Model-based fan gearbox temperature early warning method, medium and equipment

Abstract

The invention provides a model-based fan gear box temperature early warning method, medium and equipment, and the method comprises the steps: selecting a fan gear box with a typical structure, installing a temperature sensor and a working condition sensor at key parts of the gear box, and building a fan gear box physical model according to the temperature sensor and the working condition sensor; acquiring operation parameters of the gearbox under different working conditions; based on the heat conduction differential equation and the energy balance equation of the fan gearbox, a solving equation of the relation between the gearbox temperature and the operation parameters is obtained; based on the operation parameters of the gearbox and the solving equation of the relation between the temperature and the operation parameters, temperature prediction values of the gearbox under different working conditions are obtained, and the maximum prediction temperature value is selected from the temperature prediction values to serve as the maximum prediction temperature; and obtaining the early warning state and the fault risk degree of the gearbox based on the maximum predicted temperature, the temperature early warning threshold calculation equation and the fault risk assessment equation. According to the invention, effective early warning can be carried out on the temperature of the fan gear box, and safe and stable operation of the fan gear box is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of temperature monitoring and early warning of wind power generation equipment, and in particular to a model-based wind turbine gearbox temperature early warning method, medium and equipment. Background Art

[0002] The wind turbine gearbox is a key component of the wind turbine generator set, and its operating status directly affects the stability and reliability of the entire wind turbine system. During the operation of the wind turbine, the temperature change of the gearbox is one of the important indicators reflecting its operating status. Accurate monitoring and early warning of the gearbox temperature is crucial to prevent failures and improve the operating efficiency of the wind turbine.

[0003] At present, there are various methods for monitoring and warning the temperature of wind turbine gearboxes. Some traditional methods are mainly based on empirical threshold settings, and judge whether to issue a warning by simply comparing the actual temperature with the preset threshold. However, this method lacks comprehensive consideration of the complex heat transfer process inside the gearbox and the changes in operating conditions, and the accuracy of the warning is limited. In addition, although some existing technologies attempt to establish models for temperature prediction, the models are often oversimplified and do not fully consider the impact of multiple factors on temperature, such as speed, load, and lubrication status under different operating conditions, resulting in deviations between the prediction results and the actual situation, making it difficult to achieve accurate and effective temperature warnings.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the traditional empirical threshold warning method is not accurate enough, and the existing model prediction method does not fully consider multiple operating conditions, resulting in large temperature prediction deviations, and unable to accurately and effectively warn of temperature changes in the fan gearbox, affecting the safe and stable operation of the fan. Summary of the invention

[0005] The embodiments of the present invention aim to provide a model-based wind turbine gearbox temperature early warning method, medium and device to solve the technical problems raised in the prior art.

[0006] The embodiment of the present invention solves the technical problem by adopting the following technical solution:

[0007] In a first aspect, a model-based wind turbine gearbox temperature early warning method is provided, comprising:

[0008] A fan gearbox with a typical structure was selected, and temperature sensors and operating condition sensors were installed at key locations of the gearbox, based on which a physical model of the fan gearbox was constructed.

[0009] Obtain the operating parameters of the gearbox under different working conditions, including but not limited to speed, load, and lubrication status parameters;

[0010] Based on the heat conduction differential equation and energy balance equation of the fan gearbox, the solution equation of the relationship between the gearbox temperature and the operating parameters is obtained;

[0011] Based on the operating parameters of the gearbox and the solved equation of the relationship between temperature and the operating parameters, the temperature prediction values ​​of the gearbox under different working conditions are obtained and the highest predicted temperature value is selected as the maximum predicted temperature;

[0012] The warning state and the degree of fault risk of the gearbox are obtained based on the maximum predicted temperature and the temperature warning threshold calculation equation and the fault risk assessment equation.

[0013] Furthermore, the operating condition sensor includes a speed sensor, a torque sensor and an oil pressure sensor, including the following three installation positions:

[0014] Install a speed sensor and a torque sensor on the input shaft of the gearbox;

[0015] Install an oil pressure sensor on the main oil pipe of the gearbox lubrication system;

[0016] Install the temperature sensor at the appropriate position of the gearbox housing.

[0017] Furthermore, the operating parameters of the gearbox under different working conditions are obtained by the following formula:

[0018]

[0019] Q=k1×P+k2

[0020] Where P is power, n is speed, T is torque, Q is lubrication flow, and k1 and k2 are coefficients related to the gearbox structure.

[0021] Furthermore, the heat conduction differential equation of the fan gearbox is:

[0022]

[0023] Where ρ is the gearbox material density, c is the specific heat capacity, T is the temperature, t is the time, k is the thermal conductivity, and q is the internal heat source term.

[0024] The energy balance equation is:

[0025] Q in -Q out +Q gen =Q st

[0026] Among them, Q in is the input heat, Q out is the heat transferred, Q gen The internal heat generated, Q st Store changes for heat.

[0027] Furthermore, the equation for solving the relationship between gearbox temperature and operating parameters is:

[0028] T=f(n,T env ,P,L)

[0029] Where T is the gearbox temperature, n is the speed, T env is the ambient temperature, P is the power, and L is the lubrication state parameter.

[0030]

[0031] In the formula, a ijkl are coefficients determined by experiments or theoretical analysis, and m, n, p, and q are orders determined according to the complexity of the model.

[0032] Further, the solution equation based on the operating parameters of the gearbox and the relationship between the temperature and the operating parameters, obtaining the temperature prediction value of the gearbox under different working conditions and selecting the maximum temperature prediction value therefrom includes:

[0033] Substitute the operating parameters of the gearbox under different working conditions obtained by formula (3) into the equation for solving the relationship between temperature and operating parameters, solve the equation, obtain the temperature prediction values ​​of the gearbox under different working conditions, and select the highest predicted temperature as the maximum predicted temperature.

[0034] Furthermore, the temperature warning threshold calculation equation is:

[0035] T warn =T nom +k3σ

[0036] Among them, T warn is the warning temperature threshold, T nom is the mean value of normal working temperature, k3 is the safety factor, and σ is the standard deviation of normal working temperature.

[0037] Furthermore, the fault risk assessment equation is:

[0038]

[0039] In the formula, R is the fault risk level between 0 and 1, k4 is the correction factor, and T max is the maximum predicted temperature.

[0040] In a second aspect of the present invention, a computer-readable storage medium is provided, which includes instructions, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of the first aspects.

[0041] In a third aspect of the present invention, an electronic device is provided, comprising: at least one processor, a memory and an input-output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any one of the methods described in the first aspect.

[0042] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the fan gearbox temperature warning method described in the present invention can deeply analyze the internal heat transfer process of the gearbox by constructing a physical model and combining the heat conduction differential equation and the energy balance equation, and fully consider a variety of operating parameters, so as to obtain a more accurate solution equation of the relationship between temperature and operating parameters, and then accurately calculate the temperature prediction value under different working conditions, provide a reliable basis for temperature warning, improve the accuracy and reliability of warning, effectively prevent the failure of the fan gearbox due to abnormal temperature, and ensure the stable operation of the fan system.

[0043] Based on the maximum predicted temperature and temperature warning threshold calculation equation and the fault risk assessment equation, this method can not only determine the warning status of the gearbox in a timely manner, but also quantitatively evaluate the degree of fault risk, so that operation and maintenance personnel can understand the operating status of the gearbox more intuitively and take corresponding measures in advance, such as optimizing operating parameters and arranging maintenance plans, etc., to reduce maintenance costs, extend the service life of the gearbox, and improve the overall operating efficiency and economy of the wind turbine. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] One or more embodiments are exemplarily described by the figures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0045] Figure 1 A schematic flow chart of a model-based wind turbine gearbox temperature early warning method provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to facilitate the understanding of the present invention, the present invention is described in more detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that when an element is described as "connecting" another element, it can be directly on another element, or there can be one or more centered elements therebetween. The orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "upper end", "lower end", "top" and "bottom" used in this specification is based on the orientation or positional relationship shown in the accompanying drawings, only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0047] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0048] Combine the following Figure 1 , the model-based wind turbine gearbox temperature warning method 100 provided in the embodiment of the present application is described in detail through a specific embodiment.

[0049] Figure 1 : is a flow chart of a model-based wind turbine gearbox temperature early warning method provided by the present invention. A model-based wind turbine gearbox temperature early warning method provided by one embodiment of the present invention comprises:

[0050] Step 101, selecting a fan gearbox with a typical structure, installing temperature sensors and operating condition sensors at key positions of the gearbox, and constructing a physical model of the fan gearbox based on the above;

[0051] Step 102, obtaining operating parameters of the gearbox under different working conditions, including but not limited to speed, load, and lubrication state parameters;

[0052] Step 103, based on the heat conduction differential equation and energy balance equation of the wind turbine gearbox, obtain a solution equation for the relationship between the gearbox temperature and the operating parameters;

[0053] Step 104, based on the operating parameters of the gearbox and the equation for solving the relationship between the temperature and the operating parameters, obtain the temperature prediction values ​​of the gearbox under different working conditions and select the highest predicted temperature value as the maximum predicted temperature;

[0054] Step 105 , obtaining the warning state and the degree of fault risk of the gearbox based on the maximum predicted temperature and the temperature warning threshold calculation equation and the fault risk assessment equation.

[0055] It should be noted that this embodiment relates to a model-based wind turbine gearbox temperature early warning method, which first requires selecting a wind turbine gearbox with a typical structure and installing temperature sensors and operating condition sensors at key locations of the gearbox. The wind turbine gearbox here refers to a key mechanical component used to transmit power and increase speed in a wind turbine generator set, while the temperature sensor and operating condition sensor are devices used to monitor the operating status and temperature changes of the gearbox.

[0056] Specifically, the operating condition sensors include a speed sensor, a torque sensor and an oil pressure sensor, which are respectively installed at the input shaft of the gearbox, the main oil pipe of the lubrication system and appropriate positions of the gearbox body.

[0057] More specifically, these sensors are used to obtain the operating parameters of the gearbox under different working conditions, such as speed, load, lubrication status parameters, etc. For example, the speed sensor can measure the real-time speed of the gearbox, the torque sensor can measure the torque transmitted to the gearbox, and the oil pressure sensor can monitor the oil pressure of the lubrication system.

[0058] Preferably, the specific parameter settings of these sensors can be adjusted according to the specific model and operating conditions of the gearbox. For example, the speed sensor can be set to be able to measure the speed range of 0-1000 rpm, the torque sensor can be set to be able to measure the torque range of 0-10000 Nm, and the oil pressure sensor can be set to be able to measure the oil pressure range of 0-10 bar.

[0059] Furthermore, according to actual needs, wireless transmission or wired transmission can be selected to collect sensor data to achieve real-time monitoring of the wind turbine gearbox.

[0060] In some embodiments, the operating condition sensor includes a speed sensor, a torque sensor, and an oil pressure sensor, including the following three installation positions:

[0061] Install a speed sensor and a torque sensor on the input shaft of the gearbox;

[0062] Install an oil pressure sensor on the main oil pipe of the gearbox lubrication system;

[0063] Install the temperature sensor at the appropriate position of the gearbox housing.

[0064] It should be noted that this embodiment describes the specific installation position of the working condition sensor in a temperature warning method for a wind turbine gearbox. The working condition sensor includes a speed sensor, a torque sensor, and an oil pressure sensor, which are installed at different key parts of the gearbox to monitor its operating status. The speed sensor is used to measure the rotation speed of the gearbox, the torque sensor is used to measure the torque load of the gearbox, and the oil pressure sensor is used to monitor the oil pressure of the gearbox lubrication system.

[0065] Specifically, the speed sensor and torque sensor are installed at the input shaft of the gearbox to ensure that the speed and torque of the gearbox can be accurately measured when it receives the initial power from the wind turbine. The oil pressure sensor is installed at the main oil pipe of the gearbox lubrication system to monitor the pressure of the lubricating oil to ensure that the gearbox is properly lubricated. The temperature sensor is installed at the appropriate position of the gearbox housing to monitor the temperature changes inside the gearbox.

[0066] Furthermore, the specific parameter settings of these sensors should be determined according to the design and operating requirements of the gearbox. For example, the speed sensor may need to be able to measure the speed of 0-3600 rpm, the torque sensor may need to be able to measure the torque of 0-50000 Newton meters, and the oil pressure sensor may need to be able to measure the oil pressure of 0-15 bar.

[0067] More specifically, in addition to the above-mentioned installation positions, other installation schemes may also be considered. For example, in order to more accurately monitor the temperature distribution of the gear box, multiple temperature sensors may be installed at different locations of the gear box.

[0068] Furthermore, the installation position of the sensor can also be adjusted according to the specific structure and maintenance channel of the gearbox to facilitate installation and maintenance. In the selection of sensors, it is possible to consider using high-precision sensors to improve the accuracy of monitoring data, or using sensors with self-diagnosis functions so that timely alarms can be issued when the sensor fails. These improvements will help improve the reliability and effectiveness of the fan gearbox temperature early warning method.

[0069] In some embodiments, the operating parameters of the gearbox under different working conditions are obtained by the following formula:

[0070]

[0071] Q=k1×P+k2

[0072] Where P is power, n is speed, T is torque, Q is lubrication flow, and k1 and k2 are coefficients related to the gearbox structure.

[0073] It should be noted that this embodiment describes in detail how to obtain the operating parameters of the wind turbine gearbox under different working conditions. These parameters include speed, torque, power and lubrication flow, which are key indicators for evaluating the performance and health of the gearbox. Here, power refers to the energy output by the gearbox during operation, speed refers to the speed at which the gearbox rotates, torque refers to the rotational torque transmitted by the gearbox, and lubrication flow refers to the flow of lubricating oil supplied by the lubrication system to the gearbox.

[0074] Specifically, power can be calculated by multiplying speed and torque, that is,

[0075]

[0076] Where P is power, N is speed, and T is torque. The lubrication flow can be determined by another formula Q = Q1 × N + Q2, where Q is the lubrication flow, and Q1 and Q2 are coefficients related to the gearbox structure. These coefficients can be determined based on the design parameters and historical operating data of the gearbox to ensure that the calculated power and lubrication flow can accurately reflect the actual operating status of the gearbox.

[0077] Preferably, in order to improve the accuracy of parameter acquisition, high-precision sensors can be used to measure the speed and torque, and advanced data acquisition systems can be used to process these data. In addition, the values ​​of Q1 and Q2 can be dynamically adjusted through a real-time monitoring system to adapt to different working conditions and environmental changes.

[0078] Furthermore, as an alternative, a machine learning-based algorithm can also be used to predict the lubrication flow. This method can intelligently adjust the prediction model of the lubrication flow based on historical data and current operating parameters, thereby improving the adaptability and accuracy of the early warning system. Through these detailed operation steps and alternatives, the temperature early warning system of the fan gearbox can be ensured to be more accurate and reliable.

[0079] In some embodiments, the heat conduction differential equation of the fan gearbox is:

[0080]

[0081] Where ρ is the density of the gearbox material, c is the specific heat capacity, T is the temperature, t is the time, k is the thermal conductivity, and q is the internal heat source term (including friction heat, etc.).

[0082] The energy balance equation is:

[0083] Q un -Q out +Q gen =Q st

[0084] Among them, Qin is the input heat (such as friction heat), Q out To transfer heat (such as heat dissipation through the box), Q gen The heat generated internally (such as heat generated by lubricating oil stirring), Q st Store changes for heat.

[0085] It should be noted that this embodiment describes the key mathematical models in the wind turbine gearbox temperature warning method, namely the heat conduction differential equation and the energy balance equation. These equations are used to describe the heat transfer and energy conversion process inside the gearbox. The heat conduction differential equation is a mathematical expression used to describe how heat changes in the gearbox material over time and position, while the energy balance equation is an equation that describes the relationship between energy input, output, and storage inside the gearbox. The internal heat source term refers to the heat generated inside the gearbox due to factors such as friction.

[0086] Specifically, the heat conduction differential equation can be expressed as

[0087]

[0088] Where ρ is the density of the gearbox material, c is the specific heat capacity, T is the temperature, t is the time, k is the thermal conductivity, is the Laplace operator, representing the spatial gradient of temperature, and Q is the internal heat source term, including frictional heat, etc.

[0089] Furthermore, the energy balance equation can be expressed as

[0090]

[0091] in is the input heat, is the heat transferred, is the internally generated heat, The specific settings of these parameters need to be determined according to the material properties of the gearbox and the actual operating conditions.

[0092] Preferably, in order to more accurately simulate the heat conduction and energy balance of the gearbox, numerical methods such as finite element analysis can be used to solve these equations. In addition, the model parameters such as thermal conductivity k, specific heat capacity c and specific values ​​of internal heat source term Q can be calibrated in combination with experimental data.

[0093] More specifically, different thermal conductivity and specific heat values ​​can be used for different parts of the gearbox to reflect changes in material properties. As an alternative, machine learning techniques can also be used to train models to predict the temperature distribution of the gearbox, which can reduce reliance on physical models and may provide faster prediction results. Through these refined operating steps and alternatives, the accuracy and practicality of the temperature warning method can be improved.

[0094] In some embodiments, the equation for the relationship between gearbox temperature and operating parameters is:

[0095] T=f(n,T env ,P,L)

[0096] Where T is the gearbox temperature, n is the speed, T env is the ambient temperature, P is the power, and L is the lubrication state parameter (such as oil film thickness, oil quality and other related parameters).

[0097]

[0098] In the formula, a ijkl are coefficients determined by experiments or theoretical analysis, and m, n, p, and q are orders determined according to the complexity of the model.

[0099] It should be noted that this embodiment describes in detail how to solve the temperature of the gearbox based on the operating parameters of the gearbox and the relationship between the temperature and the operating parameters. The gearbox temperature here refers to the actual temperature of the gearbox during operation, and the operating parameters include speed, ambient temperature, power and lubrication state parameters. The lubrication state parameters involve the physical and chemical properties of the lubricating oil, such as oil film thickness and oil quality, which are crucial to the thermal management and performance of the gearbox.

[0100] Specifically, the relationship between the gearbox temperature and the operating parameters can be described by a function, namely

[0101] T=f(N,T env ,P,λ)

[0102] Where T represents the gearbox temperature, N represents the speed, T env represents ambient temperature, P represents power, and λ represents lubrication state parameter.

[0103] More specifically, this function can be determined by experimental data fitting or theoretical analysis, in which the parameters such as a, b, c, d are coefficients determined by experiments or theoretical analysis, while m, n, p, q are orders determined according to the complexity of the model. The specific settings of these parameters need to be adjusted according to the specific design and operating conditions of the gearbox to ensure the accuracy and applicability of the model.

[0104] Preferably, in order to improve the prediction accuracy of the model, nonlinear regression analysis can be used to determine the coefficients and orders in the function. In addition, the differences in the temperature effects of different stages such as the start-up, operation and stop of the gearbox can be considered, and different temperature prediction models can be established respectively. More specifically, a real-time data feedback mechanism can be introduced to dynamically adjust the model parameters to adapt to changes in the operating conditions of the gearbox.

[0105] Furthermore, as an alternative, we can also explore the use of artificial intelligence technology, such as neural networks, to build a more complex temperature prediction model that can handle more variables and nonlinear relationships, thereby providing more accurate temperature predictions. Through these refined operation steps and alternatives, the adaptability and accuracy of the temperature early warning system can be enhanced.

[0106] In some embodiments, the step of obtaining the predicted temperature values ​​of the gearbox under different working conditions and selecting the maximum predicted temperature value based on the operating parameters of the gearbox and the solving equation of the relationship between the temperature and the operating parameters comprises:

[0107] Substitute the operating parameters of the gearbox under different working conditions obtained by formula (3) into the equation for solving the relationship between temperature and operating parameters, solve the equation, obtain the temperature prediction values ​​of the gearbox under different working conditions, and select the highest predicted temperature as the maximum predicted temperature.

[0108] It should be noted that this embodiment describes how to use the operating parameters of the gearbox and the equation for solving the relationship between temperature and operating parameters to obtain the temperature prediction value of the gearbox under different working conditions, and select the highest predicted temperature value as the maximum predicted temperature. The temperature prediction value here refers to the future temperature of the gearbox predicted by the mathematical model based on the current and historical operating data. The maximum predicted temperature refers to the highest temperature among all the predicted values, which is used to evaluate the maximum temperature risk that the gearbox may face.

[0109] Specifically, firstly, the operating parameters of the gearbox under different working conditions obtained by formula (3) are substituted into the equation for solving the relationship between temperature and operating parameters. This substitution process involves specific mathematical operations, where the operating parameters include speed, ambient temperature, power, and lubrication state parameters.

[0110] Furthermore, the specific values ​​of these parameters need to be set according to the actual monitored data. For example, the speed may be hundreds to thousands of revolutions per minute, the ambient temperature may be from dozens of degrees below zero to dozens of degrees above zero, the power may be at the kilowatt level, and the lubrication state parameters involve the viscosity, temperature and pressure of the lubricating oil, etc. By substituting these parameters, the temperature prediction value of the gearbox under different working conditions can be solved.

[0111] Preferably, in order to improve the accuracy of temperature prediction, numerical simulation software can be used to assist in solving the equations, and these software can handle complex nonlinear equations.

[0112] More specifically, an automated system may be provided that is capable of monitoring the operating parameters of the gearbox in real time and automatically inputting these parameters into a temperature prediction model, thereby achieving real-time temperature prediction.

[0113] Furthermore, as an alternative, machine learning technology can also be used to train models to predict the temperature changes of the gearbox. This method can use historical data to improve the accuracy of predictions and the speed of response. Through these detailed operation steps and alternatives, the temperature warning system of the gearbox can be ensured to be more efficient and reliable.

[0114] In some embodiments, the temperature warning threshold calculation equation is:

[0115] T warn =T nom +k3σ

[0116] Among them, T warn is the warning temperature threshold, T nom is the mean value of normal working temperature, k3 is the safety factor, and σ is the standard deviation of normal working temperature.

[0117] The first paragraph: This embodiment relates to how to determine the temperature warning threshold of the fan gearbox. The temperature warning threshold refers to a temperature value set in the warning system. When the actual temperature of the gearbox exceeds this value, the system will issue a warning signal. The warning temperature threshold here is a key parameter to ensure the safe operation of the gearbox, and the normal operating temperature mean refers to the average temperature of the gearbox under normal operating conditions. The safety factor is a multiplier factor used to increase the warning threshold to ensure safety.

[0118] Specifically, the warning temperature threshold can be expressed by the formula

[0119] T 预警 =T 均值 +σ·k3

[0120] To calculate, where T 预警 is the warning temperature threshold, T 均值 is the mean of the normal operating temperature, σ is the standard deviation of the normal operating temperature, and k3 is a safety factor.

[0121] Furthermore, this safety factor can be set according to the design standard of the gearbox and historical fault data to ensure timely warning when the gearbox temperature rises abnormally. The mean and standard deviation of the normal operating temperature can be obtained by long-term monitoring of the gearbox operation data.

[0122] Preferably, in order to more accurately set the warning temperature threshold, a statistical analysis method may be used to determine the mean and standard deviation of the normal working temperature.

[0123] More specifically, the gearbox temperature data under different seasons and load conditions can be analyzed to determine a more accurate mean and standard deviation.

[0124] Furthermore, as an alternative, machine learning technology can also be used to predict the warning temperature threshold. This method can dynamically adjust the warning threshold based on historical fault data and real-time operation data to adapt to different operating conditions and environmental changes. Through these detailed operation steps and alternatives, the accuracy and response speed of the temperature warning system can be improved.

[0125] In some embodiments, the failure risk assessment equation is:

[0126]

[0127] In the formula, R is the fault risk level between 0 and 1, k4 is the correction factor, and T max is the maximum predicted temperature.

[0128] It should be noted that this embodiment describes how to assess the failure risk level of a wind turbine gearbox. Failure risk assessment is a key step used to determine the likelihood of a gearbox failure under specific conditions. Here, the failure risk level is a value between 0 and 1, indicating the probability of a gearbox failure, and the correction factor is a parameter used to adjust the failure risk assessment result to reflect the complexity of actual operation.

[0129] Specifically, the degree of failure risk can be expressed by the formula

[0130]

[0131] To calculate, where R represents the degree of failure risk, α is the correction factor, T 预测 is the maximum predicted temperature, T 预警 is the warning temperature threshold, T 正常 is the normal operating temperature. The correction factor α can be set based on historical failure data and expert experience to ensure the accuracy of the evaluation results. 预测 、T 预警 and T 正常 The value needs to be obtained based on actual monitoring and calculation.

[0132] Preferably, in order to improve the accuracy of fault risk assessment, a historical fault database may be used to assist in determining the correction coefficient α. More specifically, the value of α may be adjusted by analyzing the temperature data and actual fault conditions in historical fault cases to make it closer to reality.

[0133] Furthermore, as an alternative, machine learning algorithms can be used to dynamically adjust the fault risk assessment model. This method can predict fault risks based on real-time data and historical trends, thereby providing more timely warnings. Through these detailed operation steps and alternatives, the reliability and practicality of fault risk assessment can be enhanced.

[0134] The above-mentioned embodiments of the present invention have the following beneficial effects: the fan gearbox temperature warning method described in the present invention can construct a physical model of the fan gearbox, install multiple sensors at key positions to obtain operating parameters, and establish a solution equation for the relationship between temperature and operating parameters based on the heat conduction differential equation and the energy balance equation, so as to accurately obtain the temperature prediction value under different working conditions, and finally combine the warning threshold calculation equation and the fault risk assessment equation to accurately judge the warning status of the gearbox and evaluate the degree of fault risk, effectively prevent faults caused by abnormal temperature, ensure the safe and stable operation of the fan gearbox, and also provide strong guarantee for the overall reliable operation of the fan.

[0135] This method can comprehensively consider multiple factors, such as operating parameters such as speed, load, lubrication status, and external factors such as ambient temperature. The established model is more in line with actual working conditions, making temperature prediction more accurate and able to issue early warnings in a timely manner, so that operation and maintenance personnel can take measures in advance, such as adjusting operating parameters, arranging maintenance and overhaul, etc., to reduce downtime caused by faults, improve wind turbine operating efficiency, reduce maintenance costs, extend the service life of wind turbine gearboxes, and improve the economy and stability of the entire wind power generation system.

[0136] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods 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, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as above, which are not provided in detail for the sake of simplicity. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model-based wind turbine gearbox temperature early warning method, characterized in that: The steps include: A fan gearbox with a typical structure was selected, and temperature sensors and operating condition sensors were installed at key locations of the gearbox, based on which a physical model of the fan gearbox was constructed. Obtain the operating parameters of the gearbox under different working conditions, including but not limited to speed, load, and lubrication status parameters; Based on the heat conduction differential equation and energy balance equation of the fan gearbox, the solution equation of the relationship between the gearbox temperature and the operating parameters is obtained; Based on the operating parameters of the gearbox and the solved equation of the relationship between temperature and the operating parameters, the temperature prediction values ​​of the gearbox under different working conditions are obtained and the highest predicted temperature value is selected as the maximum predicted temperature; The warning state and the degree of fault risk of the gearbox are obtained based on the maximum predicted temperature and the temperature warning threshold calculation equation and the fault risk assessment equation.

2. A model-based wind turbine gearbox temperature early warning method according to claim 1, characterized in that: The operating condition sensors include a speed sensor, a torque sensor and an oil pressure sensor, and include the following three installation positions: Install a speed sensor and a torque sensor on the input shaft of the gearbox; Install an oil pressure sensor on the main oil pipe of the gearbox lubrication system; Install the temperature sensor at the appropriate position of the gearbox housing.

3. A model-based wind turbine gearbox temperature early warning method according to claim 2, characterized in that: The operating parameters of the gearbox under different working conditions are obtained through the following formula: Q=k1×P+k2 Where P is power, n is speed, T is torque, Q is lubrication flow, and k1 and k2 are coefficients related to the gearbox structure.

4. A model-based wind turbine gearbox temperature early warning method according to claim 3, characterized in that: The heat conduction differential equation of the fan gearbox is: Where ρ is the gearbox material density, c is the specific heat capacity, T is the temperature, t is the time, k is the thermal conductivity, and q is the internal heat source term. The energy balance equation is: Q in -Q out +Q gen =Q st Among them, Q in is the input heat, Q out is the heat transferred, Q gen The internal heat generated, Q st Store changes for heat.

5. A model-based wind turbine gearbox temperature early warning method according to claim 4, characterized in that: The equation for solving the relationship between gearbox temperature and operating parameters is: T=f(n,T env ,P,L) Where T is the gearbox temperature, n is the speed, T env is the ambient temperature, P is the power, and L is the lubrication state parameter; In the formula, a ij kl is a coefficient determined by experiments or theoretical analysis, and m, n, p, and q are orders determined according to the complexity of the model.

6. A model-based wind turbine gearbox temperature early warning method according to claim 5, characterized in that: The solution equation based on the operating parameters of the gearbox and the relationship between the temperature and the operating parameters, obtaining the temperature prediction values ​​of the gearbox under different working conditions and selecting the maximum temperature prediction value therefrom includes: Substitute the operating parameters of the gearbox under different working conditions obtained by formula (3) into the equation for solving the relationship between temperature and operating parameters, solve the equation, obtain the temperature prediction values ​​of the gearbox under different working conditions, and select the highest predicted temperature as the maximum predicted temperature.

7. A model-based wind turbine gearbox temperature early warning method according to claim 6, characterized in that: The temperature warning threshold calculation equation is: T warn =T nom +k3σ Among them, T warn is the warning temperature threshold, T nom is the mean value of normal working temperature, k3 is the safety factor, and σ is the standard deviation of normal working temperature.

8. The model-based wind turbine gearbox temperature early warning method according to claim 6, characterized in that: The fault risk assessment equation is: Where R is the failure risk level, which is between 0 and 1, k4 is the correction factor, and T max is the maximum predicted temperature.

9. A readable storage medium, characterized in that: The storage medium is used to store a computer program. When the processor executes the computer program, the steps of the model-based wind turbine gearbox temperature early warning method according to any one of claims 1 to 8 are implemented.

10. A model-based wind turbine gearbox temperature early warning device, characterized in that: The equipment includes: Memory for storing computer programs; A processor, configured to implement the steps of the model-based wind turbine gearbox temperature early warning method as described in any one of claims 1 to 8 when executing a computer program.

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