Monitoring and early-warning apparatus for clearance between blade root and spinner of wind turbine generator set

Abstract

A monitoring and early-warning apparatus for a clearance between a blade root and a spinner of a wind turbine generator set. The apparatus comprises a multi-modal sensor, a data processing device, an early-warning device and a communication unit, wherein the multi-modal sensor comprehensively monitors an environmental condition and a mechanical state; the data processing device establishes a temperature impact model, a vibration impact model and a material fatigue impact model, and analyzes the impacts of these factors on a clearance between a blade root and a spinner; the early-warning device determines an ideal clearance value on the basis of historical clearance data, calculates a predicted clearance value on the basis of an analysis result, and compares the ideal clearance value with the predicted clearance value to obtain the difference therebetween, so as to generate an early-warning signal; and the communication unit is responsible for data and signal transmission. The apparatus improves the safety, reliability and operation efficiency of a wind turbine generator set, also reduces the maintenance costs, and enhances the data analysis capability and the level of intelligence.

Description

A wind turbine blade root guide cover gap monitoring and early warning device Technical Field

[0001] The present invention relates to the field of wind power generation equipment maintenance and safety technology, and in particular to a wind turbine blade root shroud gap monitoring and early warning device. Background Art

[0002] With the growing global demand for renewable energy, wind power generation has developed rapidly as an important form of clean energy. Wind turbines are usually installed in open areas or at sea to fully utilize wind energy resources. However, these environments are often accompanied by extreme weather conditions and complex mechanical movements, which can have a significant impact on the performance and reliability of wind turbines. In wind turbines, the gap between the root of the blade and the shroud is one of the most critical design parameters. Proper gaps can ensure that the blades do not collide with the shroud when rotating, thereby avoiding mechanical damage and potential safety accidents. However, over time and changes in the working environment, this gap may change, leading to increased safety risks.

[0003] Existing monitoring methods often rely on manual inspection or simple sensors, which makes it difficult to monitor and accurately predict the changing trend of the gap between the blade and the guide cover in real time.

[0004] Summary of the Invention

[0005] The present invention provides a wind turbine blade root guide cover gap monitoring and early warning device to solve the problems of incomplete monitoring, inaccurate prediction and untimely early warning in the prior art.

[0006] The present invention provides a wind turbine blade root guide cover gap monitoring and early warning device, comprising:

[0007] Multimodal sensors for comprehensive monitoring of environmental conditions and mechanical status of wind turbines;

[0008] a data processing device connected to the multimodal sensor, pre-establishing a temperature influence model, a vibration influence model, and a material fatigue influence model, substituting data monitored by the multimodal sensor into the models, analyzing the degree of influence of temperature changes, vibration changes, and fatigue conditions on the distance between the blade root and the shroud, and generating analysis results;

[0009] an early warning device connected to the multimodal sensor and the data processing device, and configured to determine an ideal spacing value at a next moment based on historical spacing data; establish a comprehensive calculation model based on the analysis results to calculate a predicted spacing value; determine a difference between the predicted spacing value and the ideal spacing value, and generate an early warning signal accordingly;

[0010] A communication unit is connected to the multimodal sensor, the data processing device and the early warning device to realize the transmission of data and signals.

[0011] According to a wind turbine blade root shroud gap monitoring and early warning device provided by the present invention, the multimodal sensor includes:

[0012] A temperature sensor is used to obtain current temperature data at the gap between the blade root and the shroud;

[0013] A vibration sensor for recording vibration amplitude, vibration frequency, and phase angle at the blade root;

[0014] A timing unit connected to the vibration sensor and used to calculate the time difference between the moment corresponding to the maximum vibration amplitude during the last vibration and the current moment;

[0015] A laser rangefinder is used to obtain the real-time and initial distance values ​​between the blade root and the shroud, and to obtain the contact length between the blade root and the shroud;

[0016] The material parameter acquisition unit is used to record the linear expansion coefficient of the blade root material, the maximum stress level experienced by the material in each cycle, the stress-life curve of the material, the fatigue limit stress level of the material, and the spacing reduction per unit cycle number.

[0017] According to a wind turbine blade root shroud gap monitoring and early warning device provided by the present invention, the data processing device includes:

[0018] a temperature impact analysis unit connected to the temperature sensor, the laser rangefinder, and the material parameter acquisition unit, substituting current temperature data, the contact length between the blade root and the shroud, and the linear expansion coefficient into the temperature impact model to calculate the length change ΔL caused by the temperature change;

[0019] The vibration impact analysis unit is connected to the vibration sensor and the timing unit, and substitutes the maximum vibration amplitude, vibration frequency, phase angle and time difference into the vibration impact model to calculate the distance change Δd caused by the vibration. v ;

[0020] The material fatigue effect analysis unit is connected to the material parameter acquisition unit, and substitutes the maximum stress level, the material's stress-life curve, the fatigue limit stress level, and the spacing reduction under the unit cycle number into the material fatigue effect model to calculate the spacing change Δd caused by material fatigue. f ;

[0021] A summary output unit is connected to the temperature impact analysis unit, the vibration impact analysis unit, and the material fatigue impact analysis unit to summarize the length change ΔL and the spacing change Δd caused by vibration. v , Spacing change Δd caused by material fatigue f , generate analysis results.

[0022] According to a wind turbine blade root guide cover gap monitoring and early warning device provided by the present invention, the temperature influence model is:

[0023] ΔL=L0*α*ΔT

[0024] Where ΔL is the length change caused by temperature change, L0 is the contact length between the blade root and the shroud, α is the linear expansion coefficient, and ΔT is the temperature difference between the current temperature data and the ideal operating temperature.

[0025] According to a wind turbine blade root shroud gap monitoring and early warning device provided by the present invention, the vibration impact model is:

[0026] Where Δd v is the distance change caused by vibration, A v is the maximum vibration amplitude during the last vibration process, 2πf is the vibration angular frequency, f is the vibration frequency, and t is the maximum vibration amplitude A v The time difference between the corresponding moment and the current moment, is the phase angle.

[0027] According to a wind turbine blade root shroud gap monitoring and early warning device provided by the present invention, the material fatigue influence model is:

[0028] Where Δd f is the spacing change caused by material fatigue, C and m are material characteristic parameters obtained based on the stress-life curve, σ is the maximum stress level experienced by the material in each cycle, σf is the fatigue limit stress level of the material, and Δd D is the spacing reduction per unit cycle number, and D is the spacing reduction Δd D The corresponding number of unit cycles.

[0029] According to the present invention, a wind turbine blade root guide cover gap monitoring and early warning device is provided, the early warning device comprising:

[0030] a threshold analysis unit connected to the laser rangefinder, pre-establishing a threshold analysis model, substituting the historical real-time spacing value of the gap between the blade root and the shroud into the threshold analysis model to generate a dynamic threshold range;

[0031] The threshold analysis model is T(t) i =μ(t)+K i *ε(t); where K i is the empirical coefficient corresponding to level i, μ(t) is the average value of the real-time spacing between the blade root and the shroud in history, ε(t) is the standard deviation of the real-time spacing between the blade root and the shroud in history, T(t) i is the dynamic threshold corresponding to level i; the dynamic threshold T(t) of each level i Summarize and generate the dynamic threshold range;

[0032] A comprehensive analysis unit, connected to the summary output unit, is used to substitute the analysis results into the comprehensive calculation model to obtain a predicted spacing value through calculation;

[0033] The comprehensive calculation model is G(t)=G0+ΔL+Δd v -Δd f ; Where G(t) is the predicted spacing value, G0 is the initial spacing value, ΔL is the length change caused by temperature change, Δd v is the distance change caused by vibration, Δd f is the spacing change caused by material fatigue;

[0034] The comparison and judgment unit is connected to the threshold analysis unit and the comprehensive analysis unit, and is used to compare the predicted interval value G(t) with the dynamic threshold range, and generate a warning signal of the matching level i based on the comparison result.

[0035] According to a wind turbine blade root shroud gap monitoring and early warning device provided by the present invention, the communication unit adopts low-power wide area network technology to maintain stable transmission.

[0036] A wind turbine blade root shroud gap monitoring and early warning device provided by the present invention further includes:

[0037] A cloud service platform connected to the communication unit, configured to record and store various types of data transmitted by the communication unit during historical transmissions, and to issue corresponding alarms based on the level i of the warning signal;

[0038] The management personnel can obtain historical spacing information and warning information by accessing the cloud service platform to assist the management personnel in formulating maintenance plans.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] By continuously monitoring the gap between the blade root and the guide cover, abnormal conditions can be detected in time to prevent collision or friction caused by too small a gap, thereby avoiding equipment damage or failure.

[0041] Through accurate prediction and early warning mechanisms, preventive maintenance can be achieved, avoiding unnecessary over-repairs while reducing the cost of emergency repairs;

[0042] Achieve rapid response to environmental changes (such as temperature) and mechanical conditions (such as vibration) to ensure that wind turbines operate in optimal conditions;

[0043] Data collected by multimodal sensors, combined with data analysis algorithms, can provide a deeper understanding of the operating status of wind turbines. It also provides specific quantitative indicators of how factors such as temperature, vibration, and material fatigue affect the gap between the blade root and the shroud, providing data support for future design improvements.

[0044] The use of automated data processing and early warning systems reduces the need for manual intervention and improves the intelligence level of the system; it can achieve simultaneous management of multiple wind turbines through remote monitoring, facilitating centralized control and scheduling;

[0045] In summary, the wind turbine blade root guide cover gap monitoring and early warning device not only improves the safety and reliability of wind turbine generator sets, but also reduces maintenance costs, improves operating efficiency, and enhances data analysis capabilities and intelligence levels.

[0046] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] FIG1 is a schematic structural diagram of a wind turbine blade root shroud gap monitoring and early warning device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0051] Example 1:

[0052] An embodiment of the present invention provides a wind turbine blade root shroud gap monitoring and early warning device, as shown in FIG1 , comprising:

[0053] Multimodal sensors for comprehensive monitoring of environmental conditions and mechanical status of wind turbines;

[0054] A data processing device is connected to the multimodal sensor, and pre-establishes a temperature influence model, a vibration influence model, and a material fatigue influence model. The data monitored by the multimodal sensor is substituted into the model to analyze the degree of influence of temperature changes, vibration changes, and fatigue conditions on the distance between the blade root and the shroud, and generate analysis results.

[0055] The early warning device is connected to the multimodal sensor and data processing equipment to determine the ideal spacing value at the next moment based on historical spacing data; establish a comprehensive calculation model based on the analysis results to calculate the predicted spacing value; determine the difference between the predicted spacing value and the ideal spacing value, and generate a warning signal accordingly;

[0056] The communication unit is connected to the multimodal sensor, the data processing device and the early warning device to realize the transmission of data and signals.

[0057] The principle of the above embodiment is: data is obtained through sensors installed in different positions. These sensors can be temperature sensors, accelerometers (for measuring vibration), stress sensors, etc.; mathematical modeling methods are used to convert sensor data into quantitative indicators of the impact of factors such as temperature, vibration, material fatigue, etc. on gap changes; the deviation between the predicted spacing value and the ideal spacing value obtained by comparative analysis results is determined to determine whether an early warning signal needs to be issued; and wired or wireless communication technology is used to achieve effective information exchange within the entire system.

[0058] The beneficial effects of the above embodiments are: being able to detect potential problems in a timely manner and prevent accidents caused by abnormal contact between the blades and the shroud; by monitoring the fatigue condition of the material, maintenance can be carried out in advance, reducing unplanned downtime and extending the service life of the equipment; by precisely controlling the gap between the blade root and the shroud, aerodynamic losses can be reduced and the overall efficiency of the wind turbine can be improved; avoiding the increase in maintenance costs caused by failures and reducing the additional energy consumption caused by inefficiency.

[0059] To further optimize the above embodiment, the multimodal sensor includes:

[0060] A temperature sensor is used to obtain current temperature data at the gap between the blade root and the shroud;

[0061] A vibration sensor for recording vibration amplitude, vibration frequency, and phase angle at the blade root;

[0062] A timing unit connected to the vibration sensor for calculating the time difference between the moment of the last maximum vibration amplitude and the current moment;

[0063] A laser rangefinder is used to obtain the real-time and initial distance values ​​between the blade root and the shroud, and to obtain the contact length between the blade root and the shroud;

[0064] The material parameter acquisition unit is used to record the linear expansion coefficient of the blade root material, the maximum stress level experienced by the material in each cycle, the stress-life curve of the material, the fatigue limit stress level of the material, and the spacing reduction per unit cycle number.

[0065] It should be noted that the temperature sensor monitors the temperature at the gap between the blade root and the shroud to evaluate the impact of temperature changes on the gap; usually, the temperature is measured using technologies such as thermocouples, thermistors or infrared sensors;

[0066] The vibration sensor records the vibration of the blade root, including the amplitude, frequency, and phase angle, to assess the impact of the vibration on the gap between the blade root and the shroud. An accelerometer or vibration sensor is generally used to obtain vibration characteristics by detecting the amplitude, frequency, and phase of the vibration signal.

[0067] The timing unit is connected to the vibration sensor and calculates the time difference from the last recorded maximum vibration amplitude to the present. By recording the timestamp, the time interval between the two maximum vibration events is determined, which helps to analyze the periodicity and stability of the vibration pattern.

[0068] The laser rangefinder measures the distance between the blade root and the shroud in real time, including the real-time and initial distance values, and records the maximum contact length between the two. It uses laser pulse distance measurement technology to calculate the distance by emitting and receiving reflected laser pulses.

[0069] The material parameter acquisition unit records the relevant physical properties of the blade root material, including but not limited to the linear expansion coefficient, the maximum stress level in each cycle, the stress-life curve, the fatigue limit stress level, and the amount of clearance reduction after each cycle; the material parameters are obtained through experimental measurement or reference to material manuals, etc. These parameters help to establish a mathematical model of the effects of temperature, vibration and fatigue on clearance.

[0070] In order to further optimize the above embodiment, the data processing device includes:

[0071] The temperature impact analysis unit is connected to the temperature sensor, laser rangefinder, and material parameter acquisition unit. It substitutes the current temperature data, the contact length between the blade root and the shroud, and the linear expansion coefficient into the temperature impact model to calculate the length change ΔL caused by the temperature change.

[0072] The vibration impact analysis unit is connected to the vibration sensor and timing unit, and substitutes the maximum vibration amplitude, vibration frequency, phase angle and time difference into the vibration impact model to calculate the distance change Δd caused by the vibration. v ;

[0073] The material fatigue effect analysis unit is connected to the material parameter acquisition unit, and the maximum stress level, the material's stress-life curve, the fatigue limit stress level, and the spacing reduction under the unit cycle number are substituted into the material fatigue effect model to calculate the spacing change Δd caused by material fatigue. f ;

[0074] The summary output unit is connected to the temperature impact analysis unit, vibration impact analysis unit, and material fatigue impact analysis unit to summarize the length change ΔL and the spacing change Δd caused by vibration v , Spacing change Δd caused by material fatigue f , generate analysis results;

[0075] Among them, the temperature influence model is:

[0076] ΔL=L0*α*ΔT

[0077] Where ΔL is the length change caused by temperature change, L0 is the contact length between the blade root and the shroud, α is the linear expansion coefficient, and ΔT is the temperature difference between the current temperature data and the ideal operating temperature.

[0078] It's important to note that calculating the length change caused by temperature fluctuations can accurately assess the impact of temperature changes on the clearance between the blade root and the shroud. Temperature change is a key factor affecting clearance, and accurately calculating this temperature-induced length change helps improve the accuracy and reliability of early warnings. Understanding the specific length change caused by temperature fluctuations can help develop more appropriate maintenance plans, such as adjusting operating strategies or optimizing cooling systems.

[0079] Among them, the vibration impact model is:

[0080] Where Δd v is the distance change caused by vibration, A v is the maximum vibration amplitude during the last vibration process, 2πf is the vibration angular frequency, f is the vibration frequency, and t is the maximum vibration amplitude A v The time difference between the corresponding moment and the current moment, is the phase angle.

[0081] It should be noted that the vibration impact model describes how the distance variation caused by vibration changes over time. Describes the periodic change of the pitch variation over time. The value of the sine function fluctuates between {-1, 1}, introducing the amplitude A v After that, the range of the spacing variation becomes {A v , -A v};over time, The value of will change, reflecting the dynamic change of the spacing variation over time; if there is an initial phase offset, it can be changed by changing to adjust the starting point of the sine waveform.

[0082] Among them, the material fatigue influence model is:

[0083] Where Δd f is the spacing change caused by material fatigue, C and m are material characteristic parameters obtained based on the stress-life curve, σ is the maximum stress level experienced by the material in each cycle, σf is the fatigue limit stress level of the material, and Δd D is the spacing reduction per unit cycle number, and D is the spacing reduction Δd D The corresponding number of unit cycles.

[0084] It should be noted that the material characteristic parameters C and m are extracted from the material's stress-life curve (SN curve). They are used to describe the fatigue behavior of the material under different stress levels. Usually, m represents the material fatigue index, and C is a proportional constant. It indicates the ratio of the actual stress level to the fatigue limit stress level. When the actual stress level is close to or exceeds the fatigue limit stress level, the material is more likely to suffer fatigue damage. represents the reduction in the distance between the blade root and the shroud per unit number of cycles, and D represents the number of cycles corresponding to this reduction; therefore, the material fatigue impact analysis unit can more accurately evaluate the impact of material fatigue on the distance between the blade root and the shroud, thereby improving the accuracy and reliability of the early warning system.

[0085] To further optimize the above embodiment, the early warning device includes:

[0086] A threshold analysis unit is connected to the laser rangefinder, pre-establishes a threshold analysis model, substitutes the historical real-time spacing value of the gap between the blade root and the shroud into the threshold analysis model, and generates a dynamic threshold range;

[0087] The threshold analysis model is T(t) i =μ(t)+K i *ε(t); where K iis the empirical coefficient corresponding to level i, μ(t) is the average value of the real-time spacing between the blade root and the shroud in history, ε(t) is the standard deviation of the real-time spacing between the blade root and the shroud in history, T(t) i is the dynamic threshold corresponding to level i; the dynamic threshold T(t) of each level i Summarize and generate dynamic threshold range;

[0088] A comprehensive analysis unit, connected to the summary output unit, is used to substitute the analysis results into the comprehensive calculation model to obtain the predicted spacing value through calculation;

[0089] The comprehensive calculation model is G(t)=G0+ΔL+Δd v -Δd f ; Where G(t) is the predicted spacing value, G0 is the initial spacing value, ΔL is the length change caused by temperature change, Δd v is the distance change caused by vibration, Δd f is the spacing change caused by material fatigue;

[0090] The comparison and judgment unit is connected to the threshold analysis unit and the comprehensive analysis unit, and is used to compare the predicted interval value G(t) with the dynamic threshold range, and generate a warning signal of the matching level i based on the comparison result.

[0091] It should be noted that the average value μ(t) and standard deviation ε(t) are calculated based on historical data. Then, according to different empirical coefficients K i Calculate the dynamic threshold T(t) at different levels i . Empirical coefficient K i It can be set based on actual needs and historical data analysis results to ensure that the threshold reflects the difference between normal operating range and potential abnormal conditions;

[0092] The predicted spacing value G(t) is calculated by comprehensively considering the initial spacing value G0, the length change caused by temperature changes, the spacing change caused by vibration, and the spacing change caused by material fatigue. The key here is to accurately measure and calculate these changes to ensure the accuracy of the prediction results.

[0093] Finally, the calculated predicted distance value G(t) is compared with the dynamic threshold range. If the predicted distance value exceeds the dynamic threshold range, a warning signal is triggered. The level of the warning signal depends on the degree of deviation between the predicted distance value and the dynamic threshold range.

[0094] In order to further optimize the above embodiment, the communication unit adopts low-power wide area network technology to maintain stable transmission.

[0095] It should be noted that low-power wide-area network technology (LPWAN) is a wireless network technology designed for long-distance, low-bandwidth, and low-power application scenarios. It is particularly suitable for Internet of Things (IoT) applications, in which devices may be located in remote areas or need to operate for long periods of time without battery replacement. Compared with traditional mobile communication technologies, LPWAN technology is lower in cost and more suitable for large-scale deployment. LPWAN technology generally has good anti-interference capabilities and can maintain stable communications in complex environments.

[0096] In order to further optimize the above embodiment, the following further aspects are included:

[0097] A cloud service platform, which is connected to the communication unit and is used to record and store various types of data transmitted during the communication unit's historical transmission process, and issue corresponding alarms based on the level i of the early warning signal;

[0098] Managers access the cloud service platform to obtain historical spacing information and warning information, which assists them in formulating maintenance plans. It should be noted that the cloud service platform needs to be closely integrated with the communication unit to ensure that all transmitted data is captured and stored in a timely manner; establish an efficient data management system to ensure the integrity, consistency and security of the data; develop automated scripts or services to process received warning signals and issue corresponding alerts based on the level; build an easy-to-use interface so that managers can quickly find the information they need, such as historical spacing data, warning records, etc.; implement access control policies to ensure that only authorized managers can access sensitive data;

[0099] Through the automated alarm system, potential problems can be responded to more quickly; managers can develop more effective maintenance plans based on rich historical data and early warning information; by discovering potential problems in advance, unplanned downtime and maintenance costs can be reduced; managers can monitor equipment status without having to be on site in person, saving time and resources.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A gap monitoring and warning device for a wind power blade root fairing, characterized in that, Including: A multi-modal sensor for comprehensively monitoring the environmental conditions and mechanical states of wind power equipment; A data processing device connected to the multi-modal sensor, pre-establishing a temperature influence model, a vibration influence model, and a material fatigue influence model, substituting the data monitored by the multi-modal sensor into the models, analyzing the influence degrees of temperature changes, vibration changes, and fatigue conditions on the distance between the blade root and the fairing, and generating an analysis result; An early warning device connected to the multi-modal sensor and the data processing device, determining the ideal distance value at the next moment based on historical distance data; Establishing a comprehensive calculation model based on the analysis result, calculating the predicted distance value; judging the difference between the predicted distance value and the ideal distance value, and correspondingly generating an early warning signal; A communication unit connected to the multi-modal sensor, the data processing device, and the early warning device for realizing the transmission of data and signals.

2. The gap monitoring and early warning device for a wind power blade root fairing according to claim 1, wherein The multi-modal sensor includes: A temperature sensor for obtaining the current temperature data at the gap between the blade root and the fairing; A vibration sensor for recording the vibration amplitude, vibration frequency, and phase angle at the blade root; A timing unit connected to the vibration sensor for calculating the time difference between the moment corresponding to the maximum vibration amplitude in the previous vibration process and the current moment; A laser rangefinder for obtaining the real-time distance value and the initial distance value at the gap between the blade root and the fairing, and obtaining the length that can be contacted between the blade root and the fairing; A material parameter acquisition unit for recording the linear expansion coefficient of the blade root material, the maximum stress level experienced by the material in each cycle, the stress-life curve of the material, the fatigue limit stress level of the material, and the distance reduction amount per unit cycle number.

3. The gap monitoring and early warning device for a wind power blade root fairing according to claim 2, characterized in that, The data processing device includes: A temperature influence analysis unit connected to the temperature sensor, the laser rangefinder, and the material parameter acquisition unit, substituting the current temperature data, the length that can be contacted between the blade root and the fairing, and the linear expansion coefficient into the temperature influence model to calculate the length change amount ΔL caused by temperature changes; A vibration influence analysis unit, which is connected to the vibration sensor and the timing unit, substitutes the maximum vibration amplitude, vibration frequency, phase angle, and time difference into the vibration influence model to calculate the change in spacing Δd caused by vibration v ; A material fatigue effect analysis unit, which is connected to the material parameter acquisition unit, substitutes the maximum stress level, the stress-life curve of the material, the fatigue limit stress level, and the amount of spacing reduction under a unit cycle number into the material fatigue effect model to calculate the amount of spacing change Δd caused by material fatigue f ; A summary output unit, which is connected to the temperature influence analysis unit, the vibration influence analysis unit, and the material fatigue influence analysis unit, is used to summarize the length change amount ΔL and the spacing change amount Δd caused by vibration v and the spacing change amount Δd caused by material fatigue f , and generate an analysis result.

4. The wind power blade root fairing gap monitoring and early warning device according to claim 3, characterized in that, The temperature influence model is: ΔL = L0 * α * ΔT Where, ΔL is the length change amount caused by temperature changes, L0 is the length that can be contacted between the blade root and the fairing, α is the linear expansion coefficient, and ΔT is the temperature difference between the current temperature data and the ideal operating temperature.

5. The gap monitoring and early warning device for a wind power blade root fairing according to claim 4, characterized in that, The vibration influence model is: where Δd v is the change in distance caused by vibration, A v is the maximum vibration amplitude during the previous vibration process, 2πf is the vibration angular frequency, f is the vibration frequency, and t is the time difference between the moment corresponding to the maximum vibration amplitude A v and the current moment. is the phase angle.

6. The gap monitoring and early warning device for the wind power blade root fairing according to claim 5, characterized in that The material fatigue influence model is as follows: where, Δd f is the amount of change in the spacing caused by material fatigue, C and m are material characteristic parameters obtained from the stress-life curve, σ is the maximum stress level experienced by the material in each cycle, σ f is the fatigue limit stress level of the material, Δd D is the amount of spacing reduction per unit cycle number, D is the unit cycle number corresponding to the spacing reduction amount Δd D corresponding to 7. The wind power blade root fairing gap monitoring and early warning device according to claim 6, characterized in that, The early warning device includes: A threshold analysis unit connected to the laser rangefinder, pre-establishing a threshold analysis model, and substituting the real-time distance value at the historical gap between the blade root and the fairing into the threshold analysis model to generate a dynamic threshold range; The threshold analysis model is T(t) i = μ(t) + K i * ε(t); where K i is the level The empirical coefficient corresponding to category i, μ(t) is the average value of the real-time spacing at the gap between the historical blade root and the fairing, ε(t) is the standard deviation of the real-time spacing at the gap between the historical blade root and the fairing, T(t) i is the dynamic threshold corresponding to level i; the dynamic thresholds T(t) for each level i are aggregated to generate the dynamic threshold range; A comprehensive analysis unit connected to the summary output unit for substituting the analysis result into the comprehensive calculation model to obtain the predicted distance value through calculation; The comprehensive calculation model is G(t) = G0 + ΔL + Δd v -Δd f ; where G(t) is the predicted spacing value, G0 is the initial spacing value, ΔL is the length change caused by temperature change, and Δd v is the spacing change caused by vibration, and Δd f is the spacing change caused by material fatigue; A comparison and judgment unit connected to the threshold analysis unit and the comprehensive analysis unit for comparing the predicted distance value G(t) with the dynamic threshold range and generating an early warning signal of the matching level i based on the comparison result.

8. The gap monitoring and early warning device for the wind turbine blade root fairing according to claim 7, characterized in that, The communication unit adopts low-power wide area network technology for stable transmission.

9. The gap monitoring and early warning device for a wind power blade root fairing according to claim 8, wherein Further included are: A cloud service platform, which is connected to the communication unit, is used to record and store various types of data transmitted during the historical transmission process of the communication unit, and issue corresponding alarms based on the level i of the warning signal; The management personnel obtain historical spacing information and warning information by accessing the cloud service platform, which helps the management personnel to work out a maintenance plan.

Images