Water-hydrogen-hydrogen steam turbine generator rotor overheating early warning method and equipment

Abstract

The invention relates to a water-hydrogen-hydrogen steam turbine generator rotor overheating early warning method and equipment, and the method comprises the steps: firstly carrying out the temperature prediction of a steam turbine generator rotor, then carrying out the prediction of the overheating degree of the steam turbine generator rotor based on the predicted temperature, and finally carrying out the diagnosis and early warning of the overheating condition of the steam turbine generator rotor based on the overheating condition of the steam turbine generator rotor. In order to solve the problem that the running temperature of a current generator rotor is not monitored by a temperature measuring point, the temperature change rule in the rotor heat exchange physical process and the lifting load change process is effectively combined, and the advantages of physical modeling and machine learning are fully utilized by big data, so that accurate modeling and dynamic prediction of rotor overheating are expected to be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of generator rotor temperature prediction, and in particular to a water-hydrogen-hydrogen steam turbine generator rotor overheating early warning method and device. Background Art

[0002] As an important power generation equipment, large steam turbine generators will inevitably generate heat due to various losses (such as rotor loss, winding loss or mechanical loss, etc.) during normal service. If this heat cannot be discharged in time by the cooling system in the unit, the generator set will generate high temperature that affects its normal operation due to the gradually accumulated heat. Therefore, temperature naturally becomes an important data that needs to be monitored in real time during the operation of the unit, providing data support for the advanced control of the temperature of large steam turbine generators, determination of maintenance cycles, and fault diagnosis and analysis.

[0003] Improper start and stop operations may cause excessive thermal stress. Forced cooling of the steam turbine under high temperature conditions may cause a large temperature difference between the inside and outside, resulting in severe thermal deformation. The rotor operates in a high temperature and high pressure environment, and the inherent defects in the material will cause deformation and cracks after long-term operation; the metal fatigue damage of the rotor material will increase with the increase of thermal stress, which may cause the rotor to deform or even crack. The deformation or cracks of the rotor will affect the stability and safety of the unit operation, and may even cause serious safety accidents. The operating environment and operating procedures of the steam turbine generator set need to be strictly monitored to avoid overheating. How to judge the degree of overheating of the water-hydrogen-hydrogen steam turbine generator rotor and issue a timely warning is very important for the power plant operation and maintenance personnel to formulate reasonable operation control and maintenance strategies.

[0004] Since there are no temperature measuring points embedded in the rotor winding body, the average temperature of the rotor winding calculated based on electromagnetic theory has a large error with the actual temperature of the rotor winding, and cannot be used as an accurate criterion for rotor overheating. The current rotor winding temperature field finite element analysis method can calculate the specific temperature of a specific part of the rotor winding, but it is still in the laboratory research stage due to the influence of boundary conditions and the complicated and time-consuming calculation. There is no mature case of engineering application. Summary of the invention

[0005] The present invention proposes a water-hydrogen-hydrogen steam turbine generator rotor overheating warning method, device and storage medium, which can solve at least one of the technical problems in the background technology.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A water-hydrogen-hydrogen steam turbine generator rotor overheating early warning method, comprising first predicting the temperature of the steam turbine generator rotor, then predicting the degree of overheating of the steam turbine generator rotor based on the predicted temperature, and finally diagnosing and early warning the overheating condition of the steam turbine generator rotor based on the overheating condition of the steam turbine generator rotor;

[0008] The turbine generator rotor temperature prediction specifically includes:

[0009] Calculate the average rotor temperature under different working conditions based on the excitation current, excitation voltage, rotor DC resistance, and inlet hydrogen temperature;

[0010] Based on the correlation calculation, the parameters affecting the rotor temperature are found, and the rotor static temperature model and dynamic temperature model are constructed.

[0011] Furthermore, the average rotor temperature under different working conditions is calculated according to the excitation current, the excitation voltage, the rotor DC resistance, and the inlet hydrogen temperature; specifically,

[0012] The calculation formula of rotor average temperature is:

[0013] 0 x =KR-235

[0014] Where: R0 represents the resistance value when the rotor winding temperature is θ0;

[0015] R X Indicates the resistance value of the rotor at any time, which is R X =U F / I F , where: U F is the excitation voltage, I F is the excitation current.

[0016] Furthermore, according to the correlation calculation, the rotor temperature influencing parameters are found, and the rotor static temperature model and dynamic temperature model are built, which specifically include:

[0017] 1) Rotor static temperature model

[0018] According to the correlation degree of the physical parameters on the turbine generator rotor temperature, the temperature calculation formula is established, and the static temperature model is preliminarily built using the no-load test data of the unit:

[0019] Rotor temperature calculation formula: T t =A×I f 2 +B×U f +T0

[0020] Among them: A and B are constants for a certain generator;

[0021] If is the excitation current, U f is the excitation voltage, T0 is the inlet hydrogen temperature;

[0022] 2) Rotor dynamic temperature model

[0023] Based on the principles of electromagnetics and heat conduction, combined with the characteristics of different operating conditions and the coupling relationship between the steam turbine generator and part cooling, the dynamic temperature model is built and trained.

[0024] Furthermore, according to the electromagnetic principle and heat conduction principle, combined with the characteristics of different working conditions and the coupling relationship between the turbine generator and the cooling of the parts, the dynamic temperature model is built and trained, including:

[0025] 2.1) Working condition model

[0026] According to the different active power and reactive power values ​​of the generator, the representative working conditions are named, including:

[0027] "Generator phase advance" represents the operating condition when the reactive power is less than 0;

[0028] “Generator heavy load operation” refers to the operating condition when the active power is greater than 300MW;

[0029] “Generator normal operating condition 210” represents the operating condition when the active power is less than 210MW;

[0030] “Generator normal operating condition 210-240” represents the operating condition when the active power is within the range of 210 to 240 MW;

[0031] 2.2) Electromagnetic principle modeling

[0032] According to electromagnetic principles, a dynamic model is created according to different operating conditions of the generator, and explicit function algorithms are tried to select appropriate coefficients for different operating conditions to optimize the rotor temperature model.

[0033] Rotor temperature calculation formula: Tt=A×I f 2 +B×U f +C×P+D×Q+E×T0+F

[0034] Among them: A and B are parameters in the static model;

[0035] C, D, E, and F are constants for a certain generator;

[0036] I f is the excitation current, U f is the excitation voltage, P is the active power, Q is the reactive power, and T0 is the inlet hydrogen temperature;

[0037] 2.3) Heat conduction principle modeling

[0038] According to the principle of heat conduction, a dynamic model is created according to the coupling relationship of cooling media in various parts of the steam turbine generator, the fitting degree of different algorithms is tried, and the appropriate combination algorithm is selected to optimize the rotor temperature model;

[0039] The input parameters are set as the average value of cold hydrogen temperature, the average value of hot hydrogen temperature, and the average value of core temperature. The output parameter is the rotor temperature. The algorithm models try various linear regression and nonlinear regression classic algorithms including AlAgent, Linear, Ridge, GBDT, and MLP.

[0040] 2.4) Combine the electromagnetic principle and heat conduction principle models, use historical data, and train an appropriate dynamic temperature model.

[0041] Furthermore, the overheating degree of the turbine generator rotor is predicted based on the predicted temperature, including introducing the deviation between the dynamic model output value and the electromagnetic mechanism calculation value, the rotor temperature rise rate and other dynamic classification criteria on the basis of the static threshold classification of the rotor temperature, and predicting the overheating degree of the rotor, as follows:

[0042] 1) Static threshold of rotor temperature

[0043] Taking the 630MW unit as an example, the static threshold classification criteria for rotor temperature are:

[0044] Working conditions: Level I: 100℃> calculated rotor average temperature value ≥ 90℃; Level II: 105℃> calculated rotor average temperature value ≥ 100℃; Level III: calculated rotor average temperature value ≥ 110℃;

[0045] Delayed phase condition: When P≥600MW: Level I: 100℃> calculated rotor average temperature value ≥90℃; Level II: 105℃> calculated rotor average temperature value ≥100℃; Level III: calculated rotor average temperature value ≥110℃;

[0046] Late phase condition: 600MW>P≥300MW: Level I: 95℃>calculated rotor average temperature value ≥90℃; Level II: 100℃>calculated rotor average temperature value ≥95℃; Level III:calculated rotor average temperature value ≥100℃;

[0047] Late phase condition: When P<300MW: Level I: 90℃> calculated rotor average temperature value ≥85℃; Level II: 95℃> calculated rotor average temperature value ≥90℃; Level III: calculated rotor average temperature value ≥95℃;

[0048] 2) Deviation between the dynamic model output value and the electromagnetic mechanism calculation value

[0049] According to different operating conditions, the deviation between the output value of each rotor dynamic model of the generator and the calculated value of the electromagnetic mechanism is calculated, and the deviation is checked by comparing the current operating conditions; taking the 630MW unit as an example, the rotor temperature deviation classification criterion is:

[0050] Working conditions: Level I: 25K> rotor temperature deviation ≥ 10K; Level II: 40K> rotor temperature deviation ≥ 25K; Level III: 55K> rotor temperature deviation ≥ 40K;

[0051] Delayed phase condition: When P≥600MW: Level I: 25K> rotor temperature deviation ≥10K; Level II: 40K> rotor temperature deviation ≥25K; Level III: 55K> rotor temperature deviation ≥40K;

[0052] Delayed phase condition: 600MW>P≥300MW: Level I: 20K>rotor temperature deviation ≥10K; Level II: 30K>rotor temperature deviation ≥20K; Level III: 40K>rotor temperature deviation ≥30K;

[0053] Delayed phase condition: When P<300MW: Level I: 15K> rotor temperature deviation ≥10K; Level II: 20K> rotor temperature deviation ≥15K; Level III: 25K> rotor temperature deviation ≥20K;

[0054] 3) Rotor temperature rise rate

[0055] Automatically compare the temperature rise rate of the turbine generator rotor model value and the calculated value according to different working conditions, automatically select the working conditions with higher temperature rise rate during the period, and perform deviation verification by comparing the working conditions at that time; taking the 630MW unit as an example, the rotor temperature rise rate classification criteria are:

[0056] Working conditions: Level I: 2.5K / min> rotor temperature rise rate ≥ 1K / min; Level II: 4K / min> rotor temperature rise rate ≥ 2.5K / min; Level III: 5.5K / min> rotor temperature rise rate ≥ 4K / min;

[0057] Delayed phase condition: When P≥600MW: Level I: 2.5K / min> rotor temperature rise rate ≥1K / min; Level II: 4K / min> rotor temperature rise rate ≥2.5K / min; Level III: 5.5K / min> rotor temperature rise rate ≥4K / min;

[0058] Delayed phase condition: 600MW>P≥300MW: Level I: 2K / min>rotor temperature rise rate ≥1K / min; Level II: 3K / min>rotor temperature rise rate ≥2K / min; Level III: 4K / min>rotor temperature rise rate ≥3K / min;

[0059] Delayed phase condition: When P<300MW: Level I: 1.5K / min> rotor temperature rise rate ≥1K / min; Level II: 2K / min> rotor temperature rise rate ≥1.5K / min; Level III: 2.5K / min> rotor temperature rise rate ≥2K / min;

[0060] 5) Prediction of rotor overheating

[0061] According to the analysis of historical data, the predicted value of rotor overheating is A TZGR =0.4*A ZJ +0.4*A ZP +0.2*A ZS

[0062] Among them: A TZGR is the predicted value of rotor overheating degree, A ZJ Assign a static threshold value for the rotor temperature, A ZP Assign a value to the deviation between the dynamic model output value and the electromagnetic mechanism calculation value, A ZS Assign a value to the rotor temperature rise rate;

[0063] A ZJ No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7;

[0064] A ZP No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7;

[0065] A ZS No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7;

[0066] A TZGR :Level I warning is: 0.9; Level II warning is: 0.8; Level III warning is: 0.

[0067] Further, based on the overheating of the turbine generator rotor, a diagnosis and early warning of the overheating of the turbine generator rotor is performed, including:

[0068] Self-learning of historical normal operation data and fault operation data, continuous improvement of dynamic models, automatic completion of quantitative warning level configuration, generation of turbine generator rotor overheating warning model, and rotor overheating status diagnosis according to different overheating levels;

[0069] According to the overheating warning scores of the three types of models, the appropriate criterion for rotor overheating status is obtained.

[0070] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0071] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0072] It can be seen from the above technical scheme that the present invention is a method for predicting overheating of a water-hydrogen-hydrogen steam turbine generator rotor, which realizes online and real-time diagnosis of overheating of the steam turbine generator rotor based on the prediction of overheating trend and degree of the steam turbine generator rotor. It mainly solves the problem that offline test diagnosis is limited by the maintenance schedule of the unit and the low sensitivity and accuracy of online diagnosis. The implementation process is simple and easy to operate, and offline maintenance and test conditions are added for comprehensive judgment.

[0073] In general, the present invention aims to solve the problem of the current generator rotor having no temperature measurement points to monitor the operating temperature. It effectively combines the physical process of rotor heat exchange and the temperature change law during load changes, and fully utilizes the advantages of big data for physical modeling and machine learning, in order to achieve accurate modeling and dynamic prediction of rotor overheating.

[0074] The beneficial effects of the present invention are as follows:

[0075] 1) Improve safety. By real-time monitoring of the temperature field changes of the rotor, abnormal temperature rise of the rotor can be discovered in time to prevent mechanical failure or damage caused by overheating, thereby ensuring the safe and stable operation of the unit.

[0076] 2) Economic optimization. Online temperature monitoring can provide operators with real-time temperature information of the rotor and other key components, helping them optimize operating strategies, such as ramp rate, load change rate, and steam temperature change rate, to extend component life and reduce unnecessary maintenance costs, thereby improving operational economy.

[0077] 3.) Condition maintenance support. Through the online rotor overheat analysis system, on-demand maintenance mode can be achieved, that is, maintenance or inspection is only carried out when the components need it, which can reduce unnecessary maintenance activities, improve maintenance efficiency, and ensure the optimal operating status of the unit.

[0078] 4) Operation optimization guidance. The online temperature calculation results can be directly used to guide the operations of on-site operators, such as adjusting operating parameters to avoid or reduce excessive thermal stress and further extend the service life of key components such as rotors.

[0079] 5) Preventive maintenance. Real-time analysis of rotor overheating can help establish a more effective preventive maintenance plan, detect potential maintenance needs in advance through predictive analysis, and avoid equipment failure and downtime.

[0080] 6) Improve operational flexibility. The online rotor overheat analysis system can provide real-time temperature data to help operators make quick adjustments when necessary to respond to changes in grid demand or other operational requirements, thereby improving the operational flexibility of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is a flow chart of the water-hydrogen-hydrogen steam turbine generator rotor overheating early warning method described in an embodiment of the invention;

[0082] Figure 2 The simulation data of the 630MW unit in delayed phase operation is simulated in the calculation model of the embodiment of the present invention. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0084] like Figure 1 As shown, the water-hydrogen-hydrogen steam turbine generator rotor overheating early warning method described in the embodiment of the present invention includes three parts: steam turbine generator rotor temperature prediction, steam turbine generator rotor overheating degree prediction, and steam turbine generator rotor overheating condition diagnosis. It includes first predicting the steam turbine generator rotor temperature, then predicting the steam turbine generator rotor overheating degree based on the predicted temperature, and finally diagnosing and warning the steam turbine generator rotor overheating condition based on the steam turbine generator rotor overheating condition; the following are detailed descriptions respectively:

[0085] 1. The prediction contents of turbine generator rotor temperature are as follows:

[0086] 1. Calculate rotor temperature using electromagnetic mechanism

[0087] The average rotor temperature under different operating conditions is calculated based on the excitation current, excitation voltage, rotor DC resistance and inlet hydrogen temperature.

[0088] The calculation formula of rotor average temperature is:

[0089] θ x =KR x -235, of which

[0090] Where: R0 is the resistance value when the rotor winding temperature is θ0.

[0091] R X ——The resistance value of the rotor is calculated at any time, and its value is R X =U F / I F (Where: U F is the excitation voltage, I F is the excitation current).

[0092] 2. “AI+Physical Mechanism” algorithm calculates rotor temperature

[0093] Based on the correlation calculation, the parameters affecting the rotor temperature are found, and the rotor static temperature model and dynamic temperature model are constructed.

[0094] 1) Rotor static temperature model

[0095] According to the correlation degree of the physical parameters on the turbine generator rotor temperature, the temperature calculation formula is established, and the static temperature model is preliminarily built using the no-load test data of the unit:

[0096] Rotor temperature calculation formula: T t =A×I f 2 +B×U f +T0

[0097] Among them: A and B are constants for a certain generator;

[0098] I f is the excitation current, U f is the excitation voltage, T0 is the inlet hydrogen temperature;

[0099] 2) Rotor dynamic temperature model

[0100] According to the electromagnetic principle and heat conduction principle, combined with the characteristics of different working conditions and the coupling relationship between the turbine generator and the cooling of the parts, the dynamic temperature model is built and trained:

[0101] 2.1) Working condition model

[0102] According to the different active power and reactive power values ​​of the generator, the representative working conditions are named, such as:

[0103] "Generator phase advance" represents the operating condition when the reactive power is less than 0;

[0104] “Generator heavy load operation” refers to the operating condition when the active power is greater than 300MW;

[0105] “Generator normal operating condition 210” represents the operating condition when the active power is less than 210MW;

[0106] “Generator normal operating condition 210-240” represents the operating condition when the active power is within the range of 210 to 240 MW;

[0107] And so on.

[0108] 2.2) Electromagnetic principle modeling

[0109] Based on electromagnetic principles, a dynamic model is created according to different operating conditions of the generator, and the explicit function algorithm is tried to select appropriate coefficients for different operating conditions and optimize the rotor temperature model.

[0110] Rotor temperature calculation formula: Tt=A×I f 2 +B×U f +C×P+D×Q+E×T0+F

[0111] Among them: A and B are parameters in the static model;

[0112] C, D, E, and F are constants for a certain generator;

[0113] I f is the excitation current, U f is the excitation voltage, P is the active power, Q is the reactive power, and T0 is the inlet hydrogen temperature.

[0114] 2.3) Heat conduction principle modeling

[0115] According to the principle of heat conduction, a dynamic model is created according to the coupling relationship of cooling media in various parts of the steam turbine generator. The fitting degree of different algorithms is tried, and the appropriate combination algorithm is selected to optimize the rotor temperature model.

[0116] The input parameters are set as the average temperature of cold hydrogen, the average temperature of hot hydrogen, and the average temperature of core. The output parameter is the rotor temperature. The algorithm models try various linear regression and nonlinear regression classic algorithms such as AlAgent, Linear, Ridge, GBDT, and MLP.

[0117] 2.4) Combine the electromagnetic principle and heat conduction principle models, and use historical data to train a suitable dynamic temperature model. Specifically, the model training method provided by the machine learning algorithm can be to continuously characterize the polynomial coefficients using historical data, or to compare typical machine learning algorithms after clarifying the input and output parameters, and find algorithms or algorithm combinations with high fitting degree. The selection of historical data should be combined with the generator maintenance and operating conditions. The historical data should be historical data that covers various operating conditions when the generator rotor has not undergone major maintenance or replacement of major components.

[0118] 2. Overheating prediction

[0119] On the basis of the static threshold classification of rotor temperature, dynamic classification criteria such as the deviation between the dynamic model output value and the electromagnetic mechanism calculation value and the rotor temperature rise rate are introduced to predict the rotor overheating degree, such as Figure 2 As shown;

[0120] 1) Static threshold of rotor temperature

[0121] Taking the 630MW unit as an example, the static threshold classification criteria for rotor temperature are:

[0122] Working conditions: Level I: 100℃> calculated rotor average temperature value ≥ 90℃; Level II: 105℃> calculated rotor average temperature value ≥ 100℃; Level III: calculated rotor average temperature value ≥ 110℃

[0123] Delayed phase condition: When P≥600MW: Level I: 100℃> calculated rotor average temperature value ≥90℃; Level II: 105℃> calculated rotor average temperature value ≥100℃; Level III: calculated rotor average temperature value ≥110℃

[0124] Delayed phase condition: 600MW>P≥300MW: Level I: 95℃>Calculated rotor average temperature value ≥90℃; Level II: 100℃>Calculated rotor average temperature value ≥95℃; Level III: Calculated rotor average temperature value ≥100℃

[0125] Delayed phase condition: When P<300MW: Level I: 90℃> calculated rotor average temperature value ≥85℃; Level II: 95℃> calculated rotor average temperature value ≥90℃; Level III: calculated rotor average temperature value ≥95℃

[0126] 2) Deviation between the dynamic model output value and the electromagnetic mechanism calculation value

[0127] According to different operating conditions, the deviation between the output value of each rotor dynamic model of the generator and the calculated value of the electromagnetic mechanism is calculated, and the deviation is checked by comparing the current operating conditions. Taking the 630MW unit as an example, the rotor temperature deviation classification criteria are:

[0128] Working conditions: Level I: 25K> rotor temperature deviation ≥ 10K; Level II: 40K> rotor temperature deviation ≥ 25K; Level III: 55K> rotor temperature deviation ≥ 40K

[0129] Delayed phase condition: When P≥600MW: Level I: 25K> rotor temperature deviation ≥10K; Level II: 40K> rotor temperature deviation ≥25K; Level III: 55K> rotor temperature deviation ≥40K

[0130] Delayed phase condition: 600MW>P≥300MW: Level I: 20K>rotor temperature deviation ≥10K; Level II: 30K>rotor temperature deviation ≥20K; Level III: 40K>rotor temperature deviation ≥30K

[0131] Delayed phase condition: When P<300MW: Level I: 15K> rotor temperature deviation ≥10K; Level II: 20K> rotor temperature deviation ≥15K; Level III: 25K> rotor temperature deviation ≥20K

[0132] 3) Rotor temperature rise rate

[0133] Automatically compare the temperature rise rate of the turbine generator rotor model value and the calculated value according to different working conditions, automatically select the working conditions with higher temperature rise rate, and compare the current working conditions for deviation verification. Taking the 630MW unit as an example, the rotor temperature rise rate classification criteria are:

[0134] Working conditions: Level I: 2.5K / min> rotor temperature rise rate ≥ 1K / min; Level II: 4K / min> rotor temperature rise rate ≥ 2.5K / min; Level III: 5.5K / min> rotor temperature rise rate ≥ 4K / min

[0135] Delayed phase condition: When P≥600MW: Level I: 2.5K / min> rotor temperature rise rate ≥1K / min; Level II: 4K / min> rotor temperature rise rate ≥2.5K / min; Level III: 5.5K / min> rotor temperature rise rate ≥4K / min

[0136] Delayed phase condition: 600MW>P≥300MW: Level I: 2K / min>rotor temperature rise rate ≥1K / min; Level II: 3K / min>rotor temperature rise rate ≥2K / min; Level III: 4K / min>rotor temperature rise rate ≥3K / min

[0137] Delayed phase condition: When P<300MW: Level I: 1.5K / min> rotor temperature rise rate ≥1K / min; Level II: 2K / min> rotor temperature rise rate ≥1.5K / min; Level III: 2.5K / min> rotor temperature rise rate ≥2K / min

[0138] 4) Prediction of rotor overheating

[0139] According to the analysis of historical data, the predicted value of rotor overheating is A TZGR =0.4*A ZJ +0.4*A ZP +0.2*A ZS

[0140] Among them: A TZGR is the predicted value of rotor overheating degree, AZJ Assign a static threshold value for the rotor temperature, A ZP Assign a value to the deviation between the dynamic model output value and the electromagnetic mechanism calculation value, A ZS Assign a value to the rotor temperature rise rate.

[0141] A ZJ No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7

[0142] A ZP No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7

[0143] A ZS No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7

[0144] A TZGR :Level I warning is: 0.9; Level II warning is: 0.8; Level III warning is: 0.7.

[0145] 3. Overheating diagnosis

[0146] It conducts self-learning on historical normal operation data and fault operation data, continuously improves the dynamic model, automatically completes the configuration of quantitative warning levels, generates a turbine generator rotor overheating warning model, and diagnoses the rotor overheating status according to different overheating levels.

[0147] According to the overheating warning scores of the three types of models, the appropriate criterion for rotor overheating status is obtained.

[0148] Individual score ≥ 80 points and comprehensive score ≥ 90 points, the rotor is healthy;

[0149] 80 points> single item score ≥ 70 points and 90 points> comprehensive score ≥ 80 points, the rotor is sub-healthy;

[0150] 70 points > single item score ≥ 60 points or 80 points > comprehensive score ≥ 70 points, the rotor has the risk of overheating;

[0151] 60 points > single item score or 70 points > comprehensive score ≥ 60 points, the rotor is overheated.

[0152] In summary, the embodiments of the present invention solve the problem of the current generator rotor having no temperature measurement point to monitor the operating temperature, effectively combine the physical process of rotor heat exchange and the temperature change law during the load change process, and make full use of the advantages of big data for physical modeling and machine learning, in order to achieve accurate modeling and dynamic prediction of rotor overheating.

[0153] like Figure 2As shown, the calculation model simulates the simulation data of the 630MW unit in late phase operation, with an active power of 387.877MW. The excitation current increases from 2537.136A to 2818.518A, the simulated corresponding excitation voltage increases from 230.413V to 280.361V, and the calculated value of the average rotor temperature increases from 65.692℃ to 94.349℃, meeting the late phase operating conditions: 600MW>P≥300MW: Level I: 95℃>calculated value of rotor average temperature≥90℃ alarm conditions, and the alarm has been triggered.

[0154] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0155] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0156] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute any of the water-hydrogen-hydrogen steam turbine generator rotor overheating warning methods in the above-mentioned embodiments.

[0157] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0158] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it 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. When the computer program instructions are loaded and 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 may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may 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 may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may 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 (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0159] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0160] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0161] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A water-hydrogen-hydrogen turbine generator rotor overheating early warning method, It is characterized in that It includes first predicting the temperature of the turbine generator rotor, then predicting the overheating degree of the turbine generator rotor based on the predicted temperature, and finally diagnosing and warning the overheating condition of the turbine generator rotor based on the overheating condition of the turbine generator rotor; The turbine generator rotor temperature prediction specifically includes: Calculate the average rotor temperature under different working conditions based on the excitation current, excitation voltage, rotor DC resistance, and inlet hydrogen temperature; Based on the correlation calculation, the parameters affecting the rotor temperature are found, and the rotor static temperature model and dynamic temperature model are constructed.

2. The water-hydrogen-hydrogen turbine generator rotor overheating early warning method according to claim 1, Features: According to the excitation current, excitation voltage, rotor DC resistance, and inlet hydrogen temperature, the average rotor temperature under different working conditions is calculated; specifically, The formula for calculating the average rotor temperature is: θ x =KR x -235, of which Where: R 0 The rotor winding temperature is θ 0 The resistance value when R X Indicates the resistance value of the rotor at any time, which is R X =U F / I F , where: U F is the excitation voltage, I F is the excitation current.

3. The water-hydrogen-hydrogen turbine generator rotor overheating early warning method according to claim 2, Features: According to the correlation calculation, the rotor temperature influencing parameters are found, and the rotor static temperature model and dynamic temperature model are built, including: 1) Rotor static temperature model According to the correlation degree of the physical parameters on the turbine generator rotor temperature, the temperature calculation formula is established, and the static temperature model is preliminarily built using the no-load test data of the unit: Rotor temperature calculation formula: T t =A×I f 2 +B×U f +T 0 Among them: A and B are constants for a certain generator; I f is the excitation current, U f is the excitation voltage, T 0 is the inlet hydrogen temperature; 2) Rotor dynamic temperature model Based on the principles of electromagnetics and heat conduction, combined with the characteristics of different operating conditions and the coupling relationship between the steam turbine generator and part cooling, the dynamic temperature model is built and trained.

4. The water-hydrogen-hydrogen turbine generator rotor overheating early warning method according to claim 3, Features: According to the electromagnetic principle and heat conduction principle, combined with the characteristics of different working conditions and the coupling relationship between the turbine generator and the cooling of the parts, the dynamic temperature model is built and trained, including: 2.1) Working condition model According to the different active power and reactive power values ​​of the generator, the representative working conditions are named, including: "Generator phase advance" represents the operating condition when the reactive power is less than 0; "Generator heavy load operation" refers to the operating condition when the active power is greater than 300MW; "Generator normal operating condition 210" represents the operating condition when the active power is less than 210MW; "Generator normal operating condition 210-240" represents the operating condition when the active power is within the range of 210 to 240 MW; 2.2) Electromagnetic principle modeling According to electromagnetic principles, a dynamic model is created according to different operating conditions of the generator, and explicit function algorithms are tried to select appropriate coefficients for different operating conditions to optimize the rotor temperature model. Rotor temperature calculation formula: Tt = A × I f 2 +B×U f +C×P+D×Q+E×T 0 +F Among them: A and B are parameters in the static model; C, D, E, and F are constants for a certain generator; I f is the excitation current, U f is the excitation voltage, P is the active power, Q is the reactive power, T 0 is the inlet hydrogen temperature; 2.3) Heat conduction principle modeling According to the principle of heat conduction, a dynamic model is created according to the coupling relationship of cooling media in various parts of the steam turbine generator, the fitting degree of different algorithms is tried, and the appropriate combination algorithm is selected to optimize the rotor temperature model; The input parameters are set as the average value of cold hydrogen temperature, the average value of hot hydrogen temperature, and the average value of core temperature. The output parameter is the rotor temperature. The algorithm models try various linear regression and nonlinear regression classic algorithms including AlAgent, Linear, Ridge, GBDT, and MLP. 2.4) Combine the electromagnetic principle and heat conduction principle models, use historical data, and train an appropriate dynamic temperature model.

5. The water-hydrogen-hydrogen turbine generator rotor overheating early warning method according to claim 1, Features: Based on the predicted temperature, the overheating degree of the turbine generator rotor is predicted. This includes introducing the deviation between the dynamic model output value and the electromagnetic mechanism calculation value, the rotor temperature rise rate and other dynamic classification criteria on the basis of the static threshold classification of the rotor temperature, and predicting the overheating degree of the rotor. The details are as follows: 1) Static threshold of rotor temperature Taking the 630MW unit as an example, the static threshold classification criteria for rotor temperature are: Working conditions: Level I: 100℃> calculated rotor average temperature value ≥ 90℃; Level II: 105℃> calculated rotor average temperature value ≥ 100℃; Level III: calculated rotor average temperature value ≥ 110℃; Delayed phase condition: When P≥600MW: Level I: 100℃> calculated rotor average temperature value ≥90℃; Level II: 105℃> calculated rotor average temperature value ≥100℃; Level III: calculated rotor average temperature value ≥110℃; Late phase condition: 600MW>P≥300MW: Level I: 95℃>calculated rotor average temperature value ≥90℃; Level II: 100℃>calculated rotor average temperature value ≥95℃; Level III:calculated rotor average temperature value ≥100℃; Late phase condition: When P<300MW: Level I: 90℃> calculated rotor average temperature value ≥85℃; Level II: 95℃> calculated rotor average temperature value ≥90℃; Level III: calculated rotor average temperature value ≥95℃; 2) Deviation between the dynamic model output value and the electromagnetic mechanism calculation value According to different operating conditions, the deviation between the output value of each rotor dynamic model of the generator and the calculated value of the electromagnetic mechanism is calculated, and the deviation is checked by comparing the current operating conditions; taking the 630MW unit as an example, the rotor temperature deviation classification criterion is: Working conditions: Level I: 25K> rotor temperature deviation ≥ 10K; Level II: 40K> rotor temperature deviation ≥ 25K; Level III: 55K> rotor temperature deviation ≥ 40K; Delayed phase condition: When P≥600MW: Level I: 25K> rotor temperature deviation ≥10K; Level II: 40K> rotor temperature deviation ≥25K; Level III: 55K> rotor temperature deviation ≥40K; Delayed phase condition: 600MW>P≥300MW: Level I: 20K>rotor temperature deviation ≥10K; Level II: 30K>rotor temperature deviation ≥20K; Level III: 40K>rotor temperature deviation ≥30K; Delayed phase condition: When P<300MW: Level I: 15K> rotor temperature deviation ≥10K; Level II: 20K> rotor temperature deviation ≥15K; Level III: 25K> rotor temperature deviation ≥20K; 3) Rotor temperature rise rate Automatically compare the temperature rise rate of the turbine generator rotor model value and the calculated value according to different working conditions, automatically select the working conditions with higher temperature rise rate during the period, and compare the current working conditions for deviation verification; taking the 630MW unit as an example, the rotor temperature rise rate classification criteria are: Working conditions: Level I: 2.5K / min> rotor temperature rise rate ≥ 1K / min; Level II: 4K / min> rotor temperature rise rate ≥ 2.5K / min; Level III: 5.5K / min> rotor temperature rise rate ≥ 4K / min; Delayed phase condition: When P≥600MW: Level I: 2.5K / min> rotor temperature rise rate ≥1K / min; Level II: 4K / min> rotor temperature rise rate ≥2.5K / min; Level III: 5.5K / min> rotor temperature rise rate ≥4K / min; Delayed phase condition: 600MW>P≥300MW: Level I: 2K / min>rotor temperature rise rate ≥1K / min; Level II: 3K / min>rotor temperature rise rate ≥2K / min; Level III: 4K / min>rotor temperature rise rate ≥3K / min; Delayed phase condition: When P<300MW: Level I: 1.5K / min> rotor temperature rise rate ≥1K / min; Level II: 2K / min> rotor temperature rise rate ≥1.5K / min; Level III: 2.5K / min> rotor temperature rise rate ≥2K / min; 4) Prediction of rotor overheating According to the analysis of historical data, the predicted value of rotor overheating is A TZGR =0.4*A ZJ +0.4*A ZP +0.2*A ZS Among them: A TZGR is the predicted value of rotor overheating degree, A ZJ Assign a static threshold value for the rotor temperature, A ZP Assign a value to the deviation between the dynamic model output value and the electromagnetic mechanism calculation value, A ZS Assign a value to the rotor temperature rise rate; A ZJ No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7; A ZP No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7; A ZS No warning value: 1.0; Level I value: 0.9; Level II value: 0.8; Level III value: 0.7; A TZGR :Level I warning is: 0.9; Level II warning is: 0.8; Level III warning is:

0.

6. The water-hydrogen-hydrogen turbine generator rotor overheating early warning method according to claim 1, Features: Based on the overheating of the turbine generator rotor, the turbine generator rotor overheating condition diagnosis and warning are carried out, including: Self-learning of historical normal operation data and fault operation data, continuous improvement of dynamic models, automatic completion of quantitative warning level configuration, generation of turbine generator rotor overheating warning model, and rotor overheating status diagnosis according to different overheating levels; According to the overheating warning scores of the three types of models, the appropriate criterion for rotor overheating status is obtained.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 6.

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