Real-time monitoring system for blades of wind generating set

Abstract

The invention provides a real-time monitoring system for blades of a wind generating set, which belongs to the field of blade monitoring and comprises a monitoring module for acquiring a plurality of parameters of the blades of the wind generating set and determining the real-time light transmittance of the blades of the wind generating set monitored by a corresponding camera; the filtering module is used for acquiring light transmittance data in different time periods and detecting and filtering abnormal values; the analysis and prediction module is used for analyzing and predicting the running state of the leaf based on a machine learning algorithm of a cloud platform according to the filtered light transmittance data; the obtaining module is used for obtaining the health condition of the blade according to the running state and obtaining a repairing strategy for the blade of the wind generating set according to the health condition; and the display module is used for converting the repairing strategy of the blade of the wind generating set into a chart by using a data visualization tool and performing multi-angle display. The problem that the blade is damaged due to errors in judgment of the abnormal phenomenon of the blade caused by the fact that the running state of the fan blade cannot be obtained in real time is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of blade monitoring, and in particular to a real-time monitoring system for blades of a wind turbine generator set. Background Art

[0002] With the continuous development of wind power generation technology, wind power generation has become an important part of the renewable energy field. In wind power generation, wind turbines play an important role.

[0003] However, as the capacity of traditional wind turbines continues to increase, the size of wind turbine blades is also getting longer and longer. At the same time, in harsh environments, a large resistance is generated between the wind turbine blades and the data receiver, which affects the signal output of the data receiver, causing large errors in the monitoring data and making it impossible to obtain the operating status of the wind turbine blades in real time, which in turn leads to errors in the judgment of abnormal phenomena of the blades and damage to the blades.

[0004] Therefore, the present invention proposes a real-time monitoring system for blades of a wind turbine generator set. Summary of the invention

[0005] The present invention provides a real-time monitoring system for wind turbine blades, which is used to solve the problem in the prior art that under harsh environments, large resistance is generated between the wind turbine blades and a data receiver, thereby affecting the signal output of the data receiver, causing large errors in the monitoring data, and failing to obtain the operating status of the wind turbine blades in real time, which in turn leads to errors in the judgment of abnormal phenomena of the blades, causing damage to the blades.

[0006] In one aspect, the present invention provides a real-time monitoring system for blades of a wind turbine generator set, comprising:

[0007] Monitoring module: acquiring multiple parameters of the blades of the wind turbine generator set, and determining the corresponding camera to monitor the real-time light transmittance of the blades of the wind turbine generator set according to the multiple parameters;

[0008] Filtering module: acquiring transmittance data in different time periods, and performing abnormal value detection and filtering on the transmittance data;

[0009] Analysis and prediction module: Analyze and predict the operating status of the blades based on the filtered transmittance data and the machine learning algorithm of the cloud platform;

[0010] Acquisition module: acquires the health status of the blades according to the operating status, and acquires a repair strategy for the blades of the wind turbine generator set according to the health status;

[0011] Display module: Use data visualization tools to convert the repair strategy of wind turbine blades into charts and display them from multiple angles.

[0012] According to the present invention, a real-time monitoring system for blades of a wind turbine generator set is provided, and the monitoring module includes:

[0013] A first determination unit: acquiring a structure of a blade of a wind turbine generator set, and determining a plurality of parameters of the blade of the wind turbine generator set based on a blade database according to the structure;

[0014] A second determining unit: determining a corresponding camera according to the multiple parameters;

[0015] The first acquisition unit is used to acquire key points and key areas of the blades of the wind turbine generator set;

[0016] Monitoring unit: monitors the real-time light transmittance of the blades of the wind turbine generator set based on the key points and key areas and the corresponding cameras.

[0017] According to the present invention, a wind turbine blade real-time monitoring system, a filtering module, comprises:

[0018] The third determination unit: determines the normal range and expected value of the blade light transmittance according to the target demand of the wind turbine generator set and the historical data of the blade light transmittance;

[0019] A definition unit: defining abnormal light transmittance parameter detection rules according to the normal range and expected value of the light transmittance of the leaf;

[0020] Cluster analysis unit: performing cluster analysis on the real-time light transmittance based on the abnormal light transmittance parameter detection rule and the machine learning algorithm;

[0021] Filtering unit: detects and filters outliers based on the analysis results.

[0022] According to the present invention, a real-time monitoring system for blades of a wind turbine generator set, an analysis and prediction module, comprises:

[0023] Normalization processing unit: normalizes the filtered transmittance data according to the filter;

[0024] Dimensionality reduction and extraction unit: Dimensionality reduction and feature extraction of processed transmittance data based on principal component analysis;

[0025] Analysis and prediction unit: Analyzes and predicts the operating status of the blades based on the machine learning algorithm of the cloud platform according to the dimension reduction and features.

[0026] According to the present invention, a wind turbine blade real-time monitoring system, an acquisition module, includes:

[0027] A fourth determining unit: acquiring operating information according to the operating status, and determining the working status of the blades of the wind turbine generator set according to the operating information;

[0028] A fifth determining unit: determining a plurality of working parameters according to the working state;

[0029] A sixth determining unit: analyzing the working parameters and determining the health status of the blades of the wind turbine generator set according to the analysis results;

[0030] A judgment unit: judging the health status of the health condition according to the leaf health database to determine the abnormality to different degrees;

[0031] The second acquisition unit is used to acquire a repair strategy for the blades of the wind turbine generator set according to the judgment result.

[0032] According to the present invention, a real-time monitoring system for blades of a wind turbine generator set is provided, and a display module includes:

[0033] A third acquisition unit is used to acquire a plurality of data related to the repair strategy;

[0034] The seventh determination unit: obtaining the target requirements and display scenarios of the display, and determining the corresponding data visualization tools according to the target requirements and display scenarios of the display;

[0035] A conversion unit: importing the plurality of data into a data visualization tool, and converting the data into a chart form according to data processing and analysis capabilities;

[0036] Display unit: Display charts from multiple angles based on the various interactive functions provided by data visualization tools.

[0037] A real-time monitoring system for blades of a wind turbine generator set provided by the present invention further includes:

[0038] Determination module: determining the current geographical location of the target wind turbine blade according to the longitude and latitude, and determining the various directions of the current geographical location using a compass;

[0039] Judgment module: marking each direction on the base of the weather vane, and judging the wind direction of the current geographical location according to the wind direction sensor;

[0040] A conversion module: determining the rotation of the sensor impeller based on the wind direction, obtaining the rotation information, and converting the rotation information into an electrical signal based on pulse width modulation;

[0041] Adjustment module: transmits the electrical signal to the processor for processing, and adjusts the speed and direction of the impeller according to the processing result.

[0042] According to a real-time monitoring system for blades of a wind turbine generator set provided by the present invention, a sixth determination unit includes:

[0043] The first determination subunit determines the load parameters of the blade at different rotation heights according to the working parameters;

[0044] A second determination subunit: determining a load deviation distribution of the blade according to load parameters of the blade at different rotation heights;

[0045] The third determination subunit is configured to determine a load consistency coefficient of the blade according to the load deviation distribution of the blade, and determine a structural damage condition of the blade according to the load consistency coefficient;

[0046] The fourth determination subunit: determines the quality health index of the blade according to the structural damage of the blade, and determines the rotation speed information of the blade according to the working parameters;

[0047] Extraction subunit: Determine the noise information of the blade during rotation according to the rotation speed information and material parameters of the blade, and extract the noise characteristic parameters of the blade at different angles and heights according to the noise information;

[0048] The fifth determination subunit determines the spatial distribution law of the noise characteristic parameters of the blade according to the noise characteristic parameters of the blade at different angles and heights;

[0049] The sixth determination subunit: determines the change curve of the spatial spectrum energy amplitude of the blade according to the spatial distribution law of the noise characteristic parameters of the blade;

[0050] Evaluation subunit: determining the noise characteristic state variable interval of the blade according to the spatial spectrum energy amplitude change curve of the blade, and evaluating the abnormal rotation speed of the blade according to the noise characteristic state variable interval;

[0051] A seventh determination subunit: determining a performance health index of the blade based on the abnormal rotation speed of the blade, and determining a response resonance modal parameter of the blade under the working parameters;

[0052] Acquisition subunit: acquiring the response health index of the blade according to the response resonance modal parameters and the standard resonance modal parameters of the blade under the working parameters;

[0053] The eighth determination subunit: determines the comprehensive health index of the blade by weighted calculation based on the quality health index, performance health index and reaction health index of the blade.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] According to multiple parameters of the wind turbine blades, the corresponding camera is determined to monitor the real-time light transmittance of the wind turbine blades, and abnormal values ​​are filtered. The operating status of the blades is predicted based on the filtered data, so as to obtain the health status of the blades and implement corresponding repair strategies. This can improve data reception and make the monitoring data more accurate. At the same time, it can obtain the operating status of the wind turbine blades in real time, thereby enhancing the judgment of abnormal phenomena of the blades and reducing the chance of damage to the blades. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 is a structural schematic diagram of a real-time monitoring system for blades of a wind turbine generator set provided by an embodiment of the present invention;

[0058] Figure 2 It is a schematic diagram of the structure of the monitoring module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1:

[0061] The embodiment of the present invention provides a real-time monitoring system for blades of a wind turbine generator set, such as Figure 1 As shown, the system mainly includes the following modules:

[0062] Monitoring module: acquiring multiple parameters of the blades of the wind turbine generator set, and determining the corresponding camera to monitor the real-time light transmittance of the blades of the wind turbine generator set according to the multiple parameters;

[0063] Filtering module: acquiring transmittance data in different time periods, and performing abnormal value detection and filtering on the transmittance data;

[0064] Analysis and prediction module: Analyze and predict the operating status of the blades based on the filtered transmittance data and the machine learning algorithm of the cloud platform;

[0065] Acquisition module: acquires the health status of the blades according to the operating status, and acquires a repair strategy for the blades of the wind turbine generator set according to the health status;

[0066] Display module: Use data visualization tools to convert the repair strategy of wind turbine blades into charts and display them from multiple angles.

[0067] In this embodiment, the multiple parameters of the wind turbine blades include: size, shape, and material.

[0068] In this embodiment, corresponding cameras can be selected according to multiple parameters. For example, for blade size and shape, a camera with high resolution and high imaging quality can be selected; for blade material and manufacturing process, a camera with high performance and durability can be selected; for blade service life, a camera with long life and high reliability can be selected.

[0069] In this embodiment, the light transmittance of the blades of the wind turbine generator set refers to the ratio of the blades allowing light to pass through. The higher the light transmittance, the smaller the absorption loss of the blades, and the higher the efficiency of converting into electrical energy.

[0070] In this embodiment, the light transmittance data includes:

[0071] Light transmittance: It is an indicator that describes the degree of penetration of light into a material, usually expressed as a percentage.

[0072] Light absorbance: It is a measure of the degree to which light is absorbed in a material, usually expressed as a ratio of light intensity, that is, the ratio of the absorption coefficient to the intensity of the incident light.

[0073] Light scattering: refers to the intensity distribution of light in all directions when light passes through a medium.

[0074] In this embodiment, the operating status of the blade includes: normal operation and abnormal operation, which can be determined by position information, speed information, attitude information, temperature information and rotation sound.

[0075] In this embodiment, the repair strategy for the blades of the wind turbine generator set includes:

[0076] Material selection: When repairing blades, appropriate materials need to be selected to ensure that the repaired blades have good weather resistance, wind resistance and mechanical strength. Commonly used materials include carbon fiber composites, glass fiber composites, etc.

[0077] Repair process: According to the different types and damage degrees of blades, corresponding repair processes are adopted. Common repair methods include physical air knife cutting, chemical cleaning, thermal spraying, cold spraying, etc.

[0078] Repair quality control: After the repair is completed, the blades should be strictly inspected to ensure that the repaired blades meet the design requirements and achieve the expected use effect. At the same time, the use of the blades should be monitored regularly to detect potential problems in time and replace or repair them.

[0079] In this embodiment, the data visualization tool is a software that presents data in a visual form, such as Tableau and Power BI.

[0080] In this embodiment, the data visualization tool displays data from multiple angles so that users can observe and understand data from different angles and perspectives. For example: Power BI: supports viewing data in multiple ways such as drilling, slicing, and scrolling. In addition, multiple dimensions and measures can be set in the data model to achieve more flexible data visualization.

[0081] The beneficial effects of the above technical solution are: determining the corresponding camera to monitor the real-time light transmittance of the wind turbine blades according to multiple parameters of the wind turbine blades, and filtering out abnormal values, predicting the operating status of the blades based on the filtered data, thereby obtaining the health status of the blades, and implementing corresponding repair strategies, which can improve data reception and make the monitoring data more accurate. At the same time, it can obtain the operating status of the wind turbine blades in real time, thereby enhancing the judgment of abnormal phenomena of the blades and reducing the chance of damage to the blades.

[0082] Embodiment 2:

[0083] Based on Example 1, the monitoring module of the embodiment of the present invention includes:

[0084] A first determination unit: acquiring a structure of a blade of a wind turbine generator set, and determining a plurality of parameters of the blade of the wind turbine generator set based on a blade database according to the structure;

[0085] A second determining unit: determining a corresponding camera according to the multiple parameters;

[0086] The first acquisition unit is used to acquire key points and key areas of the blades of the wind turbine generator set;

[0087] Monitoring unit: monitors the real-time light transmittance of the blades of the wind turbine generator set based on the key points and key areas and the corresponding cameras.

[0088] In this embodiment, the structure of the wind turbine blade includes:

[0089] Blade design: Blades are usually made of composite materials (such as carbon fiber, glass fiber, Kevlar fiber, etc.).

[0090] Blade shape: Blades are usually curved or trapezoidal to maximize airflow capture. The leading edge of the blade is usually slightly curved to reduce resistance as the air flows around the blade.

[0091] Connection between blades and rotor: The blades are connected to the rotor through a series of bolts, which ensure that the blades remain in a stable position when rotating at high speeds.

[0092] In this embodiment, the blade database is a collection of detailed information about wind turbine blades, including information such as manufacturer, model, size, material, number of blades, and installation method.

[0093] In this embodiment, the multiple parameters of the wind turbine blades include: size, shape, and material.

[0094] In this embodiment, the key points and key areas include: support points, bending points and aerodynamic performance.

[0095] In this embodiment, the light transmittance of the blades of the wind turbine generator set refers to the ratio of the blades allowing light to pass through. The higher the light transmittance, the smaller the absorption loss of the blades, and the higher the efficiency of converting into electrical energy.

[0096] The beneficial effects of the above technical solution are: determining multiple parameters of the blade according to the structure of the blade, thereby obtaining the corresponding camera, which can improve the clarity of the camera shooting, thereby improving the accuracy of data acquisition, and installing the camera at key points and key areas of the blade to monitor the light transmittance of the blade. It is possible to promptly detect cracks, fractures, and ice accumulation on the blade and take measures to avoid them, thereby extending the service life of the wind turbine. At the same time, by real-time monitoring of changes in the light transmittance of the blade, the operating parameters of the wind turbine, such as speed, pitch, etc., can be adjusted in time to ensure that the wind turbine always operates in the best working condition, thereby improving the utilization rate of wind energy.

[0097] Embodiment 3:

[0098] Based on Example 2, the filtering module of the embodiment of the present invention includes:

[0099] The third determination unit: determines the normal range and expected value of the blade light transmittance according to the target demand of the wind turbine generator set and the historical data of the blade light transmittance;

[0100] A definition unit: defining abnormal light transmittance parameter detection rules according to the normal range and expected value of the light transmittance of the leaf;

[0101] Cluster analysis unit: performing cluster analysis on the real-time light transmittance based on the abnormal light transmittance parameter detection rule and the machine learning algorithm;

[0102] Filtering unit: detects and filters outliers based on the analysis results.

[0103] In this embodiment, the target required blade transmittance of the wind turbine is usually set at 30%-45%. If the transmittance is lower than this range, it means that the blade surface reflects or absorbs too much light, which will cause mechanical failure and unstable operation. If the transmittance is higher than this range, it means that the blade material needs to be replaced or otherwise processed.

[0104] In this embodiment, the abnormal light transmission data detection rule may be a rule based on spectral analysis. When a blade is abnormal, its material may change, which may cause the spectrum of the blade to change. Therefore, spectral analysis technology may be used to detect abnormalities in the light transmission data of the blade. For example, a visible light spectrometer or an infrared spectrometer may be used to analyze the spectral changes on the blade surface.

[0105] In this embodiment, cluster analysis is an unsupervised learning method that can be used to classify similar objects into the same category. In the case of cluster analysis of real-time transmittance, we can classify similar transmittance data into the same category to find data in areas where there may be problems.

[0106] In this embodiment, the outlier refers to an abnormal value.

[0107] The beneficial effects of the above technical solution are: defining abnormal light transmittance data detection rules according to the normal range and expected value of leaf light transmittance, and performing cluster analysis on the real-time light transmittance to filter out abnormal values, thereby ensuring the validity and consistency of the data.

[0108] Embodiment 4:

[0109] Based on Example 3, the analysis and prediction module of the embodiment of the present invention includes:

[0110] Normalization processing unit: normalizes the filtered transmittance data according to the filter;

[0111] Dimensionality reduction and extraction unit: Dimensionality reduction and feature extraction of processed transmittance data based on principal component analysis;

[0112] Analysis and prediction unit: Analyzes and predicts the operating status of the blades based on the machine learning algorithm of the cloud platform according to the dimension reduction and features.

[0113] In this embodiment, the filter is a signal processing technology, which is mainly used to remove or reduce noise and interference in the signal.

[0114] In this embodiment, normalization is a data preprocessing method that converts raw data into data with the same scale or range to facilitate subsequent calculation and analysis. The main purpose of normalization is to eliminate the impact of dimensions between different variables so that each variable can be compared and analyzed in the same coordinate system.

[0115] In this embodiment, principal component analysis is a commonly used linear dimensionality reduction technique, which extracts the potential information of the data by converting multiple features in the original data into new unrelated features, namely principal components. In principal component analysis, each principal component corresponds to a straight line in the original data, and this straight line can explain most of the variance information in the data.

[0116] In this embodiment, the operating status of the blade includes: normal operation and abnormal operation, which can be determined by position information, speed information, attitude information, temperature information and rotation sound.

[0117] The beneficial effects of the above technical solution are: by normalizing the filtered data, performing dimensionality reduction and feature extraction, analyzing and predicting the operating status of the blades, the operating status of the blades can be obtained in real time, potential problems with the blades can be discovered in time, and accidents caused by blade failures can be avoided. At the same time, the working condition of each blade can be understood, so that the operation of the wind farm can be adjusted and managed according to the actual situation, thereby improving the operating efficiency of the wind turbine.

[0118] Embodiment 5:

[0119] Based on Example 4, the acquisition module of the embodiment of the present invention includes:

[0120] A fourth determining unit: acquiring operating information according to the operating status, and determining the working status of the blades of the wind turbine generator set according to the operating information;

[0121] A fifth determining unit: determining a plurality of working parameters according to the working state;

[0122] A sixth determining unit: analyzing the working parameters and determining the health status of the blades of the wind turbine generator set according to the analysis results;

[0123] A judgment unit: judging the health status of the health condition according to the leaf health database to determine the abnormality to different degrees;

[0124] The second acquisition unit is used to acquire a repair strategy for the blades of the wind turbine generator set according to the judgment result.

[0125] In this embodiment, the operation information of the blades of the wind turbine generator set includes:

[0126] The working state of the blade: such as vibration amplitude, frequency and other parameters.

[0127] Fatigue degree of blades: By regularly inspecting the blades, it is possible to assess whether the blades have damage such as fatigue cracks, and take timely measures to avoid blade damage.

[0128] Operating status of the fan: Understand the operating status of the fan, such as speed, output power and other parameters.

[0129] In this embodiment, the working states of the blades of the wind turbine generator set include: normal working and abnormal working.

[0130] In this embodiment, the working parameters include: vibration, temperature, and rotation amplitude of the blades.

[0131] In this embodiment, the health status of the blades of the wind turbine generator set includes: normal, good, poor, relatively poor, and need to be replaced.

[0132] In this embodiment, different degrees of abnormality refer to the degree of abnormal conditions such as fatigue cracks, fractures, and deformations of the blades.

[0133] In this embodiment, the blade health database is a system for storing and analyzing health information related to wind turbine blades, and can track the health status of the blades according to different abnormal conditions, such as vibration frequency, intensity, usage time, etc.

[0134] The beneficial effects of the above technical solution are: obtaining the health status of the blades according to the operating status, and classifying the health status to different degrees, and obtaining the repair strategy for the wind turbine blades according to the classification results, which can ensure the targetedness of the repair strategy and improve the safety of the wind turbine blades.

[0135] Embodiment 6:

[0136] Based on Example 5, the display module of the embodiment of the present invention includes:

[0137] A third acquisition unit is used to acquire a plurality of data related to the repair strategy;

[0138] The seventh determination unit: obtaining the target requirements and display scenarios of the display, and determining the corresponding data visualization tools according to the target requirements and display scenarios of the display;

[0139] A conversion unit: importing the plurality of data into a data visualization tool, and converting the data into a chart form according to data processing and analysis capabilities;

[0140] Display unit: Display charts from multiple angles based on the various interactive functions provided by data visualization tools.

[0141] In this embodiment, the data related to the repair strategy includes:

[0142] Blade damage types: including cracks, wear, breakage, etc.

[0143] Blade material: Different types of blades may use different materials, such as carbon fiber composites, glass fiber composites, etc.

[0144] Blade design parameters: including blade shape, size and weight.

[0145] Blade usage history: including operating time, usage frequency, and load conditions.

[0146] Environmental conditions: such as temperature, humidity, air pressure, etc.

[0147] Repair methods: including replacement of parts, coating, reinforcement, etc.

[0148] In this embodiment, the target requirement refers to what data the data visualization tool is used to display, in what form, and for what purpose.

[0149] In this embodiment, the display scene can be displayed on a PPT, a computer, or a mobile phone.

[0150] In this embodiment, the interactive functions include: filtering, sorting, slicing

[0151] The beneficial effects of the above technical solution are: determining the corresponding data visualization tool according to the target requirements and display scenarios, and converting multiple data of the repair strategy into charts. Through the interactive function of the visualization tool, multi-angle display can be performed, and the relevant data of the repair strategy can be understood and analyzed from different angles and levels, making the data more intuitive and easy to understand, which helps to understand the meaning and trends behind the data and improve the repair effect of the blades.

[0152] Embodiment 7:

[0153] Based on Example 6, the embodiment of the present invention further includes:

[0154] Determination module: determining the current geographical location of the target wind turbine blade according to the longitude and latitude, and determining the various directions of the current geographical location using a compass;

[0155] Judgment module: marking each direction on the base of the weather vane, and judging the wind direction of the current geographical location according to the wind direction sensor;

[0156] A conversion module: determining the rotation of the sensor impeller based on the wind direction, obtaining the rotation information, and converting the rotation information into an electrical signal based on pulse width modulation;

[0157] Adjustment module: transmits the electrical signal to the processor for processing, and adjusts the speed and direction of the impeller according to the processing result.

[0158] In this embodiment, longitude and latitude are basic tools used in geography to determine the position of a point on the surface of the earth, and are composed of two independent values, each of which represents a specific direction of the point.

[0159] In this embodiment, the wind vane is a meteorological instrument, mainly an anemometer used to measure and indicate wind direction.

[0160] In this embodiment, the wind direction sensor is a device that can sense the wind direction in the surrounding environment and convert it into a signal that can be recognized by people.

[0161] In this embodiment, the sensor impeller is a meteorological sensor, which is mainly used to measure parameters such as wind direction, wind speed and pressure in the atmosphere.

[0162] In this embodiment, the rotation information of the sensor impeller refers to rotation data related to wind speed, wind direction, etc. obtained by the sensor impeller.

[0163] In this embodiment, pulse width modulation is a common analog signal processing technology, which implements information transmission or control by periodically performing pulse width modulation on a signal.

[0164] The beneficial effects of the above technical solution are: determining the various orientations of the current geographical location of the blades of the target wind turbine generator set, and judging the wind direction of the current geographical location based on the wind direction sensor, thereby adjusting the speed and direction of the sensor impeller, which can reduce the energy loss caused by factors such as friction and air resistance, thereby improving the power output of the wind turbine, and at the same time, can reduce the stress and fatigue of the equipment and increase the life of the equipment.

[0165] Embodiment 8:

[0166] Based on Embodiment 7, a sixth determining unit in Embodiment 6 of the present invention includes:

[0167] The first determination subunit determines the load parameters of the blade at different rotation heights according to the working parameters;

[0168] A second determination subunit: determining a load deviation distribution of the blade according to load parameters of the blade at different rotation heights;

[0169] The third determination subunit is configured to determine a load consistency coefficient of the blade according to the load deviation distribution of the blade, and determine a structural damage condition of the blade according to the load consistency coefficient;

[0170] The fourth determination subunit: determines the quality health index of the blade according to the structural damage of the blade, and determines the rotation speed information of the blade according to the working parameters;

[0171] Extraction subunit: Determine the noise information of the blade during rotation according to the rotation speed information and material parameters of the blade, and extract the noise characteristic parameters of the blade at different angles and heights according to the noise information;

[0172] The fifth determination subunit determines the spatial distribution law of the noise characteristic parameters of the blade according to the noise characteristic parameters of the blade at different angles and heights;

[0173] The sixth determination subunit: determines the change curve of the spatial spectrum energy amplitude of the blade according to the spatial distribution law of the noise characteristic parameters of the blade;

[0174] Evaluation subunit: determining the noise characteristic state variable interval of the blade according to the spatial spectrum energy amplitude change curve of the blade, and evaluating the abnormal rotation speed of the blade according to the noise characteristic state variable interval;

[0175] A seventh determination subunit: determining a performance health index of the blade based on the abnormal rotation speed of the blade, and determining a response resonance modal parameter of the blade under the working parameters;

[0176] Acquisition subunit: acquiring the response health index of the blade according to the response resonance modal parameters and the standard resonance modal parameters of the blade under the working parameters;

[0177] The eighth determination subunit: determines the comprehensive health index of the blade by weighted calculation based on the quality health index, performance health index and reaction health index of the blade.

[0178] In this embodiment, the rotation height refers to the relative position of the blades when the blades rotate around the axis at a certain speed in a vertical plane in the wind turbine.

[0179] In this embodiment, the load parameters of the blade are physical quantities that describe the external forces and internal stress distribution borne by the blade, including: wind load parameters, temperature load parameters, deadweight load parameters, and hydrodynamic load parameters.

[0180] In this embodiment, the deviation distribution of blade load refers to the degree of difference between the load value borne by the blade and its design value under different working conditions. The load deviation can be a positive deviation (i.e., the actual load value is greater than the design value) or a negative deviation (i.e., the actual load value is less than the design value).

[0181] In this embodiment, the consistency coefficient of the blade load refers to the ratio of the maximum load to the average load of the blade at each loading frequency under a specific working condition. This ratio can be used to measure the uniformity of the force of the blade under various working conditions, and can also be used as an indicator for evaluating the fatigue strength of the blade.

[0182] In this embodiment, the structural damage of the blade includes: cracks, fatigue, deformation, and wear.

[0183] In this embodiment, the blade quality health index is a non-destructive testing indicator for evaluating the health status of the blade.

[0184] In this embodiment, the blade material parameters of the blade include: density, strength, stiffness, and heat resistance.

[0185] In this embodiment, the noise characteristic parameter is a physical quantity used to describe and quantify noise characteristics, and is used to evaluate the intensity, frequency range, waveform and other characteristics of the noise source, such as sound pressure level, frequency domain characteristics, phase, and energy distribution.

[0186] In this embodiment, the blade's spatial spectrum energy amplitude variation curve is a curve diagram used to describe the variation of the spectrum energy of the blade at different positions in space, and can reflect the sound energy distribution of the blade at different positions.

[0187] In this embodiment, the noise characteristic state variable is a numerical value used to describe the characteristics of the noise signal, and can reflect the energy distribution and phase information of the phenomenon on each frequency component.

[0188] In this embodiment, the response resonance modal parameter of the blade is a parameter used to describe the relationship between the blade vibration response and the system mode, which can reflect the vibration behavior and intensity of the blade in different modes, such as:

[0189] Natural Frequency: The lowest or highest frequency produced by a blade in its natural vibration mode.

[0190] Damping ratio: describes the degree of attenuation of blade vibration.

[0191] Critical speed: The lowest rotation speed at which the blade begins to produce significant vibration, corresponding to the first critical speed of the blade.

[0192] Second critical speed: When the blade resonates, the maximum amplitude reaches the maximum critical speed.

[0193] In this embodiment, the leaf response health index is used to detect and quantify the speed at which the leaves respond to environmental stimuli.

[0194] The beneficial effect of the above technical solution is that the health status of the blade is determined by the quality health index, performance health index and reaction health index of the blade, which can ensure the accuracy and comprehensiveness of the obtained results, and at the same time, targeted plans can be specified to ensure the efficiency of blade use.

[0195] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0196] 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 embodiments of the present invention.

Claims

1. A real-time monitoring system for blades of a wind turbine generator set, characterized in that: include: Monitoring module: acquiring multiple parameters of the blades of the wind turbine generator set, and determining the corresponding camera to monitor the real-time light transmittance of the blades of the wind turbine generator set according to the multiple parameters; Filtering module: acquiring transmittance data in different time periods, and performing abnormal value detection and filtering on the transmittance data; Analysis and prediction module: Analyze and predict the operating status of the blades based on the filtered transmittance data and the machine learning algorithm of the cloud platform; Acquisition module: acquires the health status of the blades according to the operating status, and acquires a repair strategy for the blades of the wind turbine generator set according to the health status; Display module: Use data visualization tools to convert the repair strategy of wind turbine blades into charts and display them from multiple angles.

2. The real-time monitoring system for blades of a wind turbine generator set according to claim 1, characterized in that: Monitoring modules, including: A first determination unit: acquiring a structure of a blade of a wind turbine generator set, and determining a plurality of parameters of the blade of the wind turbine generator set based on a blade database according to the structure; A second determining unit: determining a corresponding camera according to the multiple parameters; The first acquisition unit is used to acquire key points and key areas of the blades of the wind turbine generator set; Monitoring unit: monitors the real-time light transmittance of the blades of the wind turbine generator set based on the key points and key areas and the corresponding cameras.

3. The real-time monitoring system for blades of a wind turbine generator set according to claim 1, characterized in that: Filter modules, including: The third determination unit: determines the normal range and expected value of the blade light transmittance according to the target demand of the wind turbine generator set and the historical data of the blade light transmittance; A definition unit: defining abnormal light transmittance parameter detection rules according to the normal range and expected value of the light transmittance of the leaf; Cluster analysis unit: performing cluster analysis on the real-time light transmittance based on the abnormal light transmittance parameter detection rule and the machine learning algorithm; Filtering unit: detects and filters outliers based on the analysis results.

4. The real-time monitoring system for blades of a wind turbine generator set according to claim 1, characterized in that: Analysis and prediction module, including: Normalization processing unit: normalizes the filtered transmittance data according to the filter; Dimensionality reduction and extraction unit: Dimensionality reduction and feature extraction of processed transmittance data based on principal component analysis; Analysis and prediction unit: Analyzes and predicts the operating status of the blades based on the machine learning algorithm of the cloud platform according to the dimension reduction and features.

5. The real-time monitoring system for blades of a wind turbine generator set according to claim 1, characterized in that: Get modules, including: A fourth determining unit: acquiring operating information according to the operating status, and determining the working status of the blades of the wind turbine generator set according to the operating information; A fifth determining unit: determining a plurality of working parameters according to the working state; A sixth determining unit: analyzing the working parameters and determining the health status of the blades of the wind turbine generator set according to the analysis results; A judgment unit: judging the health status of the health condition according to the leaf health database to determine the abnormality to different degrees; The second acquisition unit is used to acquire a repair strategy for the blades of the wind turbine generator set according to the judgment result.

6. The real-time monitoring system for blades of a wind turbine generator set according to claim 1, characterized in that: Display module, including: A third acquisition unit is used to acquire a plurality of data related to the repair strategy; The seventh determination unit: obtaining the target requirements and display scenarios of the display, and determining the corresponding data visualization tools according to the target requirements and display scenarios of the display; A conversion unit: importing the plurality of data into a data visualization tool, and converting the data into a chart form according to data processing and analysis capabilities; Display unit: Display charts from multiple angles based on the various interactive functions provided by data visualization tools.

7. The real-time monitoring system for blades of a wind turbine generator set according to claim 1, characterized in that: Also includes: Determination module: determining the current geographical location of the target wind turbine blade according to the longitude and latitude, and determining the various directions of the current geographical location using a compass; Judgment module: marking each direction on the base of the weather vane, and judging the wind direction of the current geographical location according to the wind direction sensor; A conversion module: determining the rotation of the sensor impeller based on the wind direction, obtaining the rotation information, and converting the rotation information into an electrical signal based on pulse width modulation; Adjustment module: transmits the electrical signal to the processor for processing, and adjusts the speed and direction of the impeller according to the processing result.

8. The real-time monitoring system for blades of a wind turbine generator set according to claim 5, characterized in that: The sixth determining unit comprises: The first determination subunit determines the load parameters of the blade at different rotation heights according to the working parameters; A second determination subunit: determining a load deviation distribution of the blade according to load parameters of the blade at different rotation heights; The third determination subunit is configured to determine a load consistency coefficient of the blade according to the load deviation distribution of the blade, and determine a structural damage condition of the blade according to the load consistency coefficient; The fourth determination subunit: determines the quality health index of the blade according to the structural damage of the blade, and determines the rotation speed information of the blade according to the working parameters; Extraction subunit: Determine the noise information of the blade during rotation according to the rotation speed information and material parameters of the blade, and extract the noise characteristic parameters of the blade at different angles and heights according to the noise information; The fifth determination subunit determines the spatial distribution law of the noise characteristic parameters of the blade according to the noise characteristic parameters of the blade at different angles and heights; The sixth determination subunit: determines the change curve of the spatial spectrum energy amplitude of the blade according to the spatial distribution law of the noise characteristic parameters of the blade; Evaluation subunit: determining the noise characteristic state variable interval of the blade according to the spatial spectrum energy amplitude change curve of the blade, and evaluating the abnormal rotation speed of the blade according to the noise characteristic state variable interval; A seventh determination subunit: determining a performance health index of the blade based on the abnormal rotation speed of the blade, and determining a response resonance modal parameter of the blade under the working parameters; Acquisition subunit: acquiring the response health index of the blade according to the response resonance modal parameters and the standard resonance modal parameters of the blade under the working parameters; The eighth determination subunit: determines the comprehensive health index of the blade by weighted calculation based on the quality health index, performance health index and reaction health index of the blade.

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