A fan tower instability tendency evaluation method, device, equipment and storage medium

Abstract

This invention relates to the field of wind power generation technology and discloses a method, device, equipment, and storage medium for assessing the instability tendency of wind turbine towers. The invention utilizes a preprocessing method to process acquired wind turbine monitoring data, then substitutes the preprocessed data into a wind turbine tower instability tendency probability model. This model comprehensively considers factors such as flange displacement and angle changes at various tower connections, as well as wind speed, operating time, and load, thereby obtaining a more accurate tower instability tendency probability value. Finally, by using judgment conditions to assess the stability of the tower, the invention more comprehensively and accurately reflects the stability assessment results of the wind turbine tower in actual operation, achieving the goal of meeting the current requirements for accurate and real-time monitoring of tower stability in wind power generation systems.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically to a method, apparatus, equipment, and storage medium for assessing the instability tendency of wind turbine towers. Background Technology

[0002] In wind power generation systems, the wind turbine tower, as a key structure supporting the wind turbine generator, directly affects the safety and reliability of the entire power generation system. With the continuous development of wind power generation technology, the single-unit capacity and tower height of wind turbines are gradually increasing. This causes the wind turbine tower to bear greater loads under complex natural environments and operating conditions, and the risk of tower instability is correspondingly increased.

[0003] Methods for assessing the stability of wind turbine towers in related technologies include: experience-based periodic inspection methods and numerical simulation methods such as finite element analysis. In experience-based periodic inspection methods, maintenance personnel visually inspect the tower for obvious deformation, cracks, or other abnormalities. Numerical simulation methods such as finite element analysis, on the other hand, establish mathematical models to predict the stability of the wind turbine tower.

[0004] However, the experience-based periodic inspection methods in related technologies are not only inefficient, but also difficult to detect structural damage or potential instability risks inside the tower. Numerical simulation methods such as finite element analysis have a low probability of producing inaccurate prediction results. Neither of these methods can meet the requirements of current wind power generation systems for accurate and real-time monitoring of tower stability. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for assessing the instability tendency of wind turbine towers, in order to solve the problem that the methods for assessing the stability of wind turbine towers in related technologies are difficult to meet the requirements of accurate and real-time monitoring of tower stability in current wind power generation systems.

[0006] In a first aspect, the present invention provides a method for assessing the instability tendency of a wind turbine tower, the method comprising:

[0007] Based on the acquired wind turbine monitoring data, the wind turbine monitoring data is processed using a preprocessing method to obtain preprocessed wind turbine monitoring data. The wind turbine monitoring data includes: wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time.

[0008] Based on the preprocessed wind turbine monitoring data, the instability probability of the wind turbine tower is obtained using a pre-constructed wind turbine tower instability probability model. The wind turbine tower instability probability model includes the wind turbine tower instability probability as a function of wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time.

[0009] Based on the probability of instability of the wind turbine tower, an evaluation is performed using preset judgment conditions, and the evaluation result of the instability tendency of the wind turbine tower is output.

[0010] Through the above implementation method, the acquired wind turbine monitoring data is processed using a preprocessing method. Then, the preprocessed wind turbine monitoring data is substituted into the wind turbine tower instability probability model. The wind turbine tower instability probability model comprehensively considers the flange displacement and angle changes at the connection points of each tower section of the wind turbine, as well as factors such as wind speed, operating time, and load, thereby obtaining a more accurate tower instability probability value. Finally, the stability of the tower is evaluated using judgment conditions, which more comprehensively and accurately reflects the stability evaluation results of the wind turbine tower in actual operation, and achieves the goal of meeting the current requirements of accurate and real-time monitoring of tower stability in wind power generation systems.

[0011] In one optional implementation, the construction of the wind turbine tower instability probabilistic model includes:

[0012] Based on displacement data at multiple windward tower flanges, the relative displacement variation is normalized using an error function to obtain the factors influencing the relative displacement.

[0013] Based on the relative angle change data of the upper and lower sides of multiple flange towers, the distribution of the relative angle change of the upper and lower sides of the flanges is analyzed using Gaussian function to obtain the influencing factors of angle change.

[0014] Based on the data acquisition time, a rectangular function is used to limit the effective data range and identify the influencing factors.

[0015] Based on wind speed, the relationship between wind speed and the probability of tower instability is constructed using the wind influence function, and the influencing factors of wind speed are obtained.

[0016] Based on the total operating time of the wind turbine, the relationship between time and the probability of tower instability is constructed using a time factor function to obtain the time-influencing factors.

[0017] Based on the loads borne at multiple tower connections, the relationship between load changes and the probability of tower instability is constructed using a load factor function, thus obtaining the load influencing factors.

[0018] Based on the factors influencing relative displacement, angle change, effective data range, wind speed, time, and load, and by integrating the time normalization function, a probability model for the instability tendency of the wind turbine tower is obtained.

[0019] Through the above implementation method, displacement data at multiple windward tower flanges are constructed as relative displacement influencing factors; relative angle changes on the upper and lower sides of multiple flange towers are constructed as angle change influencing factors; data acquisition time is constructed as effective data range influencing factors; wind speed is used as a wind speed influencing factor; total turbine operating time is constructed as a time influencing factor; and the load borne by multiple tower connections is constructed as a load influencing factor. By using a time normalization function, the flange displacement and angle changes at each tower connection, as well as factors such as wind speed, operating time, and load, are comprehensively considered to construct a wind turbine tower instability tendency probability model. This model is used to achieve accurate and real-time monitoring of wind turbine tower stability and to obtain the probability of wind turbine tower instability tendency, facilitating guidance for wind turbine operation and maintenance and reducing the probability of tower instability and collapse events.

[0020] In one optional implementation, the wind turbine tower instability probabilistic model includes:

[0021] ,

[0022] ,

[0023] ,

[0024] ,

[0025] in, This indicates the probability of instability in the wind turbine tower. Indicates the start time of wind turbine monitoring data collection; Indicates the end time of wind turbine monitoring data collection; The first part representing the wind turbine monitoring data One tower; Indicates factors affecting the range of valid data; This represents the time normalization function; Indicate the factors affecting wind speed; Indicates factors affecting time; Indicates factors affecting load; Indicates the factors affecting relative displacement; Indicates the factors affecting the relative angle change on the upper side of the flange; This indicates the factors affecting the relative angle change on the lower side of the flange.

[0026] Through the above implementation method, the constructed wind turbine tower instability tendency probability model is used to comprehensively consider the flange displacement and angle changes at the connection points of each wind turbine tower, as well as factors such as wind speed, operating time and load. This enables accurate and real-time monitoring of the stability of the wind turbine tower and obtains the probability of wind turbine tower instability tendency, which facilitates guidance for the operation and maintenance of the wind turbine and reduces the probability of tower instability and collapse events.

[0027] In one optional implementation, based on the probability of instability of the wind turbine tower, an evaluation is performed using preset judgment conditions, and the evaluation result of the instability tendency of the wind turbine tower is output, including:

[0028] Based on the probability of instability of the wind turbine tower, an evaluation is performed using a first judgment condition, which includes being greater than a first threshold.

[0029] When the probability of instability of the wind turbine tower meets the first judgment condition, the assessment result of the instability tendency of the wind turbine tower is high risk; otherwise, the probability of instability tendency of the wind turbine tower is assessed using the second judgment condition.

[0030] Through the above implementation method, the probability of wind turbine tower instability is evaluated using a first threshold. Cases where the probability of wind turbine tower instability is greater than the first threshold are considered high-risk. This facilitates data support for operation and maintenance personnel, enabling them to take more proactive measures against high-risk wind turbines, preventing the probability of tower instability from increasing further, reducing the risk of wind turbine tower instability and collapse, and thus ensuring the safe operation of wind turbine equipment.

[0031] In one optional implementation, the second determination condition includes being less than a second threshold;

[0032] The assessment of the probability of wind turbine tower instability using the second judgment condition includes:

[0033] When the probability of instability of the wind turbine tower meets the second judgment condition, the assessment result of the instability tendency of the wind turbine tower is low risk; otherwise, the assessment result of the instability tendency of the wind turbine tower is medium risk.

[0034] Through the above implementation method, the probability of wind turbine tower instability is assessed using the second threshold. This facilitates maintenance personnel to adjust maintenance strategies and investigate potential faults in a timely manner for wind turbines whose tower instability tendency assessment results are medium risk. It also helps to promptly identify and address potential problems, reducing the probability of tower collapse.

[0035] In one alternative implementation, it further includes:

[0036] Based on the assessment results of the wind turbine tower instability tendency, the wind turbine is inspected and repaired using a preset detection strategy.

[0037] The detection strategy includes:

[0038] When the assessment result of the wind turbine tower instability tendency is high risk, the corresponding wind turbine shall be shut down;

[0039] When the assessment result of the instability tendency of the wind turbine tower is medium risk, the corresponding wind turbine shall be operated at reduced power.

[0040] When the assessment result of the instability tendency of the wind turbine tower is low, the corresponding wind turbine shall be maintained in normal operation.

[0041] Through the above implementation methods, after obtaining the assessment results of the wind turbine tower instability tendency, wind turbines with high-risk assessment results are shut down, which facilitates maintenance personnel to promptly investigate and handle potential risks of the wind turbines, thereby reducing the probability of equipment damage caused by continued tower operation; wind turbines with medium-risk assessment results are operated at reduced power, which can reduce the risk of wind turbine tower instability and collapse, ensure the safe operation of wind turbine equipment, and also reduce the reduction in power generation caused by wind turbine shutdown; wind turbines with low-risk assessment results are maintained in normal operation, realizing the rational allocation and efficient utilization of operation and maintenance resources, and saving the consumption of human and material resources.

[0042] In one optional implementation, the step of processing the acquired wind turbine monitoring data using a preprocessing method to obtain preprocessed wind turbine monitoring data includes:

[0043] Based on the acquired wind turbine monitoring data, a Gaussian filtering algorithm is used for noise reduction to obtain smoothed wind turbine monitoring data.

[0044] Based on the smoothed wind turbine monitoring data, outliers in the wind turbine monitoring data are corrected by calculating the average value, standard deviation and preset threshold range, so as to obtain pre-processed wind turbine monitoring data.

[0045] Through the above implementation method, the Gaussian filtering algorithm is used to denoise the wind turbine monitoring data, which can effectively smooth the wind turbine monitoring data and remove high-frequency noise interference. By calculating the average value, standard deviation and preset threshold range, the outliers in the graded monitoring data are corrected, thereby ensuring the accuracy of the wind turbine tower instability tendency assessment probability results output by the wind turbine tower instability tendency probability model.

[0046] In a second aspect, the present invention provides a wind turbine tower instability tendency assessment device, the device comprising:

[0047] The data preprocessing module is used to process the acquired wind turbine monitoring data using a preprocessing method to obtain preprocessed wind turbine monitoring data. The wind turbine monitoring data includes: wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time.

[0048] The data calculation module is used to obtain the instability probability of the wind turbine tower based on the pre-processed wind turbine monitoring data and a pre-constructed wind turbine tower instability probability model. The wind turbine tower instability probability model includes the wind turbine tower instability probability as a function of wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connection points, data acquisition time, and total wind turbine operating time.

[0049] The result output module is used to evaluate the wind turbine tower instability tendency based on the probability of instability of the wind turbine tower using preset judgment conditions, and output the wind turbine tower instability tendency evaluation result.

[0050] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the wind turbine tower instability tendency assessment method of the first aspect or any corresponding embodiment described above.

[0051] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the wind turbine tower instability tendency assessment method of the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0052] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of the first process of the wind turbine tower instability tendency assessment method according to an embodiment of the present invention;

[0055] Figure 3This is a schematic diagram of the second process of the wind turbine tower instability tendency assessment method according to an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of the third process of the wind turbine tower instability tendency assessment method according to an embodiment of the present invention;

[0057] Figure 5 This is a structural block diagram of a wind turbine tower instability tendency assessment device according to an embodiment of the present invention;

[0058] Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0061] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0062] As an optional application scenario of this invention, such as Figure 1 As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0063] For example, application 101 can be any application that provides question-and-answer related services. For instance, application 101 could be a question-and-answer interactive application, such as a text-to-text application, an image-to-text application, etc. Figure 1In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as interactive pages, settings pages, query pages, etc.

[0064] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a mobile terminal, fixed terminal, or portable terminal, etc., including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0065] It should be noted that, Figure 1 This is merely an example of an application scenario and does not limit the scope of protection of this invention.

[0066] The embodiments of the present invention will now be described with reference to the accompanying drawings. It should be understood that the pages shown in the drawings are merely examples, and various page designs are possible in practice. The various graphic elements on the page may have different arrangements and different visual representations; one or more elements may be omitted or replaced, and one or more other elements may also be present, without any limitation in the embodiments of the present invention. Furthermore, the embodiments described below primarily pertain to terminal device 110. It should be understood that the actions described relative to terminal device 110 can be performed by application 101 on terminal device 110, or can be performed by application 101 in conjunction with its server (e.g., server 120).

[0067] The main methods for assessing the stability of wind turbine towers in related technologies include: one is an experience-based periodic inspection method, in which maintenance personnel visually inspect the tower for obvious deformation, cracks or other abnormalities; the other is to use numerical simulation methods such as finite element analysis to predict the stability of the wind turbine tower by establishing a mathematical model of the tower.

[0068] However, this method is not only inefficient, but also difficult to detect some internal structural damage or potential instability risks. Often, the problem is only discovered when it has become quite serious. However, this method relies on accurate model parameters and complex calculations. In actual operation, due to factors such as manufacturing process, installation errors, and structural changes after long-term operation, it is difficult to ensure that the model completely matches the actual situation, which affects the accuracy of the prediction results.

[0069] In addition, some methods based on monitoring a single physical quantity have been disclosed in related technologies, such as assessing stability by monitoring the vibration or stress data of the tower. However, the instability of a wind turbine tower is the result of the combined effects of multiple factors, and monitoring a single physical quantity cannot fully reflect the actual stability state of the tower, which can easily lead to misjudgment or omission.

[0070] To address the shortcomings of the aforementioned related technologies, this invention provides a method for assessing the instability tendency of wind turbine towers. The method involves preprocessing the acquired wind turbine monitoring data and then substituting the preprocessed data into a wind turbine tower instability probability model. This model comprehensively considers factors such as flange displacement and angle changes at the connections of each tower section, as well as wind speed, operating time, and load, thereby obtaining a more accurate tower instability probability value. Finally, the stability of the tower is assessed using judgment conditions, providing a more comprehensive and accurate reflection of the stability assessment results of the wind turbine tower during actual operation. This achieves the goal of meeting the current requirements for accurate and real-time monitoring of tower stability in wind power generation systems.

[0071] According to an embodiment of the present invention, a method for assessing the instability tendency of a wind turbine tower is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0072] This embodiment provides a method for assessing the instability tendency of wind turbine towers, which can be used in the aforementioned wind farm servers. Figure 2 This is a flowchart of a wind turbine tower instability tendency assessment method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0073] S201, Based on the acquired wind turbine monitoring data, the wind turbine monitoring data is processed using a preprocessing method to obtain preprocessed wind turbine monitoring data; the wind turbine monitoring data includes: wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time.

[0074] Wind speed, measured by a wind speed sensor placed near the wind turbine and sampled at a frequency of 10Hz, is an important environmental factor affecting the stress on the wind turbine tower.

[0075] Displacement data at multiple windward tower flanges The data is obtained in real time by high-precision displacement sensors installed at the three tower connections on the windward side of the wind turbine. The displacement sensors have a monitoring accuracy of ±0.1mm and can keenly detect minute displacement changes at the tower flanges.

[0076] Data on the relative angular changes of the upper and lower sides of multiple flange tower sections and The data is obtained by installing dual-axis tilt sensors on the upper and lower sides of the tower flange connection. The measurement range of the dual-axis tilt sensors is set to ±15°, and the accuracy is ±0.1°.

[0077] Load borne by multiple tower joints This is obtained from data recorded by a monitoring system connected to the wind turbine control system.

[0078] Data acquisition time includes the start time of data acquisition. The end time is .

[0079] Total operating time of the fan This represents the total operating time of the wind turbine since its installation, obtained from data recorded by a monitoring system connected to the wind turbine control system.

[0080] The preprocessing methods include denoising and outlier handling, which are used to process high-frequency noise interference and outliers in the wind turbine monitoring data, thereby improving the accuracy of the wind turbine tower instability tendency probability output by the subsequent wind turbine tower instability tendency probability model.

[0081] S202, Based on the pre-processed wind turbine monitoring data, the wind turbine tower instability tendency probability is obtained using a pre-constructed wind turbine tower instability tendency probability model; the wind turbine tower instability tendency probability model includes the wind turbine tower instability tendency probability as a function of wind speed, displacement data at multiple windward side tower flanges, relative angle change data of the upper and lower sides of multiple flange towers, load borne by multiple tower connection points, data acquisition time, and total wind turbine operating time.

[0082] The wind turbine tower instability probabilistic model is constructed by comprehensively considering the flange displacement and angle changes at the connection points of each tower section of the wind turbine, as well as factors such as wind speed, operating time and load. It can achieve accurate and real-time monitoring of the stability of the wind turbine tower.

[0083] The construction of the wind turbine tower instability probability model in S202 above includes:

[0084] a1. Based on displacement data at multiple windward tower flange locations, the relative displacement variation is normalized using an error function to obtain the factors influencing the relative displacement.

[0085] When analyzing the stability of wind turbine towers, the displacement changes at the connections between tower sections can vary significantly due to factors such as the turbine's location and operating conditions, making direct comparisons difficult. By using an error function for normalization, the displacement changes at each tower connection are transformed into values ​​with a uniform scale, facilitating the analysis of the impact of displacement changes on the probability of instability.

[0086] For example, a1 above includes:

[0087]

[0088] ,

[0089] in, This represents the error function, used to normalize the relative displacement change. This represents the independent variable in the error function; Indicates the specific time of data acquisition during wind turbine operation; express Time of the first The relative displacement change at each tower flange; Indicates in arrive The average value of the relative displacement changes at all tower flanges within the time period; Indicates in arrive The standard deviation of the relative displacement changes at all tower flanges over the time period.

[0090] Error function Used to normalize the relative displacement change. Compared with the overall average change Compare and combine with standard deviation By utilizing the characteristics of the error function, the difference between the relative displacement change and the average displacement change is highlighted, enabling the displacement changes at different tower connections to be compared and analyzed on a unified scale, thus providing a basis for a comprehensive assessment of instability risk.

[0091] a2. Based on the relative angle change data of the upper and lower sides of multiple flange towers, the distribution of the relative angle change of the upper and lower sides of the flange is analyzed using Gaussian function to obtain the influencing factors of angle change.

[0092] During operation, the angle change at the flange connection of the wind turbine tower is not uniform or random, but has a certain distribution pattern. By using the Gaussian function, this distribution pattern can be accurately characterized, which facilitates the analysis of the impact of the stability of the angle change of the wind turbine tower flange on the stability of the tower. This solves the technical problem of how to quantify the distribution characteristics of the angle change.

[0093] For example, a2 above includes:

[0094] ,

[0095] in, Represents the Gaussian function; Gaussian function The independent variable in the equation describes the position of the angle change within the Gaussian distribution. It is used in calculating the influencing factors of the relative angle change on the upper side of the flange. When calculating the influencing factors of the relative angle change on the lower side of the flange, it is... ; Gaussian function The mean parameter in the value is used to represent the central tendency of the angle change, and is used when calculating the influencing factors of the relative angle change on the upper side of the flange. When calculating the influencing factors of the relative angle change on the lower side of the flange, it is... ; Gaussian function The standard deviation parameter in the calculation is used to determine the influencing factors of the relative angle change on the upper side of the flange. When calculating the influencing factors of the relative angle change on the lower side of the flange, it is... .

[0096] Using Gaussian function By analyzing the distribution of the relative angle changes on the upper and lower sides of the flange, and by using a Gaussian function to analyze the distribution characteristics of the relative angle changes on the upper and lower sides of the flange, the uneven stress at the tower connection can be reflected. Furthermore, the effect of this distribution characteristic on the instability probability can be quantified, which helps to more comprehensively assess the stability of the tower.

[0097] a3, based on the data acquisition time, uses a rectangular function to limit the effective data range and obtain the influencing factors.

[0098] Abnormal data can interfere with the calculation of the tower instability probability, leading to inaccurate evaluation results. This invention utilizes a rectangular function to solve the problem of data validity screening, ensuring that only reliable data participates in the calculation and improving the accuracy of the analysis results.

[0099] For example, a3 above includes:

[0100] ,

[0101] in, Represents a rectangular function; Indicates the specific time of data acquisition during wind turbine operation; Representing rectangular functions The start time of the effective data collection time interval; Representing rectangular functions The end time of the effective data collection time interval.

[0102] Rectangle function By setting an effective data collection time interval, the data is filtered. Since some abnormal time points may appear during the data collection process, and these data may be inaccurate due to sensor failure, external interference, etc., the rectangular function is used to exclude these abnormal data from the calculation, thereby ensuring that only reliable data participates in the subsequent calculation of the tower instability tendency probability.

[0103] a4. Based on wind speed, the relationship between wind speed and the probability of tower instability is constructed using the wind influence function, thus obtaining the factors influencing wind speed.

[0104] Wind speed is a crucial external factor affecting the stability of wind turbine towers, and fluctuations in wind speed significantly impact the stress on the towers. The wind influence function provided in this invention solves the problem of accurately incorporating wind speed factors into the analysis of wind turbine tower instability tendencies, thereby improving the accuracy of wind turbine tower instability tendency assessment.

[0105] For example, a4 above includes:

[0106] ,

[0107] in, This represents the function representing the influence of strong winds. express Wind speed at any given moment; Indicates the specific time of data acquisition during wind turbine operation; Indicates in arrive The average wind speed over a period of time is used to analyze the relative magnitude of wind speed. Indicates in arrive The standard deviation of wind speed over a period of time is used to describe the degree of fluctuation in wind speed. Function representing the influence of strong winds The weighting coefficients in the formula are used to adjust the sensitivity of wind speed to the probability of wind turbine tower instability.

[0108] The influence function of strong wind is used to quantify the impact of wind speed fluctuations on tower stability. Wind speed is included as an important environmental factor in the calculation of the probability of wind turbine tower instability, thereby improving the accuracy of the obtained assessment results.

[0109] a5. Based on the total operating time of the wind turbine, the relationship between time and the probability of tower instability is constructed using a time factor function to obtain the time influencing factors.

[0110] During long-term operation, the stability of wind turbine towers gradually decreases due to factors such as aging and fatigue. In this embodiment of the invention, the problem of how to quantify the impact of time factors on the probability of instability is solved by using a time factor function, making the stability assessment more comprehensive.

[0111] For example, a5 above includes:

[0112] ,

[0113] in, Represents a time-factor function; Indicates the specific time of data acquisition during wind turbine operation; This represents the total operating time of the wind turbine, used to analyze the effect of time accumulation on the probability of tower instability. This represents the time influence coefficient in the time factor function, used to quantify the impact of time on the probability of tower instability.

[0114] By utilizing time factor functions Considering the total operating time of the wind turbine since installation and current time Through the form of an exponential function, combined with the time influence coefficient This reflects the increasing probability of tower instability due to aging and other factors over time.

[0115] a6. Based on the load borne by multiple tower connections, the relationship between load change and tower instability probability is constructed using the load factor function, thus obtaining the load influencing factors.

[0116] Since the load borne at the connection points of each tower section is one of the key factors affecting the stability of the tower, changes in load can lead to uneven stress on the tower, increasing the risk of tower instability. This invention addresses the problem of accurately assessing the impact of load changes on the instability probability through a load factor function, thus improving the instability analysis system.

[0117] For example, a6 above includes:

[0118] ,

[0119] in, Represents the load factor function; express Time of the first The load borne by each tower connection point; Indicates the specific time of data acquisition during wind turbine operation; Indicates in arrive The average load borne by each tower connection point within a time period is used to analyze the overall trend of load changes; Indicates in arrive The standard deviation of the load borne by each tower connection point within a time period is used to describe the stability of load changes; Represents the load factor function The weighting coefficients in the formula are used to adjust the sensitivity of the load to the probability of instability of the wind turbine tower.

[0120] Based on the relative displacement influencing factors, angle change influencing factors, effective data range influencing factors, wind speed influencing factors, time influencing factors, and load influencing factors, and by integrating the time normalization function, a probability model for wind turbine tower instability tendency is obtained.

[0121] When considering the impact of multiple factors on the probability of instability, the time factor needs to be analyzed on the same scale as other factors. In this embodiment of the invention, a normalization function is used to solve the standardization problem of the time factor, facilitating the integration and calculation of the time factor with other factors and improving the reliability of the analysis results.

[0122] For example, the time normalization function in a7 above includes:

[0123] ,

[0124] in, This represents the time normalization function; Indicates the specific time of data acquisition during wind turbine operation; express arrive The average time within a time period is used to standardize the time. Indicates time The standard deviation is used to assist in the standardization of time.

[0125] Time is normalized using a time normalization function. Standardize the data and use the average value over time. and standard deviation This ensures that the time factor has a uniform scale in calculating the probability of tower instability, thus guaranteeing the rationality and consistency of the time factor in the calculation of the probability of tower instability.

[0126] In summary, the probabilistic model for wind turbine tower instability includes:

[0127] ,

[0128] ,

[0129] ,

[0130] ,

[0131] in, This indicates the probability of instability in the wind turbine tower. Indicates the start time of wind turbine monitoring data collection; Indicates the end time of wind turbine monitoring data collection; The first part representing the wind turbine monitoring data One tower; Indicates factors affecting the range of valid data; This represents the time normalization function; Indicate the factors affecting wind speed; Indicates factors affecting time; Indicates factors affecting load; Indicates the factors affecting relative displacement; Indicates the factors affecting the relative angle change on the upper side of the flange; This indicates the factors affecting the relative angle change on the lower side of the flange.

[0132] By utilizing the constructed wind turbine tower instability probability model, and comprehensively considering factors such as flange displacement and angle changes at the connection points of each tower section, as well as wind speed, operating time, and load, the stability of the wind turbine tower can be accurately and in real time monitored, and the probability of tower instability can be obtained. This facilitates guidance for the operation and maintenance of the wind turbine and reduces the probability of tower instability and collapse events.

[0133] S203, based on the probability of instability of the wind turbine tower, an evaluation is performed using preset judgment conditions, and the evaluation result of the instability tendency of the wind turbine tower is output.

[0134] By using judgment conditions to assess the probability of wind turbine tower instability, the assessment results of wind turbine tower instability tendency are obtained, which can provide data reference for the operation and maintenance and repair strategies of wind turbine tower, and reduce the probability of wind turbine equipment damage caused by wind turbine tower collapse.

[0135] This embodiment provides a method for assessing the instability tendency of wind turbine towers. The method involves preprocessing acquired wind turbine monitoring data and then substituting the preprocessed data into a probability model for tower instability. This model comprehensively considers factors such as flange displacement and angle changes at various tower connections, as well as wind speed, operating time, and load, resulting in a more accurate probability value for tower instability. Finally, the stability of the tower is assessed using judgment conditions, providing a more comprehensive and accurate reflection of the tower's stability during actual operation. This achieves the goal of meeting the current requirements for precise and real-time monitoring of tower stability in wind power systems.

[0136] This embodiment provides a method for assessing the instability tendency of wind turbine towers, which can be used in the aforementioned wind farm servers. Figure 3 This is a flowchart of a wind turbine tower instability tendency assessment method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0137] S301, Based on the acquired wind turbine monitoring data, the wind turbine monitoring data is processed using a preprocessing method to obtain preprocessed wind turbine monitoring data; the wind turbine monitoring data includes: wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time.

[0138] Specifically, S301 above includes:

[0139] S3011, based on the acquired wind turbine monitoring data, uses a Gaussian filtering algorithm to perform noise reduction processing to obtain smoothed wind turbine monitoring data.

[0140] Since noise in the collected wind turbine monitoring data can affect the accuracy of subsequent analysis, a Gaussian filtering algorithm is used to denoise the wind turbine monitoring data.

[0141] Gaussian filtering algorithms can effectively smooth data and remove high-frequency noise interference by setting appropriate Gaussian kernel parameters.

[0142] For example, based on the frequency characteristics and noise level of the wind turbine monitoring data, the standard deviation of the Gaussian kernel in the Gaussian filtering algorithm is reasonably adjusted to make the wind turbine monitoring data smoother while retaining key features.

[0143] S3012, based on the smoothed wind turbine monitoring data, uses the calculated average value, standard deviation and preset threshold range to correct the outliers in the wind turbine monitoring data, and obtain the pre-processed wind turbine monitoring data.

[0144] Since outliers in the collected wind turbine monitoring data can also affect the accuracy of subsequent analysis, the mean and standard deviation of the wind turbine monitoring data are calculated, and an evaluation is performed using a preset threshold range. Data that exceeds the threshold range is then corrected.

[0145] For outliers, the mean of nearby data points or interpolation can be used for correction. For example, if the difference between the displacement data at the tower flange at a certain moment and the mean is greater than a preset threshold range, it can be corrected by weighted averaging of the displacement data at adjacent moments.

[0146] S302, based on preprocessed wind turbine monitoring data, utilizes a pre-constructed wind turbine tower instability probability model to obtain the wind turbine tower instability probability. The wind turbine tower instability probability model includes the wind turbine tower instability probability as a function of wind speed, displacement data at multiple windward-side tower flanges, relative angle changes between the upper and lower sides of multiple flanges, loads borne at multiple tower connections, data acquisition time, and total wind turbine operating time. For details, please refer to [link to details]. Figure 2 S202 of the illustrated embodiment will not be described again here.

[0147] S303, based on the probability of wind turbine tower instability, an evaluation is performed using preset judgment conditions, and the wind turbine tower instability tendency evaluation result is output. For details, please refer to [link to relevant documentation]. Figure 2 S203 of the illustrated embodiment will not be described again here.

[0148] This embodiment provides a method for assessing the instability tendency of wind turbine towers. The method involves preprocessing acquired wind turbine monitoring data and then substituting the preprocessed data into a probability model for tower instability. This model comprehensively considers factors such as flange displacement and angle changes at various tower connections, as well as wind speed, operating time, and load, resulting in a more accurate probability value for tower instability. Finally, the stability of the tower is assessed using judgment conditions, providing a more comprehensive and accurate reflection of the tower's stability during actual operation. This achieves the goal of meeting the current requirements for precise and real-time monitoring of tower stability in wind power systems.

[0149] This embodiment provides a method for assessing the instability tendency of wind turbine towers, which can be used in the aforementioned wind farm servers. Figure 4 This is a flowchart of a wind turbine tower instability tendency assessment method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0150] S401, based on the acquired wind turbine monitoring data, the wind turbine monitoring data is processed using a preprocessing method to obtain preprocessed wind turbine monitoring data; the wind turbine monitoring data includes: wind speed, displacement data at multiple windward-side tower flanges, relative angle changes between the upper and lower sides of multiple flange towers, loads borne at multiple tower connections, data acquisition time, and total wind turbine operating time. For details, please refer to [link to relevant documentation]. Figure 2 S201 of the illustrated embodiment will not be described again here.

[0151] S402, based on preprocessed wind turbine monitoring data, utilizes a pre-constructed wind turbine tower instability probability model to obtain the wind turbine tower instability probability. The wind turbine tower instability probability model includes the wind turbine tower instability probability as a function of wind speed, displacement data at multiple windward-facing tower flanges, relative angle changes between the upper and lower sides of multiple flanges, loads borne at multiple tower connections, data acquisition time, and total wind turbine operating time. For details, please refer to [link to details]. Figure 2 S202 of the illustrated embodiment will not be described again here.

[0152] S403, based on the probability of instability of the wind turbine tower, an evaluation is performed using preset judgment conditions, and the evaluation result of the instability tendency of the wind turbine tower is output.

[0153] Specifically, S403 includes:

[0154] Based on the probability of instability of the wind turbine tower, an evaluation is performed using a first judgment condition, which includes being greater than a first threshold.

[0155] When the probability of instability of the wind turbine tower meets the first judgment condition, the assessment result of the instability tendency of the wind turbine tower is high risk; otherwise, the probability of instability tendency of the wind turbine tower is assessed using the second judgment condition.

[0156] The probability of wind turbine tower instability is assessed using a first threshold. Cases where the probability of wind turbine tower instability exceeds the first threshold are considered high-risk. This provides data support for operation and maintenance personnel, enabling them to take more proactive measures against high-risk wind turbines, prevent the probability of tower instability from increasing further, reduce the risk of wind turbine tower instability and collapse, and thus ensure the safe operation of wind turbine equipment.

[0157] For example, in this embodiment of the invention, the first threshold is 0.5. By setting wind turbines with a tower instability tendency probability greater than the first threshold as high-risk, it indicates that the wind turbine tower has a high risk of instability and collapse. In this case, the wind turbine must be shut down immediately to reduce the occurrence of tower instability and collapse events caused by continuous operation of the wind turbine.

[0158] Furthermore, the second judgment condition includes being less than a second threshold; the assessment of the probability of wind turbine tower instability using the second judgment condition includes:

[0159] When the probability of instability of the wind turbine tower meets the second judgment condition, the assessment result of the instability tendency of the wind turbine tower is low risk; otherwise, the assessment result of the instability tendency of the wind turbine tower is medium risk.

[0160] Using a second threshold to assess the probability of wind turbine tower instability facilitates timely adjustments to maintenance strategies and troubleshooting of potential faults for wind turbines with a medium-risk instability assessment result. This allows for the timely detection and handling of potential problems, reducing the probability of tower collapse.

[0161] For example, in this embodiment of the invention, the second threshold is 0.3. By setting wind turbines with a tower instability tendency probability less than the second threshold as low-risk, it indicates that the wind turbine tower is in a relatively stable state, and the wind turbine can maintain normal operation. When the tower instability tendency probability is greater than or equal to 0.3 and less than or equal to 0.5, the corresponding wind turbine is set as medium-risk, indicating that the wind turbine tower has a certain risk of instability, but the probability of tower instability and collapse immediately is less than that of high-risk wind turbines. The risk of tower instability can be reduced by reducing the wind turbine output power and reducing the load on the tower.

[0162] Furthermore, the present invention also includes:

[0163] S404. Based on the assessment results of the wind turbine tower instability tendency, the wind turbine is inspected and repaired using a preset detection strategy.

[0164] The detection strategy includes:

[0165] When the assessment result of the wind turbine tower instability tendency is high risk, the corresponding wind turbine shall be shut down;

[0166] When the assessment result of the instability tendency of the wind turbine tower is medium risk, the corresponding wind turbine shall be operated at reduced power.

[0167] When the assessment result of the instability tendency of the wind turbine tower is low, the corresponding wind turbine shall be maintained in normal operation.

[0168] After obtaining the assessment results of the instability tendency of the wind turbine tower, the wind turbines with high risk assessment results are shut down to facilitate maintenance personnel to promptly investigate and deal with potential risks of the wind turbines, thereby reducing the probability of equipment damage caused by continued tower operation. The wind turbines with medium risk assessment results are operated at reduced power, which can reduce the risk of wind turbine tower instability and collapse, ensure the safe operation of wind turbine equipment, and also reduce the reduction in power generation caused by wind turbine shutdown. The wind turbines with low risk assessment results are maintained in normal operation, realizing the rational allocation and efficient use of operation and maintenance resources, and saving the consumption of human and material resources.

[0169] Optionally, after obtaining the assessment results of the wind turbine tower instability tendency, the reasons for the increase in the probability of wind turbine tower instability tendency are determined by analyzing each element in the wind turbine tower instability tendency probability model.

[0170] For example, when the wind turbine tower instability tendency assessment result is low risk, A smaller value indicates a smaller relative displacement change at the connection points of the tower sections. Close to the average The displacement of each tower section remained stable; and The angle changes on the upper and lower sides of the flange are concentrated around the mean with small fluctuations, indicating that the angle changes at the tower connection are stable. The value represents wind speed. Near average wind speed Wind speed fluctuations have a relatively small impact on tower stability; Reflects the operating time of the fan The impact on the probability of tower instability is not significant, meaning that factors such as aging during wind turbine operation do not significantly affect tower stability. The value indicates the load at the connection point of each tower section. Near average load The load change has a relatively small impact on stability.

[0171] In low-risk scenarios, wind turbines can maintain normal operation, but maintenance personnel still need to conduct regular routine inspections to check the condition of sensors, ensure data acquisition is working properly, guarantee data accuracy and reliability, and continuously monitor tower stability. Simultaneously, data from each inspection should be recorded to observe long-term trends in various physical quantities, providing a reference for subsequent maintenance.

[0172] When the assessment result of the wind turbine tower instability tendency is medium risk. The increase in the value indicates a change in relative displacement at some tower connections. Deviation from the average The displacement changes began to show abnormalities; and The value shows that the distribution of the angle change is somewhat dispersed, that is, the angle change on the upper and lower sides of the flange fluctuates more, which may lead to uneven stress on the tower. The value indicates wind speed. Deviation from average wind speed The degree of wind fluctuation is relatively large, and the impact of wind speed fluctuations on tower stability is enhanced. This implies that as running time... Factors such as increased temperature and tower aging begin to affect the stability of the tower. The value displays the load at the connection point of some tower sections. Deviation from average load The impact of load changes on tower stability increases.

[0173] For medium-risk situations, wind turbines must immediately reduce their output power to decrease the load on the tower. Maintenance personnel should increase the frequency of tower monitoring, for example, from once a day to three times a day. Inspections should include not only sensors and data acquisition systems but also a focus on any abnormalities in the tower's appearance, such as deformation or cracks. Simultaneously, close monitoring of changes in the tower's instability probability is crucial, analyzing the contribution of various factors to these changes. If a particular factor (such as displacement changes or wind speed) is found to significantly impact the tower's instability probability, further in-depth analysis of that factor is necessary, along with targeted measures, such as detailed inspection and reinforcement of the connections in tower sections with abnormal displacement.

[0174] When the assessment result of wind turbine tower instability tendency is high risk The significant increase in the value indicates a change in relative displacement at the connection points of multiple tower sections. Significant deviation from the mean The displacement changes are abnormally drastic; and The value shows that the distribution of the angle change is extremely dispersed, the angle change on the upper and lower sides of the flange fluctuates greatly, and the stress at the tower connection is severely uneven. The value indicates wind speed. Continuing to be in extreme conditions has a significant impact on the stability of the tower; Factors such as tower aging caused by long-term operation have seriously threatened the stability of the tower. The value displays the load at the connection point of multiple tower segments. Significant deviation from average load Load changes severely damage the stability of the tower.

[0175] In high-risk situations, the wind turbine must be shut down immediately, and an emergency maintenance procedure must be initiated. Maintenance personnel will conduct a comprehensive inspection of the tower, including checking the tightness of flange connections and using specialized tools to measure the bolt torque at the connections to ensure it meets requirements. The tower structure will be inspected for deformation or damage, and non-destructive testing techniques (such as ultrasonic testing and radiographic testing) can be used to examine the internal structure. Based on the inspection results, appropriate repairs and treatments will be carried out, such as retightening flange bolts and repairing deformed or damaged tower structures. After maintenance is completed, data will be collected again to calculate the probability of tower instability. Once the probability of tower instability has returned to a low-risk level, the wind turbine will be restarted.

[0176] This embodiment also provides a wind turbine tower instability tendency assessment device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0177] This embodiment provides a device for assessing the instability tendency of wind turbine towers, such as... Figure 5 As shown, it includes:

[0178] The data preprocessing module 510 is used to process the acquired wind turbine monitoring data using a preprocessing method to obtain preprocessed wind turbine monitoring data. The wind turbine monitoring data includes: wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time.

[0179] The data calculation module 520 is used to obtain the instability probability of the wind turbine tower based on the pre-processed wind turbine monitoring data and a pre-constructed wind turbine tower instability probability model. The wind turbine tower instability probability model includes the wind turbine tower instability probability as a function of wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connection points, data acquisition time, and total wind turbine operating time.

[0180] The result output module 530 is used to evaluate the wind turbine tower instability tendency based on the probability of instability of the wind turbine tower using preset judgment conditions, and output the wind turbine tower instability tendency evaluation result.

[0181] In some alternative implementations, the data preprocessing module 510 includes:

[0182] The noise reduction processing unit is used to perform noise reduction processing on the acquired wind turbine monitoring data using a Gaussian filtering algorithm to obtain smoothed wind turbine monitoring data.

[0183] The outlier processing unit is used to correct outliers in the wind turbine monitoring data based on the smoothed data, by calculating the average value, standard deviation and preset threshold range, so as to obtain the pre-processed wind turbine monitoring data.

[0184] In some optional implementations, the construction of the wind turbine tower instability probability model in the data calculation module 520 includes:

[0185] Based on displacement data at multiple windward tower flanges, the relative displacement variation is normalized using an error function to obtain the factors influencing the relative displacement.

[0186] Based on the relative angle change data of the upper and lower sides of multiple flange towers, the distribution of the relative angle change of the upper and lower sides of the flanges is analyzed using Gaussian function to obtain the influencing factors of angle change.

[0187] Based on the data acquisition time, a rectangular function is used to limit the effective data range and identify the influencing factors.

[0188] Based on wind speed, the relationship between wind speed and the probability of tower instability is constructed using the wind influence function, and the influencing factors of wind speed are obtained.

[0189] Based on the total operating time of the wind turbine, the relationship between time and the probability of tower instability is constructed using a time factor function to obtain the time-influencing factors.

[0190] Based on the loads borne at multiple tower connections, the relationship between load changes and the probability of tower instability is constructed using a load factor function, thus obtaining the load influencing factors.

[0191] Based on the factors influencing relative displacement, angle change, effective data range, wind speed, time, and load, and by integrating the time normalization function, a probability model for the instability tendency of the wind turbine tower is obtained.

[0192] In some alternative implementations, the wind turbine tower instability probabilistic model in the data calculation module 520 includes:

[0193] ,

[0194] ,

[0195] ,

[0196] ,

[0197] in, This indicates the probability of instability in the wind turbine tower. Indicates the start time of wind turbine monitoring data collection; Indicates the end time of wind turbine monitoring data collection; The first part representing the wind turbine monitoring data One tower; Indicates factors affecting the range of valid data; This represents the time normalization function; Indicate the factors affecting wind speed; Indicates factors affecting time; Indicates factors affecting load; Indicates the factors affecting relative displacement; Indicates the factors affecting the relative angle change on the upper side of the flange; This indicates the factors affecting the relative angle change on the lower side of the flange.

[0198] In some optional implementations, the result output module 530 is specifically used for:

[0199] Based on the probability of instability of the wind turbine tower, an evaluation is performed using a first judgment condition, which includes being greater than a first threshold.

[0200] When the probability of instability of the wind turbine tower meets the first judgment condition, the assessment result of the instability tendency of the wind turbine tower is high risk; otherwise, the probability of instability tendency of the wind turbine tower is assessed using the second judgment condition.

[0201] The second judgment condition includes being less than a second threshold. The assessment of the probability of wind turbine tower instability using the second judgment condition includes:

[0202] When the probability of instability of the wind turbine tower meets the second judgment condition, the assessment result of the instability tendency of the wind turbine tower is low risk; otherwise, the assessment result of the instability tendency of the wind turbine tower is medium risk.

[0203] In some alternative embodiments, the apparatus further includes:

[0204] The maintenance strategy output unit is used to perform maintenance on the wind turbine based on the assessment results of the wind turbine tower instability tendency and using a preset detection strategy.

[0205] The detection strategy includes:

[0206] When the assessment result of the wind turbine tower instability tendency is high risk, the corresponding wind turbine shall be shut down;

[0207] When the assessment result of the instability tendency of the wind turbine tower is medium risk, the corresponding wind turbine shall be operated at reduced power.

[0208] When the assessment result of the instability tendency of the wind turbine tower is low, the corresponding wind turbine shall be maintained in normal operation.

[0209] The wind turbine tower instability tendency assessment device provided in this embodiment of the invention can execute the wind turbine tower instability tendency assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0210] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0211] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0212] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0213] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the wind turbine tower instability tendency assessment method of the embodiments of the present invention.

[0214] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0215] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the wind turbine tower instability tendency assessment method shown in the above embodiments is implemented.

[0216] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0217] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for assessing the instability tendency of wind turbine towers, characterized in that, The method includes: Based on the acquired wind turbine monitoring data, the wind turbine monitoring data is processed using a preprocessing method to obtain preprocessed wind turbine monitoring data. The wind turbine monitoring data includes: wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time. Based on the preprocessed wind turbine monitoring data, the instability probability of the wind turbine tower is obtained using a pre-constructed wind turbine tower instability probability model. The wind turbine tower instability probability model includes the wind turbine tower instability probability as a function of wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time. Based on the probability of instability of the wind turbine tower, an evaluation is performed using preset judgment conditions, and the evaluation result of the instability tendency of the wind turbine tower is output. The probability model for the instability tendency of the wind turbine tower includes: ， ， ， ， in, This indicates the probability of instability in the wind turbine tower. Indicates the start time of wind turbine monitoring data collection; Indicates the end time of wind turbine monitoring data collection; The first part representing the wind turbine monitoring data One tower; Indicates factors affecting the range of valid data; This represents the time normalization function; Indicates the specific time of data acquisition during wind turbine operation; express arrive Average time within a time period; Indicates time Standard deviation; Indicate the factors affecting wind speed; express Wind speed at any given moment; Indicates in arrive The average wind speed over a given time period; Indicates in arrive Standard deviation of wind speed over a time period; Function representing the influence of strong winds The weighting coefficients in the text; Indicates factors affecting time; Indicates the total operating time of the fan; This represents the time influence coefficient in the time factor function; Indicates factors affecting load; express Time of the first The load borne by each tower connection point; Indicates in arrive The average load borne by each tower connection point within a given time period; Indicates in arrive The standard deviation of the load borne by each tower connection point within a time period; Represents the load factor function The weighting coefficients in the text; Indicates the factors affecting relative displacement; express Time of the first The relative displacement change at each tower flange; Indicates in arrive The average value of the relative displacement changes at all tower flanges within the time period; Indicates in arrive The standard deviation of the relative displacement changes at all tower flanges within the time period; Indicates the factors affecting the relative angle change on the upper side of the flange; express Time of the first The relative angle change on the upper side of the flange at each tower flange; express Time of the first The average parameter of the relative angle change on the upper side of the flange at each tower flange; express Time of the first The standard deviation parameter of the relative angle change on the upper side of the flange at each tower flange; Indicates the factors affecting the relative angle change on the lower side of the flange; express Time of the first The relative angle change of the lower side of the flange at each tower flange; express Time of the first The average parameter of the relative angle change of the lower side of the flange at each tower flange; express Time of the first The standard deviation parameter of the relative angle change on the lower side of the flange at each tower flange.

2. The method according to claim 1, characterized in that, The construction of the probability model for the instability tendency of the wind turbine tower includes: Based on displacement data at multiple windward tower flanges, the relative displacement variation is normalized using an error function to obtain the factors influencing the relative displacement. Based on the relative angle change data of the upper and lower sides of multiple flange towers, the distribution of the relative angle change of the upper and lower sides of the flanges is analyzed using Gaussian function to obtain the influencing factors of angle change. Based on the data acquisition time, a rectangular function is used to limit the effective data range and identify the influencing factors. Based on wind speed, the relationship between wind speed and the probability of tower instability is constructed using the wind influence function, and the influencing factors of wind speed are obtained. Based on the total operating time of the wind turbine, the relationship between time and the probability of tower instability is constructed using a time factor function to obtain the time-influencing factors. Based on the loads borne at multiple tower connections, the relationship between load changes and the probability of tower instability is constructed using a load factor function, thus obtaining the load influencing factors. Based on the factors influencing relative displacement, angle change, effective data range, wind speed, time, and load, and by integrating the time normalization function, a probability model for the instability tendency of the wind turbine tower is obtained.

3. The method according to claim 1, characterized in that, Based on the probability of instability of the wind turbine tower, an evaluation is performed using preset judgment conditions, and the evaluation result of the wind turbine tower instability tendency is output, including: Based on the probability of instability of the wind turbine tower, an evaluation is performed using a first judgment condition, which includes being greater than a first threshold. When the probability of instability of the wind turbine tower meets the first judgment condition, the assessment result of the instability tendency of the wind turbine tower is high risk; otherwise, the probability of instability tendency of the wind turbine tower is assessed using the second judgment condition.

4. The method according to claim 3, characterized in that, The second judgment condition includes being less than a second threshold; The assessment of the probability of wind turbine tower instability using the second judgment condition includes: When the probability of instability of the wind turbine tower meets the second judgment condition, the assessment result of the instability tendency of the wind turbine tower is low risk; otherwise, the assessment result of the instability tendency of the wind turbine tower is medium risk.

5. The method according to claim 4, characterized in that, Also includes: Based on the assessment results of the wind turbine tower instability tendency, the wind turbine is inspected and repaired using a preset detection strategy. The detection strategy includes: When the assessment result of the wind turbine tower instability tendency is high risk, the corresponding wind turbine shall be shut down; When the assessment result of the instability tendency of the wind turbine tower is medium risk, the corresponding wind turbine shall be operated at reduced power. When the assessment result of the instability tendency of the wind turbine tower is low, the corresponding wind turbine shall be maintained in normal operation.

6. The method according to any one of claims 1 to 5, characterized in that, The acquired wind turbine monitoring data is processed using a preprocessing method to obtain preprocessed wind turbine monitoring data, including: Based on the acquired wind turbine monitoring data, a Gaussian filtering algorithm is used for noise reduction to obtain smoothed wind turbine monitoring data. Based on the smoothed wind turbine monitoring data, outliers in the wind turbine monitoring data are corrected by calculating the average value, standard deviation and preset threshold range, so as to obtain pre-processed wind turbine monitoring data.

7. A device for assessing the instability tendency of a wind turbine tower, characterized in that, The device includes: The data preprocessing module is used to process the acquired wind turbine monitoring data using a preprocessing method to obtain preprocessed wind turbine monitoring data. The wind turbine monitoring data includes: wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connections, data acquisition time, and total wind turbine operating time. The data calculation module is used to obtain the instability probability of the wind turbine tower based on the pre-processed wind turbine monitoring data and a pre-constructed wind turbine tower instability probability model. The wind turbine tower instability probability model includes the wind turbine tower instability probability as a function of wind speed, displacement data at multiple windward tower flanges, relative angle change data between the upper and lower sides of multiple flange towers, load borne by multiple tower connection points, data acquisition time, and total wind turbine operating time. The result output module is used to evaluate the wind turbine tower instability tendency based on the probability of instability of the wind turbine tower using preset judgment conditions, and output the wind turbine tower instability tendency evaluation result. The probability model for the instability tendency of the wind turbine tower includes: ， ， ， ， in, This indicates the probability of instability in the wind turbine tower. Indicates the start time of wind turbine monitoring data collection; Indicates the end time of wind turbine monitoring data collection; The first part representing the wind turbine monitoring data One tower; Indicates factors affecting the range of valid data; This represents the time normalization function; Indicates the specific time of data acquisition during wind turbine operation; express arrive Average time within a time period; Indicates time Standard deviation; Indicate the factors affecting wind speed; express Wind speed at any given moment; Indicates in arrive The average wind speed over a given time period; Indicates in arrive Standard deviation of wind speed over a time period; Function representing the influence of strong winds The weighting coefficients in the text; Indicates factors affecting time; Indicates the total operating time of the fan; This represents the time influence coefficient in the time factor function; Indicates factors affecting load; express Time of the first The load borne by each tower connection point; Indicates in arrive The average load borne by each tower connection point within a given time period; Indicates in arrive The standard deviation of the load borne by each tower connection point within a time period; Represents the load factor function The weighting coefficients in the text; Indicates the factors affecting relative displacement; express Time of the first The relative displacement change at each tower flange; Indicates in arrive The average value of the relative displacement changes at all tower flanges within the time period; Indicates in arrive The standard deviation of the relative displacement changes at all tower flanges within the time period; Indicates the factors affecting the relative angle change on the upper side of the flange; express Time of the first The relative angle change on the upper side of the flange at each tower flange; express Time of the first The average parameter of the relative angle change on the upper side of the flange at each tower flange; express Time of the first The standard deviation parameter of the relative angle change on the upper side of the flange at each tower flange; Indicates the factors affecting the relative angle change on the lower side of the flange; express Time of the first The relative angle change of the lower side of the flange at each tower flange; express Time of the first The average parameter of the relative angle change of the lower side of the flange at each tower flange; express Time of the first The standard deviation parameter of the relative angle change on the lower side of the flange at each tower flange.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the wind turbine tower instability tendency assessment method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the wind turbine tower instability tendency assessment method according to any one of claims 1 to 6.

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