A method for dynamic stress mapping and efficiency prediction of hydraulic turbine runner

Abstract

The present invention discloses a method for mapping the dynamic stress and predicting the efficiency of a turbine runner, which relates to the technical field of monitoring and evaluating the operation of hydraulic machinery. It is used to solve the problem of lack of systematic mapping and tracking of the dynamic stress changes of the turbine runner under different operating conditions. By obtaining the operating parameters and environmental data of the turbine, the turbine operating conditions are classified according to the environmental data, and the corresponding operating parameters are selected based on the classification results to analyze the dynamic stress characteristics of the runner. At the same time, the turbine attribute data is collected to make a preliminary estimate of the operating efficiency. Multiple detection time points are set to collect the water pressure entering the turbine, and the water pressure fluctuation is calculated based on this. The operating resistance index is further calculated in combination with the dynamic stress characteristics of the runner. The real-time temperature of the water pressure is detected, and the temperature data and the resistance index are combined to conduct a secondary evaluation and prediction of the turbine operating efficiency, thereby achieving an accurate prediction of the turbine efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic machinery operation monitoring and evaluation, and more particularly to a method for dynamic stress mapping and efficiency prediction of a turbine runner. Background Art

[0002] As the operating capacity of large hydropower stations continues to increase, the operating status of turbines, key energy conversion devices within these plants, directly impacts the overall power generation efficiency and structural safety of the power station. During long-term operation, turbine runners are affected by factors such as water pressure, flow disturbances, and temperature variations, resulting in complex dynamic stress distribution and operational resistance variations, which in turn affect energy conversion efficiency and equipment life.

[0003] The existing technology has the following deficiencies:

[0004] At present, the existing turbine operation monitoring technology mainly focuses on single-point measurement and static model analysis, and lacks systematic mapping and tracking of the turbine runner dynamic stress changes under different operating conditions. This leads to reduced accuracy in operating status identification, untimely fault prediction, and increased deviation in operating efficiency assessment. Therefore, a method for turbine runner dynamic stress mapping and efficiency prediction is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for dynamic stress mapping and efficiency prediction of a turbine runner, which solves the problems raised in the above-mentioned background technology by applying multi-source operation data fusion analysis, intelligent classification of working conditions, dynamic stress dynamic modeling and multi-parameter efficiency prediction mechanism.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner, comprising the following steps:

[0008] Step S1: obtaining turbine operating parameters and environmental data, and classifying turbine operating conditions according to the environmental data;

[0009] Step S2: selecting different turbine operating parameters according to the operating condition classification results to analyze the dynamic stress characteristics of the turbine runner, collecting turbine attribute data, and preliminarily estimating the turbine operating efficiency based on the turbine attribute data;

[0010] Step S3: setting multiple detection time points to detect the water pressure entering the turbine, and calculating the water pressure fluctuation based on the water pressure at each detection time point, and calculating the turbine operation resistance index based on the dynamic stress characteristics of the turbine runner;

[0011] Step S4: Detecting the real-time water pressure and temperature of the water turbine, and performing a secondary evaluation and prediction of the turbine operation efficiency based on the real-time water pressure and temperature of the water turbine and the operation resistance index.

[0012] In a preferred embodiment, in step S1, the operating status of the turbine and the external environment are collected in real time to obtain turbine operating parameters and environmental data, wherein the turbine operating parameters include the pressure distribution on the runner blade surface and the speed fluctuation value, and the environmental data include the water temperature change and the water flow fluctuation value;

[0013] The turbine operating parameters and environmental data are recorded synchronously according to the sampling time within the preset unit time. The preset unit time is recorded as T, and each sampling time divided within the preset unit time is recorded as , the total number of sampling moments is recorded as N.

[0014] In a preferred embodiment, in step S1, the pressure distribution on the runner blade surface is the instantaneous pressure value on the runner blade surface at each sampling moment, expressed as: , where (x,y) is the coordinate point on the blade surface, is the instantaneous pressure value at each sampling moment;

[0015] The speed fluctuation value is the dynamic offset amplitude of the actual speed of the turbine main shaft relative to the average speed of the main shaft. The average speed of the main shaft is obtained by calculating the average of the actual speed of the turbine main shaft at each sampling moment. The square sum of the deviations between the actual speed of the main shaft and the average speed of the main shaft at each sampling moment is calculated and the square root is taken to obtain the speed fluctuation value.

[0016] The water temperature change is the maximum deviation of the incoming water temperature from the initial temperature to the turbine. The difference between the incoming water temperature and the initial temperature at each sampling moment is calculated, and the maximum absolute value is taken as the water temperature change.

[0017] The water flow fluctuation value is the deviation amplitude of the water flow entering the turbine relative to the average water flow value. The average water flow value is the average water flow value at each sampling moment. The water flow fluctuation value is obtained by taking the square root of the sum of the deviations between the water flow at each sampling moment and the average water flow value.

[0018] In a preferred embodiment, in step S1, the operating environment of the turbine is classified into the following working conditions based on the water flow fluctuation value and the water temperature change:

[0019] If the water flow fluctuation value is less than or equal to the preset fluctuation threshold, and the water temperature change is less than or equal to the preset water temperature threshold, the working condition level is judged to be excellent;

[0020] If the water flow fluctuation value is greater than the preset fluctuation threshold, or the water temperature change is greater than the preset water temperature threshold, the working condition level is judged to be good.

[0021] In a preferred embodiment, in step S2, different operating parameters are screened according to the turbine operating level to analyze the dynamic stress characteristics of the turbine runner:

[0022] When the operating condition is excellent, the pressure distribution on the runner blade surface is selected as the operating parameter; when the operating condition is good, the speed fluctuation value is selected as the operating parameter;

[0023] If the operating condition is excellent, the fluid load on the turbine runner is modeled based on the pressure distribution on the runner blade surface and combined with the mechanical modeling method to analyze the dynamic stress characteristics of the turbine runner.

[0024] If the operating condition is good, the speed fluctuation value is combined with the polynomial regression equation to calculate the dynamic stress characteristics of the turbine runner.

[0025] In a preferred embodiment, when the pressure distribution on the runner blade surface is used as an operating parameter, the mechanical modeling method realizes the physical mapping of the pressure field to the stress field through spatial integral modeling, and calculates the time average noise reduction to analyze the dynamic stress characteristics of the turbine runner:

[0026] The spatial integral modeling formula is: ,in, is the blade force area, is the local dynamic stress sensitivity coefficient, is the overall dynamic stress characteristic at each sampling moment;

[0027] The average value of the overall dynamic stress characteristics at each sampling moment in the preset unit time is taken as the dynamic stress characteristics of the turbine runner. The time average noise reduction formula is: ,in, is the dynamic stress characteristic of the turbine runner;

[0028] When the speed fluctuation value is used as the operating parameter, the dynamic stress characteristics of the turbine runner are analyzed:

[0029] The calculation formula is: ,in, , is the preset model coefficient, is the speed fluctuation value, is the average spindle speed, The dynamic stress characteristics of the turbine runner.

[0030] In a preferred embodiment, in step S2, the turbine attribute data is collected, including the surface roughness of the runner blades. and the guide vane clearance G, and preliminarily estimate the turbine operating efficiency based on the turbine attribute data;

[0031] The blade surface roughness and the guide vane gap size are normalized, and the efficiency loss caused by the blade surface roughness and the guide vane gap size is analyzed by the water flow density and water flow velocity. The calculation formulas are: 、 ,in, is the efficiency loss caused by the guide vane gap size, The efficiency loss caused by the guide vane gap size;

[0032] Taking into account the efficiency loss caused by blade surface roughness and the efficiency loss caused by guide vane gap size, the preliminary operating efficiency of the turbine is calculated: ,in, To preset the ideal efficiency value, For initial operating efficiency.

[0033] In a preferred embodiment, in step S3, the water pressure of the turbine at each water pressure monitoring point at each sampling moment is collected, and the water pressure fluctuation value is the maximum pressure difference of the water pressure of the turbine at each water pressure monitoring point within the preset unit time of the turbine, which is recorded as ;

[0034] The turbine operation resistance index is analyzed by combining the turbine water pressure fluctuation value and the turbine runner dynamic stress characteristics: ,in, 、 is the preset adjustment coefficient, is the turbine operating resistance index.

[0035] In a preferred embodiment, in step S4, the real-time temperature of the water pressure entering the turbine at each sampling moment is collected and recorded as ;

[0036] The dynamic correction factor is calculated by integrating the real-time water pressure temperature and the operating resistance index, and the final efficiency is obtained by performing a secondary evaluation and prediction on the preliminary operating efficiency;

[0037] The calculation formula of the dynamic correction factor is: ,in, is the preset adjustment coefficient, is the dynamic correction factor;

[0038] The final efficiency calculation formula is: ,in, For ultimate efficiency.

[0039] Technical effects and advantages of the present invention:

[0040] 1. The present invention obtains turbine operating parameters and environmental data, classifies the turbine operating conditions according to the environmental data, selects different turbine operating parameters according to the classification results to analyze the dynamic stress characteristics of the turbine runner, collects turbine attribute data to preliminarily estimate the turbine operating efficiency, sets multiple detection time points to detect the water pressure entering the turbine, calculates the water pressure fluctuation according to the water pressure at each detection time point, calculates the turbine operating resistance index based on the dynamic stress characteristics of the turbine runner, detects the real-time temperature of the water pressure entering the turbine, and conducts a secondary evaluation and prediction of the turbine operating efficiency based on the real-time temperature of the water pressure and the operating resistance index of the turbine, thereby achieving an accurate prediction of the turbine efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The present invention is a flow chart for implementing a method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner.

[0042] Figure 2 This is a schematic diagram of sensor network acquisition for a method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner according to the present invention.

[0043] Figure 3 The figure is a schematic diagram of realizing the dynamic stress characteristics of a turbine runner dynamic stress mapping and efficiency prediction method according to the present invention.

[0044] Figure 4 The figure is a schematic diagram of realizing the resistance index of a method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner according to the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Example 1

[0047] See also Figures 1 to 4 , a method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner, the specific operation process is as follows:

[0048] Step S1: obtaining turbine operating parameters and environmental data, and classifying turbine operating conditions according to the environmental data;

[0049] Step S2: selecting different turbine operating parameters according to the operating condition classification results to analyze the dynamic stress characteristics of the turbine runner, collecting turbine attribute data, and preliminarily estimating the turbine operating efficiency based on the turbine attribute data;

[0050] Step S3: setting multiple detection time points to detect the water pressure entering the turbine, and calculating the water pressure fluctuation based on the water pressure at each detection time point, and calculating the turbine operation resistance index based on the dynamic stress characteristics of the turbine runner;

[0051] Step S4: Detecting the real-time water pressure and temperature of the water turbine, and performing a secondary evaluation and prediction of the turbine operation efficiency based on the real-time water pressure and temperature of the water turbine and the operation resistance index.

[0052] The specific implementation is as follows:

[0053] In step S1, the operating status of the turbine and the external environment are monitored in real time through the sensor network and the acquisition device to obtain the turbine operating parameters and environmental data. The turbine operating parameters include the pressure distribution on the runner blade surface and the speed fluctuation value, and the environmental data include the water temperature change and the water flow fluctuation value. The real-time data is recorded synchronously according to the sampling time within the preset unit time.

[0054] The preset unit time is recorded as T, and each sampling moment divided within the preset unit time is recorded as , the total number of sampling moments is recorded as N. For example, if T is 60s and the sampling frequency is 100HZ, then T contains 60 sampling moments, which are , the total number of sampling moments N is 60.

[0055] The pressure distribution on the runner blade surface is the instantaneous pressure value on the runner blade surface at each sampling moment, and the expression is: , where (x,y) is the coordinate point on the blade surface, is the instantaneous pressure value at each sampling moment.

[0056] The speed fluctuation value is the dynamic deviation of the actual speed of the turbine main shaft relative to the average speed of the main shaft. The average speed of the main shaft is expressed as follows: ,in, is the actual spindle speed at each sampling moment, is the average spindle speed; the speed fluctuation value is calculated as follows: ,in, is the speed fluctuation value.

[0057] The water temperature change is the maximum deviation of the water temperature entering the turbine relative to the initial temperature: ,in, is the maximum water temperature value at each sampling moment, is the initial water temperature, is the water temperature change.

[0058] The water flow fluctuation value is the deviation of the water flow entering the turbine relative to the average water flow rate. The water flow average value is calculated as follows: ,in, is the water flow at each sampling moment, is the average water flow rate, and the calculation formula for the water flow fluctuation value is: ,in, is the water flow fluctuation value.

[0059] The operating environment of the turbine is classified into working conditions based on the comprehensive water flow fluctuation value and water temperature change: if the water flow fluctuation value is less than or equal to the preset fluctuation threshold, and the water temperature change is less than or equal to the preset water temperature threshold, the working condition level is judged to be excellent; if the water flow fluctuation value is greater than the preset fluctuation threshold, or the water temperature change is greater than the preset water temperature threshold, the working condition level is judged to be good.

[0060] By acquiring turbine operating parameters and environmental data in real time and classifying turbine operating conditions as excellent or good based on the environmental data, accurate identification of the current operating status of the turbine is achieved, which can accurately reflect the impact of the turbine operating environment on equipment performance, improve the pertinence and scientific nature of subsequent efficiency evaluations, and avoid waste of resources caused by blind adjustments.

[0061] It should be noted that a sensor network refers to a distributed data acquisition system composed of multiple sensor nodes with detection functions, which are interconnected through wired or wireless communication and are used for multi-point, multi-parameter, real-time monitoring of target equipment or environmental status; the acquisition device works together by integrating multiple high-precision sensors, including a pressure sensor array arranged in a grid on the surface of the runner blade to collect instantaneous pressure values ​​at different positions, a speed sensor installed at the end of the turbine main shaft to continuously monitor the actual speed of the main shaft, a temperature sensor installed in the water inlet area to track water temperature changes in real time, and a flow meter embedded in the water inlet pipe to collect water flow in a non-contact measurement manner, capturing the core parameters and environmental data of the turbine operation in real time. The acquisition device achieves time alignment of data acquisition through a unified clock synchronization mechanism to ensure the correlation of different parameters under the same time base. The sensor network is responsible for acquiring data from various sensor nodes, and the acquisition device is responsible for unified processing of sensor collected data.

[0062] In step S2, different operating parameters are selected according to the turbine operating condition level to analyze the dynamic stress characteristics of the turbine runner:

[0063] When the operating condition level is excellent, the pressure distribution on the runner blade surface is selected as the operating parameter; when the operating condition level is good, the speed fluctuation value is selected as the operating parameter.

[0064] When the pressure distribution on the runner blade surface is used as the operating parameter, the physical mapping of the pressure field to the stress field is achieved through spatial integral modeling combined with the mechanical modeling method, and the time average noise reduction is calculated to analyze the dynamic stress characteristics of the turbine runner:

[0065] The spatial integral modeling formula is: ,in, is the blade force area, is the local dynamic stress sensitivity coefficient, is the overall dynamic stress characteristic at each sampling moment; the average value of the overall dynamic stress characteristic at each sampling moment in the preset unit time is taken as the dynamic stress characteristic of the turbine runner, and the time average noise reduction formula is: ,in, The dynamic stress characteristics of the turbine runner.

[0066] Directly using the surface pressure distribution of the runner blades for spatial integral modeling improves the response and identification capabilities of microscopic hydraulic disturbances and enhances the accuracy of turbine condition monitoring.

[0067] When the operating condition is good, the speed fluctuation value is combined with the polynomial regression equation to calculate the dynamic stress characteristics of the turbine runner: ,in, , is the preset model coefficient, The dynamic stress characteristics of the turbine runner.

[0068] The turbine attribute data includes the runner blade surface roughness and the guide vane gap size. The runner blade surface roughness is collected by the surface profilometer and recorded as , the guide vane gap size is collected by a laser rangefinder and recorded as G, and the blade surface roughness and guide vane gap size are normalized.

[0069] Analyze the efficiency loss caused by blade surface roughness through blade surface roughness: ,in, is the water flow density, v is the water flow velocity, is the efficiency loss caused by blade surface roughness.

[0070] Analyze the efficiency loss caused by the guide vane gap size through the guide vane gap size: ,in, is the water flow density, v is the water flow velocity, is the efficiency loss caused by the guide vane gap size.

[0071] The roughness of the blade surface will disturb the water flow adhesion state and boundary layer flow, thereby causing energy loss. The greater the roughness, the stronger the water flow disturbance and the more significant the adverse effect on efficiency.

[0072] The size of the guide vane gap affects the guidance accuracy and leakage degree of the water flow. If the gap is too large, it will cause energy leakage and the water flow to deviate from the predetermined path, resulting in a decrease in operating efficiency.

[0073] Taking into account the efficiency loss caused by blade surface roughness and the efficiency loss caused by guide vane gap size, the preliminary operating efficiency of the turbine is analyzed: ,in, is the ideal efficiency value, For initial operating efficiency.

[0074] By screening different operating parameters through the operating condition classification results, the pertinence and accuracy of the dynamic stress characteristic analysis of the turbine runner are improved. The preliminary operating efficiency is estimated in combination with the turbine attribute data, providing basic data for subsequent dynamic adjustment and optimization of the final efficiency.

[0075] It should be noted that the mechanical modeling method is a mathematical modeling technology based on physical field characteristics and mechanical principles. By constructing a quantitative relationship between the pressure distribution and dynamic stress on the surface of the turbine runner blade, it can realize the accurate analysis of the dynamic stress characteristics under the operation state of the turbine. The discrete pressure data is converted into a continuous stress field distribution by using spatial integral operation, and the interaction mechanism between the water flow and the surface of the turbine runner blade is explained in combination with the local sensitivity coefficient; the local dynamic stress sensitivity coefficient is related to the material strain rate and is set by professionals, for example, through finite element simulation or dynamic loading experiment calibration; the surface profilometer is an instrument used to measure the microscopic geometric morphology of the surface of an object with high precision, and is used to detect the surface roughness of the material; the laser rangefinder is a non-contact measuring instrument based on laser triangulation or time of flight principle, which is used to accurately measure the distance between two surfaces or a target and a reference point; the ideal efficiency value is obtained through the performance characteristic curve diagram in the hydraulic model test report, where the highest point is the ideal efficiency value.

[0076] In step S3, based on the preset unit time in step S1, the turbine water pressure at the water pressure monitoring point at each sampling moment is collected through the sensor network and the collection device, and the water pressure fluctuation value is analyzed based on the turbine water pressure. The water pressure fluctuation value is the maximum pressure difference of the turbine water pressure at each water pressure monitoring point within the preset unit time of the turbine: , where K is the total number of monitoring points, At the sampling time The turbine water pressure at the j-th water pressure monitoring point in the turbine, i takes the value of 1, 2, ...N, j takes the value of 1, 2, ...K, is the maximum value of the turbine water pressure at each sampling moment, is the minimum value of the turbine water pressure at each sampling moment, is the turbine water pressure fluctuation value.

[0077] The turbine running resistance index is a quantitative indicator used to measure the friction resistance between the fluid and the runner under different working conditions. The turbine running resistance index is analyzed by comprehensively considering the turbine water pressure fluctuation value and the dynamic stress characteristics of the turbine runner: ,in, 、 is the preset adjustment coefficient, is the turbine operating resistance index.

[0078] The calculation formula of turbine operation resistance index introduces the overall square form to enhance the fusion adaptability and nonlinear ability between different physical quantities.

[0079] For example, the preset unit time T is 60s, 600 sampling moments are set, the turbine water pressure in the preset unit time is 0.45, the turbine runner dynamic stress characteristic is 18.3, and the adjustment coefficient is and If the values ​​of 0.4 and 0.6 are respectively, the turbine operation resistance index is: =(0.4·0.45+0.6·18.3)²=124.58; The larger the turbine operating resistance index, the greater the fluid resistance on the turbine runner, which affects the operating efficiency of the turbine.

[0080] By collecting the turbine water pressure and combining it with the dynamic stress characteristics of the turbine runner, the influence of fluid mechanics on the operation of the turbine is dynamically reflected. The operating resistance index effectively describes the actual stress state of the turbine and provides key parameters for subsequent efficiency correction and fault warning.

[0081] It should be noted that the water pressure monitoring points are evenly distributed at the turbine water inlet, guide vane area, runner blade surface, and turbine outlet. The evenly distributed water pressure monitoring points reflect the distribution of water flow through the turbine, which helps to improve the accuracy of the resistance index and thus improve the prediction accuracy of the turbine operating efficiency.

[0082] In step S4, based on the preset unit time, the real-time temperature of the water pressure entering the turbine at each sampling moment is collected through the sensor network and the collection device and recorded as .

[0083] The dynamic correction factor is calculated by the real-time water pressure temperature and operating resistance index, and the preliminary operating efficiency is secondary evaluated to predict the final efficiency, thereby improving the accuracy of the final efficiency.

[0084] The dynamic correction factor calculation formula is: ,in, is the preset adjustment coefficient, is the dynamic correction factor.

[0085] The final efficiency calculation formula is: ,in, For ultimate efficiency.

[0086] If the real-time temperature of the water pressure entering the turbine is higher, it means that the operating resistance index is smaller, the dynamic correction factor is smaller, and the final efficiency is closer to the initial operating efficiency; conversely, if the real-time temperature of the water pressure entering the turbine is lower, it means that the operating resistance index is larger, the dynamic correction factor is larger, and the final efficiency deviates further from the initial operating efficiency.

[0087] By collecting the real-time temperature parameters of the water pressure entering the turbine and combining them with the correlation characteristics of the operating resistance index, the initial operating efficiency is adjusted to reflect the impact of complex working conditions on the operating efficiency, avoid misjudgment caused by a single parameter, and achieve accurate evaluation and prediction of the final efficiency.

[0088] It should be noted that the preset adjustment coefficient is set by professionals to control the correction intensity and will not be described in detail here.

[0089] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0091] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0093] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0096] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0097] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0098] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner, characterized by: The following steps are involved: Step S1: obtaining turbine operating parameters and environmental data, and classifying turbine operating conditions according to the environmental data; In step S1, the operating conditions of the turbine are classified based on the comprehensive water flow fluctuation value and water temperature change: If the water flow fluctuation value is less than or equal to the preset fluctuation threshold, and the water temperature change is less than or equal to the preset water temperature threshold, the working condition level is judged to be excellent; If the water flow fluctuation value is greater than the preset fluctuation threshold, or the water temperature change is greater than the preset water temperature threshold, the working condition level is judged to be good; Step S2: selecting different turbine operating parameters according to the operating condition classification results to analyze the dynamic stress characteristics of the turbine runner, collecting turbine attribute data, and preliminarily estimating the turbine operating efficiency based on the turbine attribute data; In step S2, different operating parameters are selected according to the turbine operating condition level to analyze the dynamic stress characteristics of the turbine runner: When the operating condition is excellent, the pressure distribution on the runner blade surface is selected as the operating parameter; when the operating condition is good, the speed fluctuation value is selected as the operating parameter; If the operating condition is excellent, the fluid load on the turbine runner is modeled based on the pressure distribution on the runner blade surface and combined with the mechanical modeling method to analyze the dynamic stress characteristics of the turbine runner. If the operating condition is good, the speed fluctuation value is combined with the polynomial regression equation to calculate the dynamic stress characteristics of the turbine runner; When the pressure distribution on the runner blade surface is used as the operating parameter, the mechanical modeling method realizes the physical mapping of the pressure field to the stress field through spatial integral modeling, and calculates the time average noise reduction to analyze the dynamic stress characteristics of the turbine runner: The spatial integral modeling formula is: ,in, is the blade force area, is the local dynamic stress sensitivity coefficient, is the overall dynamic stress characteristic at each sampling moment; The average value of the overall dynamic stress characteristics at each sampling moment in the preset unit time is taken as the dynamic stress characteristics of the turbine runner. The time average noise reduction formula is: ,in, is the dynamic stress characteristic of the turbine runner; When the speed fluctuation value is used as the operating parameter, the dynamic stress characteristics of the turbine runner are analyzed: The calculation formula is: ,in, , is the preset model coefficient, is the speed fluctuation value, is the average spindle speed, is the dynamic stress characteristic of the turbine runner; Step S3: setting multiple detection time points to detect the water pressure entering the turbine, and calculating the water pressure fluctuation based on the water pressure at each detection time point, and calculating the turbine operation resistance index based on the dynamic stress characteristics of the turbine runner; Step S4: Detecting the real-time water pressure and temperature of the water turbine, and performing a secondary evaluation and prediction of the turbine operation efficiency based on the real-time water pressure and temperature of the water turbine and the operation resistance index.

2. The method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner according to claim 1, characterized in that: In step S1, the operating status of the turbine and the external environment are collected in real time to obtain turbine operating parameters and environmental data, wherein the turbine operating parameters include the pressure distribution on the runner blade surface and the speed fluctuation value, and the environmental data include the water temperature change and the water flow fluctuation value; The turbine operating parameters and environmental data are recorded synchronously according to the sampling time within the preset unit time. The preset unit time is recorded as T, and each sampling time divided within the preset unit time is recorded as , the total number of sampling moments is recorded as N.

3. The method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner according to claim 2, characterized in that: In step S1, the pressure distribution on the runner blade surface is the instantaneous pressure value on the runner blade surface at each sampling moment, and the expression is: , where (x,y) is the coordinate point on the blade surface, is the instantaneous pressure value at each sampling moment; The speed fluctuation value is the dynamic offset amplitude of the actual speed of the turbine main shaft relative to the average speed of the main shaft. The average speed of the main shaft is obtained by calculating the average of the actual speed of the turbine main shaft at each sampling moment. The square sum of the deviations between the actual speed of the main shaft and the average speed of the main shaft at each sampling moment is calculated and the square root is taken to obtain the speed fluctuation value. The water temperature change is the maximum deviation of the incoming water temperature from the initial temperature to the turbine. The difference between the incoming water temperature and the initial temperature at each sampling moment is calculated, and the maximum absolute value is taken as the water temperature change. The water flow fluctuation value is the deviation amplitude of the water flow entering the turbine relative to the average water flow value. The average water flow value is the average water flow value at each sampling moment. The water flow fluctuation value is obtained by taking the square root of the sum of the deviations between the water flow at each sampling moment and the average water flow value.

4. The method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner according to claim 1, characterized in that: In step S2, the turbine attribute data is collected, including the surface roughness of the runner blades. and the guide vane clearance G, and preliminarily estimate the turbine operating efficiency based on the turbine attribute data; The blade surface roughness and the guide vane gap size are normalized, and the efficiency loss caused by the blade surface roughness and the guide vane gap size is analyzed by the water flow density and water flow velocity. The calculation formulas are: 、 ,in, is the efficiency loss caused by the guide vane gap size, The efficiency loss caused by the guide vane gap size; Taking into account the efficiency loss caused by blade surface roughness and the efficiency loss caused by guide vane gap size, the preliminary operating efficiency of the turbine is calculated: ,in, To preset the ideal efficiency value, For initial operating efficiency.

5. The method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner according to claim 1, characterized in that: In step S3, the turbine water pressure at each water pressure monitoring point at each sampling moment is collected, and the water pressure fluctuation value is the maximum pressure difference of the turbine water pressure at each water pressure monitoring point within the preset unit time of the turbine, which is recorded as ; The turbine operation resistance index is analyzed by combining the turbine water pressure fluctuation value and the turbine runner dynamic stress characteristics: ,in, 、 is the preset adjustment coefficient, is the turbine operating resistance index.

6. The method for dynamic stress mapping and efficiency prediction of a hydraulic turbine runner according to claim 5, characterized in that: In step S4, the real-time temperature of the water pressure entering the turbine at each sampling moment is collected and recorded as ; The dynamic correction factor is calculated by integrating the real-time water pressure temperature and the operating resistance index, and the final efficiency is obtained by performing a secondary evaluation and prediction on the preliminary operating efficiency; The calculation formula of the dynamic correction factor is: ,in, is the preset adjustment coefficient, is the dynamic correction factor; The final efficiency calculation formula is: ,in, For ultimate efficiency.

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