temperature is solved, the change of the heating power is predicted through multidimensional variables, dual driving of physical prediction and data feedback is realized, and it is ensured that the estimation value of the blade temperature still has extremely high physical confidence when the steam parameters fluctuate sharply.

Description

Technical Field

[0001] This application relates to the field of industrial monitoring technology, and in particular to a method and system for online calculation of turbine blade temperature. Background Technology

[0002] The temperature rises sharply in the last stage blades of the low-pressure cylinder of a steam turbine under cylinder cut-off conditions. Real-time and accurate monitoring of their temperature is crucial to ensuring the safe operation of the unit.

[0003] Current technologies primarily rely on fixed temperature sensors mounted on the exhaust cylinder or cylinder block for indirect monitoring. However, due to the inability to mount the sensors on high-speed rotating blades and the spatial distance and complex steam medium between them and the heat source, severe thermal inertia lag occurs, causing the monitoring response speed to lag far behind the actual temperature rise rate of the blades, resulting in an extremely short warning window. Secondly, during cylinder switching operation, the water spray desuperheating system activated to reduce exhaust temperature generates a large amount of water mist. This water mist contacting the sensor causes large, high-frequency random fluctuations in the measurement signal, making it difficult for traditional filtering methods to distinguish noise from the actual temperature rise trend, easily leading to false alarms or missed alarms. Furthermore, existing technologies mostly use simple deviation compensation or static empirical formulas for correction, lacking the ability to dynamically model complex thermodynamic processes under unsteady conditions, and thus failing to achieve high-precision real-time tracking of the temperature of "invisible" rotating components. Summary of the Invention This application provides a method, system, electronic device, and storage medium for online calculation of turbine blade temperature, in order to at least solve the problem of inaccurate turbine blade temperature estimation in related technologies.

[0004] In a first aspect, embodiments of this application provide a method for online calculation of turbine blade temperature, the method comprising: Collect the operating parameters of the steam turbine and the temperature data of the fixed measuring points on the blades, and determine the heat output of the blades based on the operating parameters; Based on the blade heating power and the state transition matrix, a priori estimate of the blade temperature is obtained. The state transition matrix is ​​a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density. Based on the state transition matrix and the covariance matrix of the previous time step, the prediction covariance matrix is ​​obtained. Based on the predicted covariance matrix, the measurement noise covariance, and the observation matrix, the fusion weight is calculated. The observation matrix is ​​used to characterize the spatial heat conduction mapping relationship between the blade temperature and the temperature data of the fixed measuring point. Based on the fusion weights, the observation matrix, and the temperature data from the fixed measurement points, the prior estimate of the blade temperature is corrected to obtain the posterior estimate of the blade temperature.

[0005] In some embodiments, the operating parameters include the low-pressure cylinder inlet steam flow rate and the steam density in the exhaust cylinder; the prior estimate of the blade temperature based on the blade heating power and the state transition matrix includes: The convective heat transfer coefficient is determined based on the steam inlet flow rate of the low-pressure cylinder and the steam density in the exhaust cylinder. Based on the convective heat transfer coefficient, effective heat transfer area, blade mass, and blade specific heat capacity, a state transition matrix is ​​generated; Based on the blade heating power, the blade temperature estimate of the previous moment, and the state transition matrix, a time update prediction is performed to obtain the prior estimate of the blade temperature.

[0006] In some embodiments, the operating parameters include exhaust pressure and blade rotation speed; determining the blade heating power based on the operating parameters includes: Based on the blower heat generation formula, combined with the exhaust pressure and the blade rotation speed, the blade heating power is obtained; The formula for heat generation by blower air includes:

[0007] Among them, P w denoted as blade heat output, k as blower coefficient, p as exhaust pressure, and n as blade rotational speed.

[0008] In some embodiments, the operating parameters include the opening degree of the desuperheating water valve; the calculation of the fusion weights based on the predicted covariance matrix, the measurement noise covariance, and the observation matrix includes: When the opening degree of the desuperheating water valve is zero, the measurement noise covariance is set as the benchmark measurement noise covariance; When the opening degree of the desuperheating water valve is greater than zero, the reference measurement noise covariance is amplified and adjusted according to the opening degree of the desuperheating water valve to obtain the measurement noise covariance. Based on the Kalman gain calculation model, the Kalman gain is calculated as the fusion weight according to the predicted covariance matrix, the observation matrix, and the measurement noise covariance.

[0009] In some embodiments, the Kalman gain calculation model includes:

[0010] Among them, K k To integrate weights, P k|k-1 To predict the covariance matrix, H is the observation matrix, and R is the measurement noise covariance.

[0011] In some embodiments, the process of constructing the observation matrix includes: Based on the characteristic length of the heat conduction path, the thermal conductivity of the material, the effective heat transfer area, and the convective heat transfer coefficient, the spatial heat conduction attenuation factor is calculated. The observation matrix is ​​constructed based on the space thermal conduction attenuation factor.

[0012] In some embodiments, the posterior estimate of the blade temperature is calculated using the following formula:

[0013] in, This is a posterior estimate of the leaf temperature. K is a priori estimate of the blade temperature. k Here, H represents the fusion weights, and z represents the observation matrix. k Temperature data for fixed measuring points.

[0014] Secondly, embodiments of this application provide an online turbine blade temperature estimation system, the system comprising: The acquisition module is used to acquire the operating parameters of the steam turbine and the temperature data of the fixed measuring points on the blades, and to determine the heat generation power of the blades based on the operating parameters. The prior estimation module is used to obtain a prior estimate of the blade temperature based on the blade heating power and the state transition matrix. The state transition matrix is ​​a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density. The prediction covariance matrix is ​​obtained based on the state transition matrix and the covariance matrix of the previous time step. The weight calculation module is used to calculate the fusion weight based on the prediction covariance matrix, the measurement noise covariance and the observation matrix. The observation matrix is ​​used to characterize the spatial heat conduction mapping relationship between the blade temperature and the temperature data of the fixed measuring point. The posterior estimation module is used to correct the prior estimate of the blade temperature based on the fusion weights, the observation matrix, and the temperature data of the fixed measurement points, so as to obtain the posterior estimate of the blade temperature.

[0015] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the online turbine blade temperature estimation method as described in the first aspect above.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the online turbine blade temperature estimation method as described in the first aspect above.

[0017] Compared to related technologies, the online turbine blade temperature estimation method provided in this application uses a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density as the state transition matrix. By dynamically correcting the state transition moment through real-time sensing of changes in the internal flow field of the low-pressure cylinder, the algorithm can simulate the complex convective heat transfer process under cylinder switching conditions in real time, solving the problem of divergence or accuracy loss of traditional algorithms under extreme unsteady conditions. It accurately characterizes the complex influence of parameters such as steam flow rate and steam density on blade heat transfer, and compared to traditional linear models, it more closely reflects the nonlinear heat transfer laws under actual operating conditions.

[0018] A multi-source data-driven feedforward calibration mechanism was introduced, which predicts changes in heating power in real time through multi-dimensional variables, realizing dual driving force of physical prediction and data feedback, and ensuring that the estimated value of blade temperature still has a very high physical confidence when steam parameters fluctuate drastically.

[0019] The prediction-correction architecture using Kalman filtering achieves dynamic mapping of space, compensates for the space heat transfer loss from the rotating heat source to the static measuring point using the observation matrix, restores the far-end observed temperature to the near-end real temperature in real time, eliminates space heat conduction lag, and achieves advanced early warning at the intrinsic safety level. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the online calculation method for turbine blade temperature according to an embodiment of this application; Figure 2 This is a structural block diagram of the online turbine blade temperature calculation system according to an embodiment of this application; Figure 3 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated 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 scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0022] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0023] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0024] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0025] This embodiment provides a method for online calculation of turbine blade temperature. Figure 1 This is a flowchart of the online calculation method for turbine blade temperature according to an embodiment of this application, as shown below. Figure 1As shown, the process includes the following steps: Step S101: Collect the operating parameters of the steam turbine and the temperature data of the fixed measuring points on the blades, and determine the heat generation power of the blades based on the operating parameters.

[0026] The unit's operating parameters are collected in real time through a distributed control system (DCS), including but not limited to unit load P, exhaust pressure p, blade speed n, low-pressure cylinder inlet steam flow G, and exhaust temperature T. e and the opening degree S of the desuperheating water valve w Synchronously acquire the measured temperature signal z at a fixed measuring point. k Optionally, the sampling frequency is set to 1Hz, and data alignment and storage are completed through the DCS interface to avoid timestamp discrepancies between multiple data sources, ensuring the timing consistency of subsequent heat generation power calculation and state transition matrix iteration. The DCS is the core control system for the operation of the steam turbine unit. It directly obtains data from the source without the need to add a large number of additional sensors, reducing hardware modification costs. Moreover, the data is completely synchronized with the actual operating status of the unit, avoiding delays and data deviations caused by external acquisition devices.

[0027] In some embodiments, the operating parameters include exhaust pressure and blade rotation speed; step S101, determining the blade heating power based on the operating parameters, includes: Based on the blast heat generation formula, combined with exhaust pressure and blade rotation speed, the blade heating power is obtained; The formula for heat generation by blower includes:

[0028] Among them, P w denoted as blade heat output, k as blower coefficient, p as exhaust pressure, and n as blade rotational speed.

[0029] The power coefficient k is dynamically calibrated using feedforward variables such as load and coal feed rate collected from the DCS. The system uses key upstream feedforward variables collected from the DCS, such as unit load, total coal feed rate, and total boiler air volume, to determine the current overall energy input and steam generation status of the unit. If these feedforward variables indicate that the unit's operating status has significantly deviated from the design conditions when k was initially calibrated (e.g., under extreme variable conditions such as cylinder cut-off), the algorithm will fine-tune the k value in real time according to a preset mapping relationship or model to ensure the blast heat generation power P. w The calculations can respond to the overall operating status of the unit, not just local pressure and speed, thus providing predictions that are closer to the actual physical process even under varying operating conditions.

[0030] Step S102: Based on the blade heating power and the state transition matrix, the prior estimate of the blade temperature is obtained. The state transition matrix is ​​a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density. Based on the state transition matrix and the covariance matrix of the previous time step, the prediction covariance matrix is ​​obtained.

[0031] In some embodiments, the operating parameters include the low-pressure cylinder inlet steam flow rate and the steam density in the exhaust cylinder; in step S102, the prior estimate of the blade temperature based on the blade heating power and the state transition matrix includes: Step S1021: Determine the convective heat transfer coefficient based on the steam flow rate in the low-pressure cylinder and the steam density in the exhaust cylinder.

[0032] Step S1022: Generate a state transition matrix based on the convective heat transfer coefficient, effective heat transfer area, blade mass, and blade specific heat capacity.

[0033] Step S1023: Based on the blade heating power, the blade temperature estimate of the previous moment, and the state transition matrix, perform time update prediction to obtain the prior estimate of the blade temperature.

[0034] Traditionally, the state transition matrix is ​​often set to a constant value. In this embodiment, it is defined as the dynamic heat transfer function matrix A:

[0035] Where h(G,ρ) is the convective heat transfer coefficient that fluctuates in real time with flow rate G and density ρ, S is the effective heat transfer area, m is the blade mass, and C ρ Δt represents the specific heat capacity of the blade, and Δt represents the unit time.

[0036] The core convective heat transfer coefficient h(G,ρ) fluctuates in real time with G and ρ, perfectly matching the dynamic changes in the flow field of the steam turbine (especially in cylinder switching conditions). This solves the prediction deviation problem caused by the change in heat transfer law when the traditional fixed matrix switches operating conditions, and realizes the algorithm's real-time simulation capability for actual operating conditions.

[0037] The formula for calculating the prior estimate of blade temperature is as follows:

[0038] in, Let be the prior estimate of the blade temperature, and A be the state transition matrix. The estimated blade temperature P at the previous moment. w,k-1 Γ represents the blade heating power at the previous moment, and Γ represents the control input matrix.

[0039] By utilizing physical models to anticipate temperature rise trends, pre-compensation for heat transfer lag can be achieved. Using the blade's heat generation power as the heat source input, and combining the dynamic state transition matrix with the temperature estimate from the previous moment, time-update predictions are performed. The entire process strictly follows the physical logic of "heat generation - heat transfer - temperature change," enabling the early detection of blade temperature rise / fall trends and pre-compensation for the inherent lag in the turbine blade heat transfer process. Compared to traditional pure data prediction without physical basis, this method can more accurately predict the direction and magnitude of temperature changes.

[0040] Traditional methods lack the ability to predict heat generation. This embodiment focuses on the heat generation power P of the blower. w The coefficient k in the calculation formula undergoes dynamic calibration based on multi-source feedforward data, enabling the calculation of heat source input to move beyond mechanically applying formulas and instead possess feedforward sensing and self-adjustment capabilities. This enhances the initial accuracy of the physical prediction process, makes the starting point of the prior estimate more reliable, reduces the burden on subsequent data correction stages, and fundamentally improves the physical confidence of the entire estimation chain.

[0041] Assess the uncertainty of the current physical model under complex conditions by predicting the covariance matrix:

[0042] Among them, P k|k-1 To predict the covariance matrix, P k-1 Let be the covariance matrix of the previous time step, and Q be the process noise covariance, representing the impact of instantaneous disturbances in the steam flow field on the prediction.

[0043] In the calculation of the predicted covariance matrix, the state transition matrix A fully inherits its dynamic characteristics, and the process noise covariance Q is introduced to characterize the impact of instantaneous disturbances in the steam flow field on the prediction. This makes the update of the covariance matrix highly compatible with the actual thermodynamic conditions and flow field disturbance state of the current steam turbine, solving the problems of traditional covariance assessment being out of touch with operating conditions and inaccurate uncertainty quantification, and ensuring the authenticity and reference value of the assessment results.

[0044] Step S103: Based on the prediction covariance matrix, measurement noise covariance, and observation matrix, calculate the fusion weight. The observation matrix is ​​used to characterize the spatial heat conduction mapping relationship between the blade temperature and the temperature data of the fixed measuring point.

[0045] In some embodiments, the operating parameters include the opening degree of the desuperheating water valve; step S103 specifically includes: Step S1031: When the opening degree of the desuperheating water valve is zero, the measurement noise covariance is set as the benchmark measurement noise covariance.

[0046] Step S1032: When the opening degree of the desuperheating water valve is greater than zero, the reference measurement noise covariance is amplified and adjusted according to the opening degree of the desuperheating water valve to obtain the measurement noise covariance.

[0047] Step S1033: Based on the Kalman gain calculation model, calculate the Kalman gain as the fusion weight according to the prediction covariance matrix, the observation matrix and the measurement noise covariance.

[0048] Traditional noise measurements remain constant after debugging and cannot identify water spray interference. This embodiment employs a logic-driven strategy: when the desuperheating water valve opening degree S... w When the value is greater than 0, real-time correction is performed by automatically reducing the fusion weight K by increasing the measurement noise covariance R. k This allows the algorithm to switch to a trusted and stable physical model under interference, thus avoiding false alarms.

[0049] Specifically, the formula for calculating the measurement noise covariance R is as follows:

[0050] Where R0 is the reference measurement noise covariance, and β is a preset coefficient.

[0051] Traditional measurement noise covariance matrices are typically set all at once during the commissioning phase, failing to identify abnormal fluctuations in the measurement signal caused by external interventions (such as water spray cooling). This embodiment establishes a condition-aware adaptive strategy to monitor the opening degree S of the cooling water valve in real time. w Once the spray interference is detected, the algorithm immediately and dynamically reconstructs the noise matrix R, effectively solving the problem of false alarms caused by sensor reading jumps due to the cooling water spray, and significantly improving the robustness of the system.

[0052] In some embodiments, the Kalman gain calculation model includes:

[0053] Among them, K k To integrate weights, P k|k-1 To predict the covariance matrix, H is the observation matrix, and R is the measurement noise covariance.

[0054] In some embodiments, the process of constructing the observation matrix includes: calculating the spatial heat conduction attenuation factor based on the characteristic length of the heat conduction path, the thermal conductivity of the material, the effective heat transfer area, and the convective heat transfer coefficient; and constructing the observation matrix based on the spatial heat conduction attenuation factor.

[0055] The traditional observation matrix H is usually set to 1. In this embodiment, the spatial heat transfer mapping is established using the following formula to compensate for the conduction attenuation from the heat source of the rotating blade to the remote static measuring point.

[0056]

[0057] Where L is the characteristic length of the heat conduction path, λ is the thermal conductivity of the material, and S eff The effective heat transfer area is given by h, which is the convective heat transfer coefficient.

[0058] This step achieves spatiotemporal decoupling, restoring the hysteresis temperature sensed by the sensor to the instantaneous temperature of the blade core in real time. Addressing the spatiotemporal separation between the core heat source of the rotating turbine blade and the static remote measuring point, the physical design of the observation matrix restores the hysteresis and attenuated temperature sensed by the sensor to the instantaneous true temperature of the blade core in real time. This achieves spatiotemporal decoupling of temperature data and solves the core problem of mismatch between observed data and actual blade temperature caused by spatiotemporal deviations in traditional methods.

[0059] The system simultaneously compensates for two types of errors: spatial conduction attenuation and time perception lag. In the spatial dimension, the energy loss during temperature conduction is quantified and corrected through an attenuation factor. In the temporal dimension, a precise temperature mapping relationship is used to ensure that the lagging measurement data accurately corresponds to the current instantaneous temperature of the blade. This dual error compensation significantly improves the effectiveness of the observation data.

[0060] This embodiment borrows the idea of ​​Smith predictors to handle pure time delays, but innovatively achieves dynamic spatial mapping through a Kalman filter prediction-correction architecture. The observation matrix H is used to compensate for spatial heat transfer losses from the rotating heat source to the static measuring point. The far-end observed temperature is restored to the near-end true temperature in real time, eliminating spatial heat conduction lag (typically shortening the response time by 120-180 seconds), and achieving advanced early warning at the intrinsic safety level.

[0061] Step S104: Based on the fusion weights, observation matrix, and temperature data from fixed measurement points, the prior estimate of the blade temperature is corrected to obtain the posterior estimate of the blade temperature.

[0062] The fusion weights are dynamically calculated by combining the prediction covariance matrix (representing the uncertainty of prior estimates), the measurement noise covariance (representing the noise of the measurement data), and the observation matrix (representing the accuracy of temperature mapping). This avoids the bias of a single data source dominating the results, ensuring that the fusion process closely reflects the actual reliability of the data. The prior estimates capture the temperature rise trend and compensate for heat transfer hysteresis based on the physical model, while the fixed measurement point temperature data provides feedback on the actual operating conditions. The fusion weights precisely balance the contributions of both, preserving the foresight of the physical model while incorporating the authenticity of the measured data. The fusion significantly improves the overall accuracy of temperature estimation.

[0063] In some embodiments, the posterior estimate of the blade temperature is calculated using the following formula:

[0064] in, This is a posterior estimate of the leaf temperature. K is a priori estimate of the blade temperature. k Here, H represents the fusion weights, and z represents the observation matrix. k Temperature data for fixed measuring points.

[0065] While outputting the posterior estimate, the posterior covariance matrix Pk is updated simultaneously. The quantified uncertainty result after this correction is used as the input basis for predicting the covariance matrix at the next time step, forming a complete recursive closed loop of "prior estimation → predicting covariance → fusion weights → posterior estimation → posterior covariance update → prior estimation at the next time step", ensuring that the algorithm can run continuously and stably online iteratively.

[0066] It should be noted that if the data from the previous time step does not exist in this embodiment, it can be replaced with the initially defined data. Table 1 is a symbol explanation table according to an embodiment of this application.

[0067] Table 1

[0068] Through the above steps, the state transition matrix adopts a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density. By dynamically correcting the state transition moment through real-time sensing of changes in the internal flow field of the low-pressure cylinder, the algorithm can simulate the complex convective heat transfer process under cylinder cutting conditions in real time, solving the problem of divergence or accuracy loss of traditional algorithms under extreme unsteady conditions. It accurately characterizes the complex influence of parameters such as steam flow rate and steam density on blade heat transfer, and compared with traditional linear models, it more closely reflects the nonlinear heat transfer law under actual operating conditions.

[0069] A multi-source data-driven feedforward calibration mechanism was introduced, which predicts changes in heating power in real time through multi-dimensional variables, realizing dual driving force of physical prediction and data feedback, and ensuring that the estimated value of blade temperature still has a very high physical confidence when steam parameters fluctuate drastically.

[0070] The prediction-correction architecture using Kalman filtering achieves dynamic mapping of space, compensates for the space heat transfer loss from the rotating heat source to the static measuring point using the observation matrix, restores the far-end observed temperature to the near-end real temperature in real time, eliminates space heat conduction lag, and achieves advanced early warning at the intrinsic safety level.

[0071] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0072] This embodiment also provides an online turbine blade temperature calculation system, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0073] Figure 2 This is a structural block diagram of the online turbine blade temperature calculation system according to an embodiment of this application, as shown below. Figure 2 As shown, the system includes: The acquisition module 21 is used to acquire the operating parameters of the steam turbine and the temperature data of the fixed measuring points on the blades, and to determine the heat generation power of the blades based on the operating parameters. The prior estimation module 22 is used to obtain the prior estimate of the blade temperature based on the blade heating power and the state transition matrix. The state transition matrix is ​​a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density. The prediction covariance matrix is ​​obtained based on the state transition matrix and the covariance matrix of the previous time step. The weight calculation module 23 is used to calculate the fusion weight based on the prediction covariance matrix, the measurement noise covariance and the observation matrix. The observation matrix is ​​used to characterize the spatial heat conduction mapping relationship between the blade temperature and the temperature data of the fixed measuring point. The posterior estimation module 24 is used to correct the prior estimate of the blade temperature based on the fusion weights, the observation matrix, and the temperature data of the fixed measurement points, so as to obtain the posterior estimate of the blade temperature.

[0074] In some embodiments, operating parameters include the low-pressure cylinder inlet steam flow rate and the steam density in the exhaust cylinder; the prior estimation module 22 includes: The coefficient calculation module is used to determine the convective heat transfer coefficient based on the steam flow rate in the low-pressure cylinder and the steam density in the exhaust cylinder.

[0075] The matrix generation module is used to generate a state transition matrix based on the convective heat transfer coefficient, effective heat transfer area, blade mass, and blade specific heat capacity.

[0076] The prior value determination module is used to perform time update prediction based on the blade heating power, the blade temperature estimate of the previous moment, and the state transition matrix to obtain the prior estimate of the blade temperature.

[0077] In some embodiments, the operating parameters include exhaust pressure and blade rotation speed; the acquisition module 21 includes: The heat generation power calculation module is used to obtain the blade heat generation power based on the blower heat generation formula, combined with the exhaust pressure and blade speed.

[0078] The formula for heat generation by blower includes:

[0079] Among them, P w denoted as blade heat output, k as blower coefficient, p as exhaust pressure, and n as blade rotational speed.

[0080] In some embodiments, operating parameters include the opening degree of the desuperheating water valve; the weight calculation module 23 includes: The first covariance determination module is used to set the measurement noise covariance as the benchmark measurement noise covariance when the opening degree of the desuperheating water valve is zero.

[0081] The second covariance determination module is used to amplify and adjust the reference measurement noise covariance based on the opening degree of the desuperheating water valve when the valve opening degree is greater than zero, so as to obtain the measurement noise covariance.

[0082] The weight determination module is used to calculate the Kalman gain as the fusion weight based on the Kalman gain calculation model, according to the prediction covariance matrix, the observation matrix, and the measurement noise covariance.

[0083] In some embodiments, the Kalman gain calculation model includes:

[0084] Among them, K k To integrate weights, P k|k-1 To predict the covariance matrix, H is the observation matrix, and R is the measurement noise covariance.

[0085] In some embodiments, the weight calculation module 23 includes an observation matrix construction module for calculating the spatial heat conduction attenuation factor based on the characteristic length of the heat conduction path, the thermal conductivity of the material, the effective heat transfer area, and the convective heat transfer coefficient; and constructing the observation matrix based on the spatial heat conduction attenuation factor.

[0086] In some embodiments, the posterior estimate of the blade temperature is calculated using the following formula:

[0087] in, This is a posterior estimate of the leaf temperature. K is a priori estimate of the blade temperature. k Here, H represents the fusion weights, and z represents the observation matrix. k Temperature data for fixed measuring points.

[0088] The system employs a time-varying nonlinear heat transfer function matrix based on steam flow rate and steam density. By dynamically correcting the state transition moment through real-time sensing of changes in the internal flow field of the low-pressure cylinder, the algorithm can simulate complex convective heat transfer processes under cylinder-cutting conditions in real time. This solves the problem of divergence or accuracy loss in traditional algorithms under extreme unsteady conditions. It accurately characterizes the complex influence of parameters such as steam flow rate and steam density on blade heat transfer, and compared to traditional linear models, it better reflects the nonlinear heat transfer laws under actual operating conditions.

[0089] A multi-source data-driven feedforward calibration mechanism was introduced, which predicts changes in heating power in real time through multi-dimensional variables, realizing dual driving force of physical prediction and data feedback, and ensuring that the estimated value of blade temperature still has a very high physical confidence when steam parameters fluctuate drastically.

[0090] The prediction-correction architecture using Kalman filtering achieves dynamic mapping of space, compensates for the space heat transfer loss from the rotating heat source to the static measuring point using the observation matrix, restores the far-end observed temperature to the near-end real temperature in real time, eliminates space heat conduction lag, and achieves advanced early warning at the intrinsic safety level.

[0091] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0092] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0093] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0094] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program: S1 collects the operating parameters of the steam turbine and the temperature data of the fixed measuring points on the blades, and determines the heat generation power of the blades based on the operating parameters.

[0095] S2. Based on the blade heating power and the state transition matrix, the prior estimate of the blade temperature is obtained. The state transition matrix is ​​a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density. Based on the state transition matrix and the covariance matrix of the previous time step, the predicted covariance moment is obtained.

[0096] S3 calculates the fusion weights based on the prediction covariance matrix, measurement noise covariance, and observation matrix. The observation matrix is ​​used to characterize the spatial heat conduction mapping relationship between the blade temperature and the temperature data at fixed measurement points.

[0097] S4. Based on the fusion weights, observation matrix, and temperature data from fixed measurement points, the prior estimate of the blade temperature is corrected to obtain the posterior estimate of the blade temperature.

[0098] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0099] In one embodiment, Figure 3 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application, such as... Figure 3 As shown, an electronic device is provided, which can be a server, and its internal structure diagram can be as follows. Figure 3 As shown, this electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for online calculation of turbine blade temperature.

[0100] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0102] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for online calculation of turbine blade temperature, characterized in that, The method includes: Collect the operating parameters of the steam turbine and the temperature data of the fixed measuring points on the blades, and determine the heat output of the blades based on the operating parameters; Based on the blade heating power and the state transition matrix, a priori estimate of the blade temperature is obtained. The state transition matrix is ​​a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density. Based on the state transition matrix and the covariance matrix of the previous time step, the prediction covariance matrix is ​​obtained. Based on the predicted covariance matrix, the measurement noise covariance, and the observation matrix, the fusion weight is calculated. The observation matrix is ​​used to characterize the spatial heat conduction mapping relationship between the blade temperature and the temperature data of the fixed measuring point. Based on the fusion weights, the observation matrix, and the temperature data from the fixed measurement points, the prior estimate of the blade temperature is corrected to obtain the posterior estimate of the blade temperature.

2. The method according to claim 1, characterized in that, The operating parameters include the low-pressure cylinder inlet steam flow rate and the exhaust cylinder steam density; the prior estimate of the blade temperature based on the blade heating power and state transition matrix includes: The convective heat transfer coefficient is determined based on the steam inlet flow rate of the low-pressure cylinder and the steam density in the exhaust cylinder. Based on the convective heat transfer coefficient, effective heat transfer area, blade mass, and blade specific heat capacity, a state transition matrix is ​​generated; Based on the blade heating power, the blade temperature estimate of the previous moment, and the state transition matrix, a time update prediction is performed to obtain the prior estimate of the blade temperature.

3. The method according to claim 1, characterized in that, The operating parameters include exhaust pressure and blade rotation speed; determining the blade heating power based on the operating parameters includes: Based on the blower heat generation formula, combined with the exhaust pressure and the blade rotation speed, the blade heating power is obtained; The formula for heat generation by blower air includes: Among them, P w denoted as blade heat output, k as blower coefficient, p as exhaust pressure, and n as blade rotational speed.

4. The method according to claim 1, characterized in that, The operating parameters include the opening degree of the desuperheating water valve; the calculation of the fusion weights based on the predicted covariance matrix, the measurement noise covariance, and the observation matrix includes: When the opening degree of the desuperheating water valve is zero, the measurement noise covariance is set as the benchmark measurement noise covariance; When the opening degree of the desuperheating water valve is greater than zero, the reference measurement noise covariance is amplified and adjusted according to the opening degree of the desuperheating water valve to obtain the measurement noise covariance. Based on the Kalman gain calculation model, the Kalman gain is calculated as the fusion weight according to the predicted covariance matrix, the observation matrix, and the measurement noise covariance.

5. The method according to claim 4, characterized in that, The Kalman gain calculation model includes: Among them, K k To integrate weights, P k|k-1 To predict the covariance matrix, H is the observation matrix, and R is the measurement noise covariance.

6. The method according to claim 1, characterized in that, The process of constructing the observation matrix includes: Based on the characteristic length of the heat conduction path, the thermal conductivity of the material, the effective heat transfer area, and the convective heat transfer coefficient, the spatial heat conduction attenuation factor is calculated. The observation matrix is ​​constructed based on the space thermal conduction attenuation factor.

7. The method according to claim 1, characterized in that, The formula for calculating the posterior estimate of the blade temperature is as follows: in, This is a posterior estimate of the leaf temperature. K is a priori estimate of the blade temperature. k Here, H represents the fusion weights, and z represents the observation matrix. k Temperature data for fixed measuring points.

8. An online turbine blade temperature estimation system, characterized in that, The system includes: The acquisition module is used to acquire the operating parameters of the steam turbine and the temperature data of the fixed measuring points on the blades, and to determine the heat generation power of the blades based on the operating parameters. The prior estimation module is used to obtain a prior estimate of the blade temperature based on the blade heating power and the state transition matrix. The state transition matrix is ​​a time-varying nonlinear heat transfer function matrix with respect to steam flow rate and steam density. The prediction covariance matrix is ​​obtained based on the state transition matrix and the covariance matrix of the previous time step. The weight calculation module is used to calculate the fusion weight based on the prediction covariance matrix, the measurement noise covariance and the observation matrix. The observation matrix is ​​used to characterize the spatial heat conduction mapping relationship between the blade temperature and the temperature data of the fixed measuring point. The posterior estimation module is used to correct the prior estimate of the blade temperature based on the fusion weights, the observation matrix, and the temperature data of the fixed measurement points, so as to obtain the posterior estimate of the blade temperature.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the online calculation method for turbine blade temperature as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the online calculation method for turbine blade temperature as described in any one of claims 1 to 7.

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