Method for evaluating influence of steam turbine through-flow efficiency on unit output

Abstract

The invention discloses a method for evaluating the influence of steam turbine through-flow efficiency on unit output, and particularly relates to the field of evaluation management, and the method comprises the steps: S1, synchronously obtaining two-dimensional parameters with consistent timestamps; s2, preprocessing the two-dimensional parameters, evaluating data validity based on the preprocessed data, and processing invalid data; s3, on the basis of the preprocessed effective data, evaluating through-flow efficiency core characteristic factors; s4, evaluating a unit output correlation coupling factor based on the through-flow efficiency core characteristic factor; s8, generating evaluation suggestions of the influence of the steam turbine through-flow efficiency on the output of the unit to be referred to by dispatchers; according to the method, the energy efficiency optimization decision suggestion with clear priority is generated, the evaluation result is directly converted into an operable management decision basis, the problem that an existing evaluation method is disjointed with an actual decision is solved, unit energy efficiency improvement and operation cost optimization are effectively supported, and efficient utilization of energy resources is achieved.

Description

Technical Field

[0001] This invention relates to the field of evaluation and management technology, and more specifically, to a method for evaluating the impact of turbine flow efficiency on unit output. Background Technology

[0002] In the field of steam turbine operation optimization and energy efficiency management, flow efficiency, as a core performance indicator, is a key factor in assessing its impact on unit output, and is a crucial step in energy system optimization decisions. Existing technologies mostly use computational fluid dynamics (CFD) simulations, field tests, and traditional statistical analysis methods to construct correlation analyses between flow efficiency and unit output, providing basic data support for obtaining unit operating parameters and making energy efficiency improvement decisions.

[0003] While existing technologies can initially achieve quantitative analysis of the correlation between flow efficiency and unit output, providing a reference for basic energy efficiency optimization under stable operating conditions, they still have significant shortcomings in supporting precise decision-making. On the one hand, parameter selection is often limited to a single dimension, lacking multi-dimensional collaborative consideration of the correlation between flow-side characteristics and unit output parameters, resulting in incomplete input dimensions for evaluation. On the other hand, evaluation methods often employ linear fitting or simple nonlinear regression, ignoring the coupling effect between data under dynamic operating conditions, making it difficult to reflect the complex inherent laws of the impact of flow efficiency on unit output, resulting in low accuracy of evaluation results and failing to provide effective information for relevant dispatchers. Furthermore, existing methods lack real-time data verification and dynamic evaluation mechanisms, making it impossible to quickly respond to changes in operating conditions and support the required real-time, precise decision-making optimization, leading to blindness in unit operation decisions and insufficient exploration of energy efficiency optimization potential. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an evaluation method for the impact of turbine flow efficiency on unit output. By systematically collecting key parameters related to the flow path and unit output, a multi-dimensional characteristic factor and dynamic coupling evaluation method are constructed to achieve accurate quantitative evaluation of the impact of flow efficiency on unit output. This provides scientific and real-time technical support for unit energy efficiency optimization decisions, thereby solving the problems of low accuracy, poor dynamic adaptability, and insufficient decision support capabilities of existing evaluation methods.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the impact of turbine flow efficiency on unit output, comprising: S1. Synchronously obtain two-dimensional parameters with consistent timestamps; S2. Preprocess the two-dimensional parameters, evaluate the data validity based on the preprocessed data, and correct and process invalid data. S3. Based on the preprocessed effective data, evaluate the core characteristic factors of flow efficiency; S4. Evaluate the unit output correlation coupling factor based on the core characteristic factor of flow efficiency; S5. Through big data regression analysis, dynamic evaluation is performed using historical data; S6. Obtain the flow efficiency impact coefficient and obtain decision data based on the flow efficiency impact coefficient; S7. Obtain the deviation rate based on the unit output value, and obtain the dynamic evaluation correction coefficient based on the deviation rate; S8. Provide assessment suggestions on the impact of turbine flow efficiency on unit output for dispatchers' reference.

[0006] Preferably, the dual-dimensional parameters include flow-side characteristic parameters and output-related parameters; the flow-side characteristic parameters specifically include the static pressure at the blade leading edge stagnation point, the boundary layer thickness on the blade suction surface, the airflow deflection angle at the blade trailing edge, the steam turbulence intensity at the steam passage cross-section, the thickness of the steam condensate film on the steam passage wall, and the steam moisture content distribution within the steam passage; the output-related parameters specifically include the exhaust steam residual velocity kinetic energy density, the exhaust steam condensate subcooling degree, the high-pressure stage extraction steam dryness, the intermediate-pressure stage extraction steam specific volume, the rotor bearing oil film friction torque, and the steam seal friction power loss.

[0007] Preferably, the preprocessing uses the Grubbs criterion to remove outliers, and then uses linear interpolation to supplement missing data; the data validity assessment is used to obtain the validity index of each parameter. ,in This represents the real-time collected value of the i-th parameter. This represents the historical statistical mean of parameter i under various operating conditions. This represents the historical standard deviation of parameter i. The method for handling invalid data is as follows: when the validity index of a parameter is greater than a preset threshold, the parameter data is considered valid; otherwise, the parameter data is re-collected until all parameter validity indices meet the requirements. Simultaneously, valid data is output: the static pressure at the blade leading edge stagnation point is denoted as P, and the boundary layer thickness on the blade suction surface is denoted as... The airflow deflection angle at the blade trailing edge is denoted as... The steam turbulence intensity at the cross-section of the steam duct is denoted as The thickness of the steam condensation film on the steam duct wall is denoted as h, and the distribution of steam moisture content within the steam duct is denoted as h. The residual kinetic energy density of the exhaust steam is denoted as E, and the subcooling degree of the exhaust steam condensate is denoted as... The high-pressure stage extraction steam dryness fraction is denoted as x, the intermediate-pressure stage extraction steam specific volume is denoted as v, the rotor bearing oil film friction torque is denoted as T, and the steam seal friction power loss is denoted as . .

[0008] Preferably, the core characteristic factors of flow efficiency include aerodynamic performance characteristic factors, flow loss characteristic factors, and energy loss characteristic factors; the aerodynamic performance characteristic factors ,in This indicates the static pressure at the leading edge of the blade under standard operating conditions. The thickness of the boundary layer on the suction surface of the blade under standard operating conditions; the flow loss characteristic factor ,in The energy loss characteristic factor represents the thickness of the steam condensation film on the steam duct wall under standard operating conditions. .

[0009] Preferably, the method for evaluating the unit output correlation coupling factor is as follows: aerodynamic performance characteristic factors, flow loss characteristic factors, and energy loss characteristic factors are correlated with the intermediate-pressure stage extraction steam specific volume v, rotor bearing oil film friction torque T, and steam seal friction power loss. The coupling is performed to obtain the unit output correlation coupling factor. ,in This indicates the frictional power loss of the steam seal under standard operating conditions.

[0010] Preferably, the dynamic evaluation uses the unit output as a coupling factor and the input steam quantity. Unit output is the independent variable. With [variable name] as the dependent variable, a multivariate nonlinear functional relationship is constructed, specifically represented as follows: , where a, b and c represent evaluation coefficients, obtained by fitting historical data using the least squares method.

[0011] Preferably, the flow efficiency influence coefficient K is obtained by evaluating the coupling factor C of the unit output under the current operating condition and the coupling factor of the unit output under the standard operating condition. The difference is obtained, specifically expressed as: The decision data includes: assessing the unit output loss under actual operating conditions based on the flow efficiency influence coefficient. Assess the sensitivity of the effect of changes in flow efficiency on unit output based on the flow efficiency influence coefficient. ,in This indicates the flow efficiency.

[0012] Preferably, the method for obtaining the deviation rate is: using the real-time acquired unit output... Compared with the actual operating output monitoring value of the unit By comparing the results, the deviation rate was obtained. The dynamic evaluation correction coefficient After correction, the unit output .

[0013] Preferably, the evaluation recommendations are as follows: based on the quantitatively calculated impact coefficient K, unit output loss, and impact sensitivity, energy efficiency optimization decision recommendations are generated; sorted by sensitivity S, the parameter adjustment priority, operating condition optimization range, and expected energy efficiency improvement are output to provide data support for unit operation management and maintenance plan formulation.

[0014] The technical effects and advantages of this invention are as follows: 1. By constructing a multi-dimensional key parameter system and verifying its validity, this invention ensures the integrity and credibility of the evaluation input data, solves the problems of single parameter selection and difficulty in guaranteeing data quality in existing technologies, lays a data foundation for accurate evaluation, significantly improves the reliability of evaluation results, and enhances the decision-making basis of relevant technical personnel by providing them with more valuable reference information, thereby further improving the accuracy of evaluation results. 2. This invention extracts flow efficiency characteristic factors and constructs output correlation coupling factors, fully considering the coupling effect between various parameters. It breaks through the limitations of traditional linear evaluation models and solves the problems of low accuracy and difficulty in reflecting complex correlation laws in existing evaluation methods. It provides a scientific quantitative basis for the evaluation and management of unit energy efficiency optimization. Through real-time data comparison and evaluation, it solves the problems of poor dynamic adaptability and inability to support real-time decision-making in existing technologies, meets the needs for real-time decision support, improves the pertinence of unit evaluation decisions, and reduces operating costs. 3. This invention generates energy efficiency optimization decision recommendations with clear priorities by quantifying the impact coefficient, output loss, and sensitivity. It directly transforms the evaluation results into actionable management decision-making basis, solving the problem of the disconnect between existing evaluation methods and actual decision-making. It effectively supports the improvement of unit energy efficiency and the optimization of operating costs, and realizes the efficient utilization of energy resources. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0016] 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.

[0017] As attached Figure 1 The method for evaluating the impact of turbine flow efficiency on unit output includes a parameter acquisition terminal, a data preprocessing module, a feature extraction module, a model building module, an evaluation calculation module, a verification and correction module, and a decision output module. Specifically, it includes the following steps: S1. Synchronously obtain two-dimensional parameters with consistent timestamps; Specifically: the dual-dimensional parameters include flow-side characteristic parameters and output-related parameters; the flow-side characteristic parameters specifically include the static pressure at the blade leading edge stagnation point, the thickness of the boundary layer on the blade suction surface, the airflow deflection angle at the blade trailing edge, the steam turbulence intensity at the steam passage cross section, the thickness of the steam condensate film on the steam passage wall, and the steam moisture content distribution within the steam passage; the output-related parameters specifically include the exhaust steam residual velocity kinetic energy density, the exhaust steam condensate subcooling degree, the high-pressure stage extraction steam dryness, the intermediate-pressure stage extraction steam specific volume, the rotor bearing oil film friction torque, and the steam seal friction power loss. It should be noted that the parameter acquisition terminal contains 12 specialized acquisition units: static pressure sensor unit, LDV measurement unit, optical angle sensor unit, hot-wire anemometer unit, microwave thickness gauge unit, capacitive humidity sensor unit, five-hole probe array unit, PT1000 temperature sensor unit, microwave humidity sensor unit, differential pressure density sensor unit, strain gauge torque sensor unit, and non-contact power sensor unit. According to the preset parameter system, it synchronously acquires real-time data of 12 core parameters, ensuring that the data timestamps are consistent and storing them in a temporary database. After the steam turbine starts and enters the stable operation stage (speed ≥3000r / min, load fluctuation ≤±2%), it continuously acquires data at the preset frequency of each parameter from the first acquisition. The data acquisition units are deployed in the turbine's flow path system and related locations: blade leading edge, suction surface, and trailing edge (corresponding to blade parameters); steam duct cross-section, wall surface, and axial section (corresponding to steam duct parameters); exhaust pipe outlet and condensate pipe (corresponding to exhaust end parameters); high-pressure / medium-pressure / low-pressure extraction steam pipes (corresponding to extraction steam parameters); bearing end shaft, thrust bearing housing, and steam seal (corresponding to rotor loss parameters); each data acquisition unit is installed according to preset requirements. Static pressure sensor unit: A miniature wall static pressure sensor is embedded in a 0.5mm hole drilled at the leading edge of the blade; LDV measurement unit: A measurement point is arranged every 0.01mm along the normal direction of the blade's suction surface; Five-hole probe array unit: 16 probes are evenly arranged at the exhaust pipe outlet section; Set acquisition frequency: 100Hz for blade parameters, 50Hz for steam duct parameters, 20Hz for exhaust and extraction parameters, and 10Hz for rotor loss parameters; Data synchronization: The clocks of all acquisition units are calibrated via a GPS timing module to ensure that the 12 sets of parameters at the same timestamp are stored synchronously; Data transmission: Real-time data is transmitted to a temporary database on the local edge computing node using industrial Ethernet, with a transmission delay ≤10ms. S2. Preprocess the two-dimensional parameters, evaluate the data validity based on the preprocessed data, and correct and process invalid data. Specifically: the preprocessing uses the Grubbs criterion to remove outliers, and then uses linear interpolation to supplement missing data; the data validity evaluation uses validity indices for each parameter. ,in This represents the real-time collected value of the i-th parameter. This represents the historical statistical mean of parameter i under various operating conditions. This represents the historical standard deviation of parameter i. The method for handling invalid data is as follows: when the validity index of a parameter is greater than a preset threshold, the parameter data is considered valid; otherwise, the parameter data is re-collected until all parameter validity indices meet the requirements. Simultaneously, valid data is output: the static pressure at the blade leading edge stagnation point is denoted as P, and the boundary layer thickness on the blade suction surface is denoted as... The airflow deflection angle at the blade trailing edge is denoted as... The steam turbulence intensity at the cross-section of the steam duct is denoted as The thickness of the steam condensation film on the steam duct wall is denoted as h, and the distribution of steam moisture content within the steam duct is denoted as h. The residual kinetic energy density of the exhaust steam is denoted as E, and the subcooling degree of the exhaust steam condensate is denoted as... The high-pressure stage extraction steam dryness fraction is denoted as x, the intermediate-pressure stage extraction steam specific volume is denoted as v, the rotor bearing oil film friction torque is denoted as T, and the steam seal friction power loss is denoted as . It should be noted that the data preprocessing module, including an outlier handling unit, a missing value imputation unit, and a validity verification unit, cleans the collected raw parameter data (removes outliers and imputes missing values), verifies data validity, and outputs a valid parameter dataset. After the parameter acquisition terminal completes one round of complete data acquisition (triggered at the minimum acquisition frequency of 10Hz, i.e., triggered once every 100ms), the local edge computing node's data preprocessing server, a computing node built on a Linux system, supports parallel data processing. Outlier handling: - The outlier handling unit calls the Grubbs criterion to calculate the mean and standard deviation for each set of parameter data; sets the significance level and calculates the Grubbs critical value; if the data points... Outliers are identified and removed; Missing value imputation: The missing value imputation unit checks the dataset after outlier removal. If missing data points exist, linear interpolation is used to calculate the interpolation based on three valid data points before and after the missing point; Validity verification: The validity verification unit calls a preset formula; Import historical operating condition statistical mean and standard deviation (calculated from the system's historical database using data of the same type of operating conditions from the past three months); If the data is deemed invalid, a re-sampling instruction is sent to the parameter acquisition terminal, and the corresponding acquisition unit re-acquires the parameter data until all parameters meet the requirements; Output valid dataset: The data that passes the verification is stored in the input database of the feature extraction module in the format of "timestamp-parameter name-parameter value".

[0018] S3. Based on the preprocessed effective data, evaluate the core characteristic factors of flow efficiency; Specifically: the core characteristic factors of flow efficiency include aerodynamic performance characteristic factors, flow loss characteristic factors, and energy loss characteristic factors; the aerodynamic performance characteristic factors ,in This indicates the static pressure at the leading edge of the blade under standard operating conditions. The thickness of the boundary layer on the suction surface of the blade under standard operating conditions; the flow loss characteristic factor ,in The energy loss characteristic factor represents the thickness of the steam condensation film on the steam duct wall under standard operating conditions. It should be noted that the feature extraction module, which includes an aerodynamic performance calculation unit, a flow loss calculation unit, and an energy loss calculation unit, calculates three core flow efficiency feature factors based on the effective parameter dataset and outputs a set of feature factors. The feature extraction server on the edge computing node, triggered immediately after the data preprocessing module outputs the effective parameter dataset (delay ≤ 5ms), uses GPU-accelerated computation and supports fast floating-point data processing. Parameter retrieval: the three calculation units simultaneously extract corresponding parameters from the input database. Specifically, the aerodynamic performance calculation unit extracts static pressure, boundary layer thickness, and deflection angle; the flow loss calculation unit extracts turbulence intensity, condensation film thickness, and moisture content. The energy loss calculation unit extracts residual kinetic energy density, subcooling degree, and extraction steam dryness fraction; standard parameter acquisition: preset values ​​are retrieved from the system standard parameter library (based on turbine design rated operating condition calibration); factor calculation: the aerodynamic performance calculation unit substitutes the formula, first calculates the static pressure ratio, then calculates the exponential term, and finally multiplies it with the cosine value of the deflection angle; the flow loss calculation unit substitutes the formula and calculates in the order of "reciprocal of turbulence intensity → condensation film exponential term → moisture content correction"; the energy loss calculation unit substitutes the formula, first calculates the square root reciprocal of the exhaust steam parameter, and then multiplies it with the logarithmic term of the extraction steam dryness fraction; result storage: the three core characteristic factors of flow efficiency are stored in the coupled modeling database according to the timestamp.

[0019] S4. Evaluate the unit output correlation coupling factor based on the core characteristic factor of flow efficiency; Specifically, the method for evaluating the unit output correlation coupling factor is as follows: aerodynamic performance characteristic factors, flow loss characteristic factors, and energy loss characteristic factors are correlated with the intermediate-pressure stage extraction steam specific volume v, rotor bearing oil film friction torque T, and steam seal friction power loss. The coupling is performed to obtain the unit output correlation coupling factor. ,in This represents the steam seal friction power loss under standard operating conditions. It should be noted that the coupling factor calculation unit of the feature extraction module integrates three flow efficiency feature factors with output-related parameters, calculates the output-related coupling factor C, and outputs coupling factor data. This is triggered immediately after all three flow efficiency feature factors have been calculated and stored in the coupling modeling database. The coupling calculation node of the feature extraction server supports multi-parameter collaborative calculations. Data retrieval: The coupling factor calculation unit extracts the three flow efficiency feature factors from the coupling modeling database and extracts the extraction steam specific volume, friction torque, and steam seal friction power from the effective parameter dataset; Standard parameter retrieval: Extracts from the system's standard parameter library... Coupling calculation: The calculation is performed in the following order: "product of characteristic factors → square root of extraction volume ratio → reciprocal of friction torque → steam seal power index term"; Substitute into the formula, and retain 6 decimal places for each step of the calculation to avoid precision loss; Output result: Associate the coupling factor C with the corresponding timestamp and characteristic factor data, and transmit it to the input interface of the evaluation calculation module.

[0020] S5. Through big data regression analysis, dynamic evaluation is performed using historical data; Specifically: the dynamic assessment uses the unit output as a coupling factor and the input steam quantity. Unit output is the independent variable. With [variable name] as the dependent variable, a multivariate nonlinear functional relationship is constructed, specifically represented as follows: Here, a, b, and c represent evaluation coefficients, obtained by fitting historical data using the least squares method. It should be noted that the model building module, including a historical data retrieval unit, a coefficient fitting unit, and a model storage unit, trains a dynamic evaluation model based on historical operating data, determines the model coefficients a, b, and c, and outputs a callable dynamic evaluation model. This is used during the initial deployment phase (before the turbine is under load). During normal system operation, after accumulating 100 sets of new valid historical data (triggered approximately every 10 seconds), the cloud-based model training server, built on the Python TensorFlow framework, supports batch data fitting and model updates. Specifically, historical data retrieval involves: - The historical data retrieval unit filtering data from the system's historical database that meets certain criteria: including over 500 sets of parameter data (12 core parameters) under different operating conditions (load 20%-100%), flow efficiency data, and actual unit output data; - The filtered data is recalculated using the S2-S4 process to ensure consistent data format; Data labeling uses the actual unit output data as a label. Coupling factor C and input steam quantity As input feature coefficient fitting: The coefficient fitting unit calls the least squares method to construct the objective function; it solves for the optimal solutions of a, b, and c through matrix operations, requiring the goodness of fit to meet the preset requirements. If it does not meet the requirements, it expands the amount of historical data (adds 200 sets) and refits; Model storage: The model storage unit stores the final model formula and coefficients a, b, and c in the model library of the evaluation calculation module, and sets a version number for easy traceability.

[0021] S6. Obtain the flow efficiency impact coefficient and obtain decision data based on the flow efficiency impact coefficient; Specifically: the flow efficiency influence coefficient K is obtained by evaluating the coupling factor C of the unit output under the current operating condition and the coupling factor of the unit output under the standard operating condition. The difference is obtained, specifically expressed as: The decision data includes: assessing the unit output loss under actual operating conditions based on the flow efficiency influence coefficient. Assess the sensitivity of the effect of changes in flow efficiency on unit output based on the flow efficiency influence coefficient. ,in This indicates the flow efficiency. It should be noted that the evaluation calculation module includes a real-time coupling factor calculation unit, an influence coefficient calculation unit, an output loss calculation unit, and a sensitivity analysis unit. Substituting the real-time coupling factor into the model, it calculates the influence coefficient, output loss, and influence sensitivity, outputting quantitative evaluation results. After the dynamic evaluation model is built / updated, it triggers every 100ms (synchronized with the parameter acquisition frequency) to ensure real-time evaluation. The evaluation calculation server on the edge computing node uses a real-time operating system (RTOS) to ensure a calculation latency ≤20ms. Real-time data retrieval includes: the real-time coupling factor calculation unit calculates the coupling factor for the current operating condition according to the S4 process; and it retrieves the coupling factor under the standard operating condition from the system's standard parameter library. Unit output Retrieves input steam quantity from valid parameter dataset Influence coefficient calculation: The influence coefficient calculation unit first substitutes the values ​​into the model to calculate the current operating condition. Substituting these values ​​into formula K yields the percentage-based influence coefficient; Output loss calculation: The output loss calculation unit substitutes the values ​​into the formula, first calculating the exponential term, then multiplying it by the loss ratio and standard output; Influence sensitivity calculation: The sensitivity analysis unit first calculates the flow efficiency; The derivative of the flow efficiency with respect to the influence coefficient K is calculated, and substituted into the formula to obtain the sensitivity value; Result output: K, ... S and timestamp The data is then linked and transmitted to the verification and correction module.

[0022] S7. Obtain the deviation rate based on the unit output value, and obtain the dynamic evaluation correction coefficient based on the deviation rate; Specifically, the method for obtaining the deviation rate is as follows: the real-time acquired unit output... Compared with the actual operating output monitoring value of the unit By comparing the results, the deviation rate was calculated. The dynamic evaluation correction coefficient Used to dynamically correct the evaluation model, resulting in improved unit output. It should be noted that the verification and correction module includes an actual output acquisition unit, a deviation rate calculation unit, and a model correction unit. It compares the model's predicted output with the unit's actual output, calculates the deviation rate, dynamically corrects the model, and outputs the corrected evaluation results. After the evaluation calculation module outputs the quantitative evaluation results, it immediately triggers (delay ≤ 5ms) the verification and correction server on the edge computing node to communicate in real-time with the unit's EMS system (Energy Management System). Specifically, the actual output acquisition unit connects to the unit's EMS system via the Modbus TCP protocol to collect the unit's real-time actual output. Acquisition latency ≤ 10ms; Deviation rate calculation: The deviation rate calculation unit substitutes the values ​​into the formula to calculate the relative error between the model's predicted value and the actual value; Model correction: If the deviation rate meets the preset requirements, the model prediction is determined to be accurate, and the result is directly output. K If the preset requirements are not met, the model correction unit calculates the correction coefficients. The corrected unit output is obtained; at the same time, the correction coefficient is... Feedback is sent to the model building module to update model coefficients a, b, and c, ensuring prediction accuracy in the next round; the corrected result is output: the corrected... K The S signal is transmitted to the decision output module.

[0023] S8. Provide assessment suggestions on the impact of turbine flow efficiency on unit output for dispatchers' reference.

[0024] Specifically: The assessment recommendations are based on the quantitatively calculated impact coefficient K, unit output loss, and impact sensitivity to generate energy efficiency optimization decision recommendations. Sorted by sensitivity S, the recommendations output parameter adjustment priorities, operating condition optimization ranges, and expected energy efficiency improvement rates, providing data support for unit operation management and maintenance plan development. It should be noted that the decision output module includes a priority sorting unit, an optimization range calculation unit, and a decision generation unit. Based on the corrected quantitative assessment results, it generates parameter adjustment priorities, operating condition optimization ranges, and expected energy efficiency improvement rates, outputting actionable decision recommendations. After the verification and correction module outputs the corrected assessment results, it summarizes them every 500ms (to avoid frequent fluctuations in decisions). The cloud-based decision output server connects to the unit operation and maintenance management platform and the DCS system (distributed control system). The assessment results are retrieved from the verification and correction module from the last 5 (within 2.5 seconds) results. The average value of S and its corresponding parameters is used as the basis for decision-making; priority ranking: priority ranking units are sorted according to the absolute value of the sensitivity S (the larger the absolute value of S, the higher the priority), while also considering... ,like For power outputs >100kW, priority is automatically increased by 1 level; Optimization interval calculation: The optimization interval calculation unit calculates the optimization range of each high-priority parameter based on historical best operating condition data and the difference between the current parameter value and the standard parameter value; Decision generation: The decision generation unit generates decision suggestions according to the structure of "priority sorting → optimization interval → expected benefit", which clarifies "which parameter to adjust → to what range → expected reduction in output loss"; The decision suggestions are pushed to the unit operation and maintenance management platform (Web terminal) and DCS system (control terminal) via HTTP / HTTPS protocol, and stored in the decision history database for subsequent traceability and effect evaluation.

[0025] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the impact of turbine flow efficiency on unit output, characterized in that, include: S1. Synchronously obtain two-dimensional parameters with consistent timestamps; S2. Preprocess the two-dimensional parameters, evaluate the data validity based on the preprocessed data, and correct and process invalid data. S3. Based on the preprocessed effective data, evaluate the core characteristic factors of flow efficiency; S4. Evaluate the unit output correlation coupling factor based on the core characteristic factor of flow efficiency; S5. Through big data regression analysis, dynamic evaluation is performed using historical data; S6. Obtain the flow efficiency impact coefficient and obtain decision data based on the flow efficiency impact coefficient; S7. Obtain the deviation rate based on the unit output value, and obtain the dynamic evaluation correction coefficient based on the deviation rate; S8. Provide assessment suggestions on the impact of turbine flow efficiency on unit output for dispatchers' reference.

2. The method for evaluating the impact of turbine flow efficiency on unit output according to claim 1, characterized in that: The dual-dimensional parameters include flow-side characteristic parameters and output-related parameters. The flow-side characteristic parameters specifically include the static pressure at the blade leading edge stagnation point, the boundary layer thickness on the blade suction surface, the airflow deflection angle at the blade trailing edge, the steam turbulence intensity at the steam passage cross-section, the thickness of the steam condensate film on the steam passage wall, and the steam moisture content distribution within the steam passage. The output-related parameters specifically include the exhaust steam residual velocity kinetic energy density, the exhaust steam condensate subcooling degree, the high-pressure stage extraction steam dryness, the intermediate-pressure stage extraction steam specific volume, the rotor bearing oil film friction torque, and the steam seal friction power loss.

3. The method for evaluating the impact of turbine flow efficiency on unit output according to claim 1, characterized in that: The preprocessing uses the Grubbs criterion to remove outliers, and then uses linear interpolation to supplement missing data; the evaluation of data validity is used to obtain the validity index of each parameter; The method for handling invalid data is as follows: when the validity index of a parameter is greater than a preset threshold for the validity index of that parameter, the parameter data is determined to be valid; otherwise, the parameter data is re-collected until the validity index of all parameters meets the requirements. Simultaneously, valid data is output: the static pressure at the leading edge of the blade is denoted as P, and the boundary layer thickness on the suction surface of the blade is denoted as... The airflow deflection angle at the blade trailing edge is denoted as... The steam turbulence intensity at the cross-section of the steam duct is denoted as The thickness of the steam condensation film on the steam duct wall is denoted as h, and the distribution of steam moisture content within the steam duct is denoted as h. The residual kinetic energy density of the exhaust steam is denoted as E, and the subcooling degree of the exhaust steam condensate is denoted as... The high-pressure stage extraction steam dryness fraction is denoted as x, the intermediate-pressure stage extraction steam specific volume is denoted as v, the rotor bearing oil film friction torque is denoted as T, and the steam seal friction power loss is denoted as . .

4. The method for evaluating the impact of turbine flow efficiency on unit output according to claim 3, characterized in that: The core characteristic factors of the flow efficiency include aerodynamic performance characteristic factors, flow loss characteristic factors, and energy loss characteristic factors; the aerodynamic performance characteristic factors ,in This indicates the static pressure at the leading edge of the blade under standard operating conditions. The thickness of the boundary layer on the suction surface of the blade under standard operating conditions; the flow loss characteristic factor ,in The energy loss characteristic factor represents the thickness of the steam condensation film on the steam duct wall under standard operating conditions. .

5. The method for evaluating the impact of turbine flow efficiency on unit output according to claim 4, characterized in that: The specific method for evaluating the unit output correlation coupling factor is as follows: aerodynamic performance characteristic factors, flow loss characteristic factors, and energy loss characteristic factors are correlated with the intermediate-pressure stage extraction steam specific volume v, rotor bearing oil film friction torque T, and steam seal friction power loss. The coupling is performed to obtain the unit output correlation coupling factor. .

6. The method for evaluating the impact of turbine flow efficiency on unit output according to claim 5, characterized in that: The dynamic assessment uses the unit output correlation coupling factor and input steam quantity. Unit output is the independent variable. With [variable name] as the dependent variable, a multivariate nonlinear functional relationship is constructed, specifically represented as follows: , where a, b and c represent evaluation coefficients, obtained by fitting historical data using the least squares method.

7. The method for evaluating the impact of turbine flow efficiency on unit output according to claim 6, characterized in that: The flow efficiency influence coefficient is determined by evaluating the coupling factor C of the unit output under the current operating condition and the coupling factor of the unit output under the standard operating condition. The difference is obtained; the decision data includes: assessing the unit output loss under actual operating conditions based on the flow efficiency influence coefficient; and assessing the sensitivity of the flow efficiency change to the unit output based on the flow efficiency influence coefficient.

8. The method for evaluating the impact of turbine flow efficiency on unit output according to claim 7, characterized in that: The method for obtaining the deviation rate is as follows: the real-time acquired unit output... Compared with the actual operating output monitoring value of the unit By comparing the results, the deviation rate was obtained. The dynamic evaluation correction coefficient After correction, the unit output .

9. The method for evaluating the impact of turbine flow efficiency on unit output according to claim 1, characterized in that: The assessment recommendations are as follows: Based on the quantitatively calculated flow efficiency impact coefficient, unit output loss, and sensitivity to the impact on unit output, energy efficiency optimization decision recommendations are generated. The system prioritizes output parameter adjustments, optimizes operating conditions, and assesses expected energy efficiency improvements based on their impact sensitivity, providing data support for unit operation management and maintenance planning.

Images