Wind turbine generator fault early warning method combined with load analysis

Abstract

The invention discloses a wind turbine generator fault early warning method combined with load analysis, and relates to the technical field of wind turbine generator monitoring, and the method comprises the steps: collecting the real-time wind power data and unit structure parameters of a target wind turbine generator; acquiring wind direction change characteristics of the target wind turbine generator; establishing a load calculation model, and obtaining a load analysis result of the target wind turbine generator; constructing a fault early warning model; and generating a fault early warning signal. The technical problems that in existing wind turbine generator set fault early warning, it is difficult to accurately capture and evaluate the influence of the wind direction on the set load, then fault early warning lags behind, the accuracy is limited, and the reliability and efficiency of a wind turbine generator set are insufficient are solved. The technical effects of improving the accuracy and timeliness of fault early warning and enhancing the operation stability and reliability of the unit are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to wind turbine monitoring, and in particular to a wind turbine fault early warning method combined with load analysis. Background Art

[0002] With the growing global demand for renewable energy, wind power generation, as a clean and renewable energy form, has become increasingly important. As the core equipment of wind power generation, the operating stability and reliability of wind turbines are directly related to the power generation efficiency and economic benefits of the entire wind farm. However, wind turbines are exposed to complex and changeable natural environments for a long time, and are subjected to various loads, especially wind direction changes. Traditional wind turbine fault warning methods mainly rely on real-time monitoring of unit operating parameters and statistical analysis of fault history data. Although they can identify the abnormal state of the unit to a certain extent, there are often problems such as early warning lag and insufficient accuracy. Especially in the face of complex and changeable wind direction changes, it is difficult to accurately capture the stress changes and potential failure risks inside the unit structure, which in turn affects the stability and life of the wind turbine.

[0003] At present, there is a technical problem in the relevant technologies of wind turbine fault warning that it is difficult to accurately capture and evaluate the impact of wind direction on the unit load, which leads to delayed fault warning and limited accuracy, resulting in insufficient reliability and efficiency of wind turbines. Summary of the invention

[0004] The present application provides a wind turbine fault warning method combined with load analysis, thereby solving the technical problem that it is difficult to accurately capture and evaluate the impact of wind direction on the load of the unit in the existing wind turbine fault warning, which leads to delayed fault warning and limited accuracy, resulting in insufficient reliability and efficiency of the wind turbine. The application achieves the technical effect of improving the accuracy and timeliness of fault warning and enhancing the operating stability and reliability of the unit.

[0005] The present application provides a wind turbine fault warning method combined with load analysis, including: collecting real-time wind data and unit structural parameters of a target wind turbine; analyzing the influence of wind direction on the load of the target wind turbine based on the real-time wind data, and obtaining the wind direction change characteristics of the target wind turbine; establishing a load calculation model according to the unit structural parameters and the wind direction change characteristics, and obtaining the load analysis results of the target wind turbine; constructing a fault warning model; if there is an abnormal load change in the load analysis result, generating a fault warning signal according to the fault warning model.

[0006] In a possible implementation, the analysis of the impact of wind direction on the load of the target wind turbine also performs the following processing: using a wind direction sensor to collect wind direction data at the location of the target wind turbine in real time; dynamically analyzing the wind direction data to obtain the wind direction change characteristics, wherein the wind direction change characteristics include wind direction fluctuation amplitude, wind direction change frequency and wind direction change rate; and identifying load changes when wind direction changes based on the wind direction change characteristics.

[0007] In a possible implementation, the wind turbine fault warning method combined with load analysis also performs the following processing: collecting multiple wind speed data at different heights of the target wind turbine through a wind speed sensor to obtain a wind speed gradient; and correcting the wind direction change characteristics according to the wind speed gradient.

[0008] In a possible implementation, the wind turbine fault warning method combined with load analysis also performs the following processing: collecting ambient temperature data and ambient humidity data of the target wind turbine; establishing an environmental impact factor of the target wind turbine based on the ambient temperature data and the ambient humidity data; optimizing the load calculation model according to the environmental impact factor, and adjusting the load analysis result.

[0009] In a possible implementation, the wind turbine fault warning method combined with load analysis also performs the following processing: obtaining the real-time power output parameters of the target wind turbine; correlating the real-time power output parameters with the load analysis results to evaluate the operating efficiency of the target wind turbine; if the operating efficiency of the target wind turbine is reduced, generating an operation and maintenance warning signal.

[0010] In a possible implementation, the construction of the fault warning model also performs the following processing: the key components of the target wind turbine include blades, towers, and main shafts; the historical fault records of the target wind turbine are obtained to determine blade fault data samples, tower fault data samples, and main shaft fault data samples; the first fault warning sub-model, the second fault warning sub-model, and the third fault warning sub-model are constructed according to the blade fault data samples, the tower fault data samples, and the main shaft fault data samples; the fault warning model is synthesized with the first fault warning sub-model, the second fault warning sub-model, and the third fault warning sub-model.

[0011] In a possible implementation, a first fault warning sub-model, a second fault warning sub-model and a third fault warning sub-model are respectively constructed according to the blade fault data samples, the tower fault data samples and the main shaft fault data samples, and the following processing is also performed: blade load data samples and blade fault data samples are extracted from the blade fault data samples; the blade load data samples are used as training inputs and the blade fault data samples are used as training outputs, and the first fault warning sub-model is obtained by training based on a neural network model; similarly, the second fault warning sub-model and the third fault warning sub-model are obtained by training.

[0012] In a possible implementation, if there is an abnormal load change in the load analysis result, a fault warning signal is generated according to the fault warning model, and the following processing is also performed: if the blade load of the target wind turbine group exceeds the first load threshold, the first fault warning model generates a first fault warning signal; if the tower load of the target wind turbine group exceeds the second load threshold, the second fault warning model generates a second fault warning signal; if the main shaft load of the target wind turbine group exceeds the third load threshold, the third fault warning model generates a third fault warning signal.

[0013] The present application proposes a wind turbine fault warning method combined with load analysis to collect real-time wind data and unit structural parameters of the target wind turbine; obtain the wind direction change characteristics of the target wind turbine; establish a load calculation model to obtain the load analysis results of the target wind turbine; construct a fault warning model; and generate a fault warning signal. This solves the technical problem that the existing wind turbine fault warning is difficult to accurately capture and evaluate the impact of wind direction on the unit load, which leads to delayed fault warning and limited accuracy, resulting in insufficient reliability and efficiency of the wind turbine, and achieves the technical effect of improving the accuracy and timeliness of fault warning and enhancing the operational stability and reliability of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a flow chart of a wind turbine fault warning method combined with load analysis provided in an embodiment of the present application;

[0016] Figure 2A schematic diagram of a flow chart of analyzing load influence in a wind turbine fault warning method combined with load analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application embodiment provides a wind turbine fault warning method combined with load analysis, such as Figure 1 As shown, the method includes:

[0021] Step S100, collecting real-time wind power data and unit structural parameters of the target wind turbine unit.

[0022] Preferably, the real-time wind data and unit structural parameters of the target wind turbine are obtained. The real-time wind data refers to the relevant data of the wind environment in which the wind turbine is located during operation, mainly including wind speed data and wind direction data, wherein the wind speed directly affects the power generation efficiency and load state of the wind turbine, and the change of wind direction will cause the change of the force direction of the wind turbine, thereby affecting its load distribution; the unit structural parameters refer to the physical characteristics and structural dimensions of the wind turbine itself, including the diameter of the wind rotor, the outer diameter of the wind rotor when rotating, which determines the size of the wind rotor swept area and the length of the blades; the number of blades, the number of blades on the wind rotor, and different numbers of blades have different effects on the wind energy capture efficiency and load distribution; the length, material and shape of the blade, the length of the blade determines its swept area, the choice of material affects the weight, strength and durability of the blade, and the shape determines the aerodynamic performance of the blade; the tower height, the height of the tower determines the distance of the wind turbine from the ground, thereby affecting its wind receiving area and load state; the transmission system parameters such as the parameters of the gearbox, generator and other components determine the transmission efficiency and load transfer characteristics of the wind turbine.

[0023] Step S200: analyzing the influence of wind direction on the load of a target wind turbine generator set based on the real-time wind power data, and obtaining wind direction change characteristics of the target wind turbine generator set.

[0024] Preferably, wind speed data and wind direction data of the area where the target wind turbine is located are obtained in real time through sensors installed on the wind turbine, and these data are deeply analyzed to understand the changing pattern of wind direction and its impact on the load of the wind turbine, including analyzing the frequency distribution, duration, change speed and other characteristics of the wind direction and how these characteristics affect the stress state of the wind turbine. Specifically, the change of wind direction will directly affect the stress state of the wind turbine. When the wind direction changes, the blades of the wind turbine will be subjected to forces and torques in different directions, thereby causing changes in the overall load of the unit. On the basis of in-depth analysis of real-time wind data and load influence, the wind direction change characteristics of the target wind turbine are further obtained, including the dominant wind direction, that is, the wind direction with the highest frequency within a certain period of time, the wind direction change range, that is, the range of wind direction fluctuations within a certain period of time. The larger the wind direction change range, the greater the load fluctuation on the wind turbine may be, and the wind direction change speed, that is, the speed of wind direction change in a short period of time. Rapid wind direction changes may cause a sharp change in the stress state of the wind turbine, increasing the risk of damage to the unit.

[0025] like Figure 2As shown, step S200 further includes step S210, using a wind direction sensor to collect wind direction data of the target wind turbine in real time; step S220, dynamically analyzing the wind direction data to obtain the wind direction change characteristics, wherein the wind direction change characteristics include wind direction fluctuation amplitude, wind direction change frequency and wind direction change rate; step S230, identifying load changes when wind direction changes based on the wind direction change characteristics.

[0026] Preferably, a wind direction sensor is a device for measuring wind direction, which can capture and convert wind direction information into electrical signals or digital data in real time. In the application scenario of a wind turbine, the wind direction sensor is usually installed at an appropriate position of the wind turbine to collect wind direction data at the location of the turbine in real time. The collected wind direction data needs to be dynamically analyzed to extract the changing characteristics of the wind direction, including the wind direction fluctuation amplitude, wind direction change frequency and wind direction change rate. Specifically, the wind direction fluctuation amplitude refers to the maximum degree to which the wind direction deviates from the average wind direction within a certain period of time, reflecting the stability or volatility of the wind direction. The wind direction change frequency refers to the number of times the wind direction changes within a certain period of time, reflecting the speed of the wind direction change. The wind direction change rate refers to the amount of change in wind direction per unit time, reflecting the speed and volatility of the wind direction change. Trend. Through dynamic analysis of wind direction data, these wind direction change characteristics can be obtained to provide a basis for subsequent identification of load changes; then, based on the wind direction change characteristics, the load changes when the wind direction changes are identified. Specifically, the load changes of wind turbines are closely related to the wind direction changes. When the wind direction changes, the blades, towers and other key components of the wind turbine will be subject to different wind forces and wind pressures, thereby causing load changes. Based on the wind direction change characteristics, the load changes when the wind direction changes can be further identified, specifically including establishing a mathematical model between wind direction changes and load changes according to the structural characteristics and historical data of the wind turbine. While monitoring the wind direction data in real time, the load data of the wind turbine is also monitored in real time. The wind direction change characteristics are compared and analyzed with the load data to identify the load change law when the wind direction changes.

[0027] Step S220 further includes step S221, collecting multiple wind speed data at different heights of the target wind turbine group through a wind speed sensor to obtain a wind speed gradient; step S222, correcting the wind direction change characteristic according to the wind speed gradient.

[0028] Preferably, wind speed sensors (such as gradient anemometers) are installed at different heights of the target wind turbine group, and multiple wind speed data at each height are collected in real time. Specifically, the wind speed sensor is usually installed on the tower of the wind turbine group or in the surrounding environment to measure the wind speed at different heights. By collecting wind speed data at multiple different heights, a gradient curve of wind speed changing with height (i.e., wind speed gradient) can be constructed, which reflects the distribution and change trend of wind speed at different heights of the target wind turbine group. In actual operation, the wind direction change characteristics may be affected by many factors, including the influence of wind speed gradient, and then according to the wind speed The wind direction change characteristics are corrected by the wind speed gradient to more accurately describe and predict the wind direction change. Specifically, the wind speed data and wind direction data at different heights are integrated to form a complete data set. The correlation between the wind speed gradient and the wind direction change characteristics is explored through statistical analysis methods (such as calculating the correlation coefficient, performing regression analysis, etc.). Based on the results of the correlation analysis, a mathematical model is constructed to describe the influence of the wind speed gradient on the wind direction change characteristics. Using the constructed model, the wind direction change characteristics are corrected and calculated according to the real-time collected wind speed gradient data, including the adjustment of the wind direction fluctuation amplitude, wind direction change frequency and wind direction change rate. The wind speed gradient is obtained by collecting multiple wind speed data through the wind speed sensor, and the wind direction change characteristics are corrected according to the wind speed gradient, which provides more powerful support for the operation and maintenance of wind turbines, thereby improving the operating efficiency and safety of wind turbines and reducing the risk of failure and operation and maintenance costs.

[0029] Step S300: establishing a load calculation model according to the unit structural parameters and the wind direction change characteristics, and obtaining the load analysis result of the target wind turbine unit.

[0030] Preferably, a load calculation model is established based on the collected unit structural parameters and the determined wind direction change characteristics. Specifically, the wind force acting on the wind turbine is calculated according to the wind direction and wind speed, including tangential force and normal force, and the structural response of the wind turbine, such as displacement, strain and stress, is calculated according to the wind force and the unit structural parameters. At the same time, the response of the target wind turbine under dynamic wind load, such as vibration and fatigue, is considered. Based on these forces, a load calculation model is established using the momentum-blade element theory, and then the load calculation and analysis of the target wind turbine is performed. Specifically, the unit structural parameters and wind direction change characteristics are input into the model, and the load state of the wind turbine under different wind directions and wind speeds is calculated according to the model. The calculation results are analyzed and evaluated to understand the stress state and potential risks of the wind turbine under different working conditions, and the load analysis results of the target wind turbine are output.

[0031] Step S300 further includes step S310, collecting ambient temperature data and ambient humidity data of the target wind turbine group; step S320, establishing the environmental impact factor of the target wind turbine group based on the ambient temperature data and the ambient humidity data; step S330, optimizing the load calculation model according to the environmental impact factor and adjusting the load analysis result.

[0032] Preferably, temperature and humidity sensors are installed around the target wind turbine or at a specific location to collect data on ambient temperature and humidity in real time, because ambient temperature and humidity are important factors affecting the performance and load of the wind turbine. These ambient temperature and humidity data are usually transmitted and recorded through a sensor network, and then based on the collected ambient temperature and humidity data, an environmental impact factor is established, such as through statistical analysis, machine learning or physical modeling. The environmental impact factor is a comprehensive indicator that describes the impact of environmental factors (such as temperature, humidity, etc.) on the performance and load of the wind turbine. Specifically, the collected ambient temperature and humidity data are preprocessed, such as cleaning, denoising and normalization, to ensure the accuracy and consistency of the data, and features that can reflect changes in ambient temperature and humidity are extracted from the preprocessed data, such as average Mean, standard deviation, maximum value, minimum value, etc. Based on the extracted features, a mathematical model of environmental influencing factors is constructed. The accuracy and reliability of the model are verified by comparing with actual observation data or historical data. Then, the load calculation model is optimized according to the established environmental influencing factors to more accurately reflect the impact of ambient temperature and humidity on the load. Specifically, it may include adjusting the relevant parameters in the load calculation model according to the changes in environmental influencing factors to reflect the direct impact of environmental factors on the load; improving the structure of the load calculation model to better adapt to the changes in ambient temperature and humidity, for example, new variables can be introduced or the complexity of the model can be adjusted; finally, the optimized load calculation model is used to adjust the original load analysis results. This process may include re-evaluation of load distribution, load peak value and load change trend. Through this optimization process, more accurate and reliable load analysis results can be obtained, providing more powerful and accurate support for the operation and maintenance of wind turbines. At the same time, it also helps to reduce the risk of failure and operation and maintenance costs, and improve the operation efficiency and safety of wind turbines.

[0033] Step S330 further includes step S331, obtaining the real-time power output parameters of the target wind turbine group; step S332, correlating the real-time power output parameters with the load analysis results to evaluate the operating efficiency of the target wind turbine group; step S333, if the operating efficiency of the target wind turbine group is reduced, generating an operation and maintenance warning signal.

[0034] Preferably, the real-time power output parameter is an important indicator for measuring the operating status and power generation efficiency of the wind turbine. Corresponding sensors or data acquisition devices are installed in the control system of the target wind turbine to obtain the real-time power output parameters. These devices can monitor the power output of the wind turbine in real time and convert it into digital signals for recording and analysis. The real-time power output parameters usually include instantaneous power, average power, power fluctuation, etc., which can reflect the power generation capacity and stability of the wind turbine in different time periods; then the real-time power output parameters are correlated with the load analysis results (including key parameters such as blade load, tower load, main shaft load, etc.) to evaluate the operating efficiency of the target wind turbine. Specifically, ensure that the real-time power output parameters and the load analysis results are consistent in time so as to perform accurate comparative analysis. Through statistical analysis methods, the correlation between the real-time power output parameters and the load analysis results is explored. Based on the results of the correlation analysis, The results are used to evaluate the operating efficiency of the target wind turbine. If there is a positive correlation between the real-time power output parameters and the load analysis results, and the power output is stable and high, then the operating efficiency of the wind turbine can be considered to be high. On the contrary, if the power output fluctuates greatly or decreases, and the load analysis results also show abnormal stress conditions, it may mean that the operating efficiency of the wind turbine is reduced. If the operating efficiency of the target wind turbine is reduced after correlation analysis, an operation and maintenance warning signal is generated to remind the operation and maintenance personnel to take timely measures. Among them, the content of the operation and maintenance warning signal usually includes the warning level. According to the degree of reduction in operating efficiency and the size of potential risks, the level of the warning signal (such as low, medium, high, etc.) is determined; the warning reason briefly describes the possible reasons for the reduction in operating efficiency, such as blade wear, tower loosening, main shaft failure, etc.; recommended measures provide recommended measures for the warning reasons, such as shutdown inspection, replacement of components, adjustment of parameters, etc. Through this process, the operation and maintenance personnel can timely discover and solve problems in the operation of wind turbines to ensure the stable operation and efficient power generation of the units. At the same time, it also helps to reduce the risk of failure and operation and maintenance costs, and improve the overall economic benefits of wind farms.

[0035] Step S400, constructing a fault warning model. Further, step S400 also includes step S410, the key components of the target wind turbine include blades, towers, and main shafts; step S420, obtaining historical fault records of the target wind turbine, determining blade fault data samples, tower fault data samples, and main shaft fault data samples; step S430, constructing a first fault warning sub-model, a second fault warning sub-model, and a third fault warning sub-model according to the blade fault data samples, tower fault data samples, and main shaft fault data samples; step S440, synthesizing the fault warning model with the first fault warning sub-model, the second fault warning sub-model, and the third fault warning sub-model.

[0036] Preferably, a fault warning model for the target wind turbine is constructed, that is, historical fault data is used to predict and prevent possible future faults. Specifically, the target wind turbine and its key components, including blades, towers and main shafts, are clearly identified. They are the most important components of the wind turbine, and their performance and status directly affect the overall operating efficiency and safety of the wind turbine. The historical fault records of the target wind turbine are collected, including information such as the time, location, component type, fault phenomenon, cause of the fault, and maintenance measures. After the historical fault records are collected, blade fault data samples, tower fault data samples, and main shaft fault data samples need to be determined according to the fault conditions of the blades, towers, and main shafts, respectively; according to the determined The fault data samples are used to construct the first fault warning sub-model (for blades), the second fault warning sub-model (for towers) and the third fault warning sub-model (for main shafts). These fault warning sub-models are constructed and trained using machine learning algorithms (such as support vector machines, neural networks, decision trees, etc.). Specifically, when constructing the model, the fault data samples are divided into training sets and test sets. The training set is used to train the model so that it can learn the laws and characteristics of fault occurrence; the test set is used to evaluate the performance of the model to ensure that it has sufficient accuracy and reliability in practical applications; after constructing the fault warning sub-models of the three key components, they are integrated to obtain a comprehensive fault warning model. The fault warning model is applied to the actual operation of the target wind turbine. By real-time monitoring and analyzing the status data of key components, the occurrence of potential faults can be predicted and warning signals can be issued in advance.

[0037] Step S430 further includes step S431, extracting blade load data samples and blade fault data samples from the blade fault data samples; step S432, using the blade load data samples as training input and the blade fault data samples as training output, based on the neural network model, training to obtain the first fault warning sub-model; step S433, similarly, training to obtain the second fault warning sub-model and the third fault warning sub-model.

[0038] Preferably, blade load data samples and blade fault data samples are extracted from blade fault data samples, and a first fault warning sub-model (for blades) is trained based on these samples, and a second fault warning sub-model (for towers) and a third fault warning sub-model (for main shafts) are similarly trained. Specifically, blade-related fault data are screened out from the historical fault records of the target wind turbine to form blade fault data samples, which include specific information of the blade fault, such as fault type, fault occurrence time, fault location, etc.; corresponding data related to blade load are extracted to form blade load data samples, which include the load state of the blade under corresponding working conditions, such as tangential force, normal force, bending moment, torque, etc.; a warning model is constructed based on a neural network model (such as a multi-layer perceptron, a convolutional neural network, etc.), and the blade load data samples are used as training input and the blade fault data samples are used as training output to train the neural network model. After completion, another part of the blade fault data samples that did not participate in the training are used to verify the model to obtain a first fault warning sub-model for blade fault warning of the target wind turbine. Similarly, tower fault data samples are extracted from historical fault records, and data samples related to tower load are extracted from real-time wind data and unit structural parameters, and these data samples are used to train a second fault warning sub-model (for the tower); main shaft fault data samples are extracted from historical fault records, and data samples related to main shaft status are extracted from real-time wind data and unit structural parameters, and these data samples are used to train a third fault warning sub-model (for the main shaft).

[0039] Step S500: if the load analysis result shows an abnormal load change, a fault warning signal is generated according to the fault warning model.

[0040] Preferably, when there is an abnormal load change in the load analysis result, a fault warning signal is generated according to the fault warning model. Specifically, the load data of the target wind turbine is monitored in real time, including monitoring the load status of key components such as blades, towers, and main shafts of the target wind turbine under different working conditions. According to the preset load threshold, it is identified whether there is an abnormal load change, such as a sudden increase, decrease, or fluctuation in the load. The identified abnormal load change is then used as input data and input into the previously trained fault warning model. The fault warning model will perform fault prediction based on the input abnormal load change data, such as predicting the type of target wind turbine fault, the time of occurrence, the possible impact, etc. If the model predicts the existence of a potential failure risk, a fault warning signal will be automatically generated, which will help operation and maintenance personnel to promptly discover and deal with potential failure risks and ensure the stable operation and safety of the wind turbine.

[0041] Step S500 further includes step S510, if the blade load of the target wind turbine group exceeds the first load threshold, the first fault warning model generates a first fault warning signal; step S520, if the tower load of the target wind turbine group exceeds the second load threshold, the second fault warning model generates a second fault warning signal; step S530, if the main shaft load of the target wind turbine group exceeds the third load threshold, the third fault warning model generates a third fault warning signal.

[0042] Preferably, based on the load analysis results of the target wind turbine set, the blade load of the set is determined, the first load threshold is used to determine whether the blade load is abnormal, and the load standard value is set based on the normal operating range of the blade and past experience data. When the real-time monitoring value of the blade load exceeds the first load threshold, the first fault warning model will determine whether the abnormality can trigger a fault warning. If it is confirmed that there is a fault risk, the first fault warning model will generate a first fault warning signal. Similarly, based on the load analysis results of the target wind turbine set, the tower load of the set is determined, the second load threshold is used to determine whether the tower load is abnormal, and the load standard value is set based on the normal operating range of the tower and past experience data. When the real-time monitoring value of the tower load exceeds the second load threshold, the second fault warning model will determine whether the abnormality can trigger a fault warning. If it is confirmed that there is a fault risk, the second fault warning model will generate a second fault warning signal. Based on the load analysis results of the target wind turbine, the main shaft load of the unit is determined. The third load threshold is used to determine whether the main shaft load is abnormal. The load standard value is set based on the normal working range of the main shaft and past experience data. When the real-time monitoring value of the main shaft load exceeds the third load threshold, the third fault warning model will determine whether the abnormality can trigger a fault warning. If it is confirmed that there is a fault risk, the third fault warning model will generate a third fault warning signal. The wind turbine fault warning signal generation mechanism based on load thresholds helps operation and maintenance personnel to promptly discover and deal with potential fault risks and ensure the stable operation and safety of wind turbines.

[0043] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A wind turbine fault early warning method combined with load analysis, characterized in that: The method comprises: Collect real-time wind data and unit structural parameters of the target wind turbine; Analyze the influence of wind direction on the load of the target wind turbine generator set based on the real-time wind power data, and obtain the wind direction change characteristics of the target wind turbine generator set; According to the unit structural parameters and the wind direction change characteristics, a load calculation model is established to obtain the load analysis results of the target wind turbine unit; Build a fault warning model; If there is an abnormal load change in the load analysis result, a fault warning signal is generated according to the fault warning model.

2. A wind turbine fault early warning method combined with load analysis as claimed in claim 1, characterized in that: The analysis of the influence of wind direction on the load of the target wind turbine generator set includes: Use wind direction sensors to collect wind direction data at the location of the target wind turbine in real time; Dynamically analyzing the wind direction data to obtain the wind direction change characteristics, wherein the wind direction change characteristics include wind direction fluctuation amplitude, wind direction change frequency and wind direction change rate; A load change when the wind direction changes is identified based on the wind direction change characteristic.

3. A wind turbine fault early warning method combined with load analysis as claimed in claim 1, characterized in that: The method comprises: The wind speed sensor is used to collect multiple wind speed data at different heights of the target wind turbine to obtain the wind speed gradient; The wind direction change characteristic is corrected according to the wind speed gradient.

4. A wind turbine fault early warning method combined with load analysis as claimed in claim 2, characterized in that: include: Collect the ambient temperature data and ambient humidity data of the target wind turbine; Establishing an environmental impact factor of a target wind turbine generator system based on the environmental temperature data and the environmental humidity data; The load calculation model is optimized according to the environmental influencing factors, and the load analysis results are adjusted.

5. The wind turbine fault early warning method combined with load analysis according to claim 1, characterized in that: The method comprises: Obtain the real-time power output parameters of the target wind turbine; Correlation analysis is performed on the real-time power output parameter and the load analysis result to evaluate the operating efficiency of the target wind turbine; If the operating efficiency of the target wind turbine is reduced, an operation and maintenance warning signal is generated.

6. A wind turbine fault early warning method combined with load analysis as claimed in claim 1, characterized in that: The constructing of the fault warning model comprises: The key components of the target wind turbines include blades, towers, and main shafts; Obtain historical fault records of the target wind turbine and determine blade fault data samples, tower fault data samples and main shaft fault data samples; Constructing a first fault warning sub-model, a second fault warning sub-model and a third fault warning sub-model respectively according to the blade fault data samples, the tower fault data samples and the main shaft fault data samples; The fault warning model is synthesized by using the first fault warning sub-model, the second fault warning sub-model and the third fault warning sub-model.

7. A wind turbine fault early warning method combined with load analysis as claimed in claim 6, characterized in that: A first fault warning sub-model, a second fault warning sub-model and a third fault warning sub-model are respectively constructed according to the blade fault data samples, the tower fault data samples and the main shaft fault data samples, including: extracting blade load data samples and blade failure data samples from the blade failure data samples; Taking the blade load data sample as training input and the blade fault data sample as training output, based on the neural network model, training to obtain the first fault warning sub-model; Likewise, the second fault warning sub-model and the third fault warning sub-model are obtained through training.

8. A wind turbine fault early warning method combined with load analysis as claimed in claim 7, characterized in that: If the load analysis result shows an abnormal load change, generating a fault warning signal according to the fault warning model includes: If the blade load of the target wind turbine exceeds a first load threshold, the first fault warning model generates a first fault warning signal; If the tower load of the target wind turbine exceeds a second load threshold, the second fault warning model generates a second fault warning signal; If the main shaft load of the target wind turbine generator set exceeds a third load threshold, the third fault warning model generates a third fault warning signal.

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