Deicing method and system applied to wind turbine generator blades

Abstract

The invention discloses a deicing method and system applied to a wind turbine generator blade, and relates to the technical field of wind power generation, and the method comprises the following steps: collecting meteorological data and historical icing data of a region where a wind power plant is located, preprocessing the meteorological data and the historical icing data, constructing an icing monitoring system, wherein the meteorological data comprises real-time meteorological data and historical meteorological data, analyzing correlation between the meteorological data and historical icing data, extracting abnormal factors of icing of the blades of the wind turbine generator, evaluating the severity and range of icing of the blades of the wind turbine generator, and analyzing the preprocessed meteorological data and historical icing data. According to the method, the icing event can be predicted through the weather prediction model, the real-time data acquisition system and the deicing system, and actions are taken before icing or at the initial stage, so that the wind turbine generator can better adapt to the complex and changeable icing environment, the downtime caused by icing is shortened, and the usability and flexibility of the wind turbine generator are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a deicing method and system for wind turbine blades. Background Art

[0002] Freezing rain climate is widely distributed in my country, especially in winter, when many regions will be affected by freezing rain. Freezing rain is the fastest freezing method among various freezing climates, and its harm is also the greatest. This climatic condition can easily cause ice to form on the surface of wind turbine blades. The blades of wind turbines are key components of wind turbines, and their aerodynamic shape has a direct impact on power generation efficiency. After the surface of the blades freezes, their aerodynamic shape will change, resulting in a decrease in the power generation capacity of the wind turbine. When the ice is severe, the wind turbine even needs to be shut down to avoid further damage. In order to ensure power generation efficiency, the blades need to be de-iced. With the rapid development of the wind power industry and the continuous increase in wind power installed capacity, the demand for de-icing technology for wind turbine blades is also increasing, especially in areas with extensive ice areas and abundant wind power resources. This demand is even more urgent.

[0003] In the prior art, due to the long length of wind turbine blades, the icing conditions in different areas may be different, and the degree of icing on the blades in different time periods also varies significantly. In addition, changes in climatic conditions will directly affect the speed and degree of icing on the blades, which will affect the responsiveness of the de-icing system to a certain extent. This makes it difficult for a single de-icing method to fully cover all icing areas, resulting in poor actual de-icing effects and affecting the overall operating efficiency of the wind turbine. Therefore, the development of more intelligent and efficient de-icing technologies to adapt to complex and changeable icing environments has become an important issue that needs to be urgently addressed in the wind power field. Summary of the invention

[0004] The present invention aims to provide a deicing method and system for wind turbine blades to solve the problems mentioned in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] In a first aspect, a deicing method for a wind turbine blade comprises the following steps:

[0007] Step 1, collect meteorological data and historical icing data of the area where the wind farm is located, pre-process the meteorological data and historical icing data, and build an icing monitoring system, wherein the meteorological data includes real-time meteorological data and historical meteorological data, wherein the meteorological data includes temperature, humidity, wind speed, wind direction, etc., and the historical icing data includes the type, thickness, distribution and duration of blade icing;

[0008] Step 2: Analyze the correlation between meteorological data and historical icing data, extract abnormal factors of wind turbine blade icing, and evaluate the severity and scope of wind turbine blade icing, such as mild, moderate, severe and local, regional, and comprehensive, to determine the priority and necessity of de-icing solutions;

[0009] Step 3, analyzing the pre-processed meteorological data and historical icing data, obtaining the temperature assessment index, humidity assessment index and wind speed assessment index, and predicting the severity and location of icing on the wind turbine blades, and evaluating the operating status of the wind turbine blades;

[0010] Step 4: obtain an icing warning evaluation coefficient based on the temperature evaluation index, the humidity evaluation index, and the wind speed evaluation index, and determine different warning levels of wind turbine blade icing conditions in combination with the preprocessed historical data, set warning evaluation thresholds for different warning levels, and obtain a warning evaluation sequence table to facilitate rapid response and decision-making;

[0011] Step 5: Evaluate the icing condition of the wind turbine blades in combination with the icing warning evaluation coefficient, and output a deicing plan for the wind turbine blades according to the warning level. At the same time, formulate a specific deicing plan based on the real-time monitoring data of the deicing effect. For example, increase the heating power, adjust the inflation frequency of the expansion tube, etc., to improve the deicing efficiency.

[0012] A further improvement of the technical solution of the present invention is that the process of constructing the icing monitoring system is:

[0013] Step 101, obtain real-time meteorological data through the wind tower and micro-meteorological station at the wind farm, obtain historical meteorological data from the database of the meteorological department, and collect the records of past icing of wind turbine blades to identify the conditions and patterns of icing, including the time, duration, thickness, location and type of icing. The icing types are divided into frost, snow and ice, etc. The real-time meteorological data and historical meteorological data include wind speed, wind direction, temperature, humidity, atmospheric pressure, optical rainfall, total radiation and other data. The wind tower is usually equipped with meteorological instruments such as anemometers, wind direction sensors, temperature sensors, humidity sensors, etc., which can measure and transmit data in real time.

[0014] Step 102, data cleaning and preprocessing are performed on the real-time meteorological data and historical meteorological data to remove unnecessary redundant information, wherein the preprocessing includes data integration, data conversion, etc., and the long-term change trend of the meteorological data is analyzed, and a blade icing risk prediction model is constructed, such as analyzing the seasonal and long-term change trends of parameters such as temperature, humidity, and precipitation.

[0015] Step 103, based on the pre-processed meteorological data, determine the required monitoring objects and monitoring indicators, such as fan blade icing, ice thickness, wind speed, wind direction, etc., and deploy software in the wind farm, and deploy hardware at the corresponding locations, such as meteorological instruments such as ice thickness sensors, anemometers, wind direction sensors, and icing monitoring system software, such as a central monitoring system, to ensure that all equipment can operate stably and transmit data in real time, so as to analyze meteorological data in real time and predict icing risks.

[0016] A further improvement of the technical solution of the present invention is that the process of evaluating the severity and scope of icing of wind turbine blades is as follows:

[0017] Step 201, based on the pre-processed meteorological data and historical icing data, extract the records of wind turbine blade icing, including the time of icing, environmental conditions, icing degree and the affected wind turbine blade range, analyze the correlation between the meteorological data and wind turbine blade icing, and output an analysis report, in which the environmental conditions are temperature, humidity, wind speed, etc.;

[0018] Step 202, extracting abnormal factors of blade icing based on the analysis report, and determining which meteorological abnormal factors, such as temperature, humidity, precipitation, wind speed, etc., have a significant correlation with the occurrence of blade icing of the wind turbine;

[0019] Step 203, in combination with the blade icing risk prediction model, evaluate the thickness, range and duration of wind turbine blade icing, as well as the specific area affected by wind turbine blade icing, and comprehensively consider meteorological data and the severity of wind turbine blade icing, calculate the duration of icing events, and the frequency of icing in specific time periods such as seasons, day and night, and evaluate the risk to wind turbine operation.

[0020] A further improvement of the technical solution of the present invention is that the process of evaluating the operating status of the wind turbine blades is:

[0021] Step 301, integrating the pre-processed meteorological data with the historical icing data to form a unified data set;

[0022] Step 302, dividing the integrated data set into a training set, a validation set, and a test set, using the training set data to train the blade icing risk prediction model, and using the validation set and the test set data to verify the trained blade icing risk prediction model, and evaluating the performance of the blade icing risk prediction model;

[0023] Step 303, using the trained blade icing risk prediction model and combining it with real-time meteorological data, predict the severity and location of wind turbine blade icing under current conditions, and obtain a temperature assessment index, a humidity assessment index, and a wind speed assessment index;

[0024] Step 304, based on the temperature assessment index, humidity assessment index and wind speed assessment index, comprehensively consider the severity and location of icing to assess the impact on the operating state of the wind turbine blades, which may include reduced power generation efficiency, increased mechanical stress, risk of blade damage, etc.

[0025] A further improvement of the technical solution of the present invention is that the calculation formula of the temperature evaluation index is:

[0026]

[0027] Among them, TI is the temperature evaluation index, α is the temperature coefficient, β is the temperature adjustment coefficient, T is the real-time temperature, T f Stable at freezing point, usually 0°C, T s is the standard temperature;

[0028] The calculation formula of the humidity assessment index is:

[0029]

[0030] Among them, HI is the humidity assessment index, γ is the humidity basic coefficient, δ is the temperature fluctuation coefficient, RH is the real-time relative humidity, RH s is the standard relative humidity, Hi is usually between 0 and γ+δ;

[0031] The calculation formula of the wind speed assessment index is:

[0032]

[0033] Among them, VI is the wind speed assessment index, ∈ is the wind speed influence coefficient, ζ is the wind speed fluctuation coefficient, V is the real-time wind speed m / s, V min is the minimum wind speed threshold, v max is the maximum wind speed threshold.

[0034] A further improvement of the technical solution of the present invention is that the process of obtaining the early warning evaluation sequence table is:

[0035] Step 401, combining the temperature assessment index, the humidity assessment index and the wind speed assessment index to analyze the correlation between the abnormal factors of wind turbine blade icing and the meteorological data;

[0036] Step 402, assigning different weights to the temperature assessment index, the humidity assessment index, and the wind speed assessment index, performing weighted calculation on the three indexes, and obtaining an icing warning assessment coefficient;

[0037] Step 403, matching the icing conditions of wind turbine blades in historical icing data with the preprocessed meteorological data, and determining different warning levels, namely low risk warning, medium risk warning and high risk warning;

[0038] Step 404, based on the divided warning levels of wind turbine blade icing conditions and in combination with the icing warning evaluation coefficient, setting warning evaluation thresholds of different warning levels;

[0039] Step 405, based on the meteorological data and historical icing data and the determined warning assessment threshold, a warning assessment sequence table is generated, and the trend and pattern of icing risk are identified to provide decision support for wind farm operation and maintenance. The warning assessment sequence table records the icing warning level at each time point.

[0040] A further improvement of the technical solution of the present invention is that the calculation formula of the icing warning evaluation coefficient is:

[0041]

[0042] Among them, IWC is the icing warning assessment coefficient, TI is the temperature assessment index, T s is the standard temperature, T r is the temperature range, HI is the humidity assessment index, H max is the maximum humidity, VI is the wind speed assessment index, v c is the critical value of wind speed, e is the base of natural logarithm, approximately equal to 2.71828; its value range and trend are: Temperature part: The value range is from 0 to infinity. When the temperature assessment index is close to the standard value, this part tends to 0; when the temperature assessment index is far away from the standard value, this part increases, indicating that the impact of temperature on the risk of icing increases. Humidity part: The value range is from 0 to 1, but does not include 0, and is magnified to the fourth power. The larger the humidity assessment index, the more significant the impact of humidity on the risk of icing. For the wind speed part: The value range of is from 0 to 1, excluding the extreme cases of 0 and 1, depending on the ratio of the wind speed evaluation index to the critical value and the value of the temperature evaluation index. As the wind speed increases, the value of this part increases, but the growth rate is affected by the temperature. The lower the temperature, the faster or slower the growth rate may be, depending on the nature of the exponential function.

[0043] A further improvement of the technical solution of the present invention is that: the multiple warning levels correspond to the multiple warning assessment thresholds, wherein the warning assessment thresholds include an upper threshold and a lower threshold;

[0044] The multiple warning levels and the multiple warning assessment thresholds satisfy the following relationship:

[0045] Low alert level: IWC <C1;

[0046] Medium warning level: C1 <IWC<C2;

[0047] High warning level: C2 <IWC<C3;

[0048] Among them, IWC is the icing warning assessment coefficient, C1 is the upper threshold corresponding to the low warning level and the lower threshold corresponding to the medium warning level, C2 is the upper threshold corresponding to the medium warning level and the lower threshold corresponding to the high warning level, C3 is the upper threshold corresponding to the high warning level and the lower threshold corresponding to the fourth warning level, C1=0.2, C2=0.5, C3=0.8.

[0049] A further improvement of the technical solution of the present invention is that the process of formulating a specific deicing plan is:

[0050] Step 501: input the pre-processed meteorological data and historical icing data into the blade icing risk prediction model to obtain the temperature assessment index, humidity assessment index and wind speed assessment index.

[0051] Step 502, combining the temperature assessment index, the humidity assessment index and the wind speed assessment index to obtain an icing warning assessment coefficient, and determining the current wind turbine blade icing warning level in combination with a preset warning assessment threshold;

[0052] Step 503, based on the current warning level of wind turbine blade icing, generate corresponding warning information, formulate a suitable deicing plan, allocate necessary human and material resources, and monitor data in real time to evaluate the deicing effect. The plan should include deicing methods, deicing time, deicing sequence, etc.

[0053] In a second aspect, a deicing system for a wind turbine blade is used to implement the deicing method for a wind turbine blade, comprising a control center, wherein the control center is communicatively connected to a monitoring module, a deicing module, an energy supply module, and a safety protection module, wherein electrical signals are connected between the modules;

[0054] The monitoring module is used to monitor the ice coverage of the wind turbine blades in real time, as well as the wind turbine blade information after deicing;

[0055] Including wind turbine blade surface temperature, humidity, wind speed and direction, as well as ice thickness, distribution location and other ice coverage information, providing accurate data support for subsequent de-icing decisions;

[0056] The deicing module is used to remove ice from the blade surface and restore the blade to normal operation; such as electric deicing, gas deicing, vibration deicing, etc., to melt or fall off the ice layer;

[0057] The energy supply module is used to provide stable energy support for the deicing system, including power supply and heat energy supply, etc.;

[0058] The safety protection module is used to monitor the safety status of the de-icing module. When abnormal situations occur, such as excessive temperature, excessive current, etc., protective measures are taken in time to ensure that the de-icing system can operate safely under various extreme conditions and prevent equipment damage or safety accidents caused by de-icing operations.

[0059] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0060] 1. The present invention provides a deicing method and system for wind turbine blades. By real-time monitoring of the icing conditions in various areas of the blades, including ice thickness, distribution range and change trend, the deicing system is ensured to respond quickly to changes in climatic conditions and to accurately de-ice according to the differences in icing in different areas of the blades, thereby effectively avoiding the problem of excessive or insufficient deicing, significantly improving the deicing efficiency and accuracy, and ensuring the stable operation of wind turbines in complex and changing environments.

[0061] 2. The present invention provides a deicing method and system for wind turbine blades. Through a meteorological prediction model and a real-time data acquisition system, the deicing system can predict icing events and take action before or in the early stages of icing, so that the wind turbine can better adapt to complex and changeable icing environments, reduce downtime caused by icing, and improve the availability and flexibility of the wind turbine.

[0062] 3. The present invention provides a deicing method and system for wind turbine blades. By adopting multi-source collaborative deicing technology, the optimal deicing strategy is flexibly selected according to the severity and location of blade icing, which not only speeds up the deicing speed, but also reduces the downtime of the wind turbine during the deicing process, thereby maximizing the continuous output of power generation. The deicing strategy can also be automatically adjusted according to the actual icing conditions of the blades, further reducing the need for manual intervention and maintenance costs.

[0063] 4. The present invention provides a deicing method and system for wind turbine blades, which can timely and effectively remove the ice layer on the blade surface, thereby reducing the destructive effect of the ice layer on the blade material and extending the service life of the blade. In addition, the intelligent monitoring system can also detect potential damage or defects of the blade in advance, provide data support for preventive maintenance, and improve the reliability and safety of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0065] Figure 1 is a flow chart of the method of the present invention;

[0066] Figure 2 A flowchart for obtaining the severity and range of blade icing according to the present invention;

[0067] Figure 3 A flow chart for obtaining the temperature assessment index, humidity assessment index and wind speed assessment index of the present invention;

[0068] Figure 4 It is a module structure diagram of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments 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.

[0070] Embodiment 1, as Figure 1-3 As shown, the present invention provides a deicing method for wind turbine blades, comprising the following steps:

[0071] Step 1, collect meteorological data and historical icing data in the area where the wind farm is located, pre-process the meteorological data and historical icing data, and build an icing monitoring system, wherein the meteorological data includes real-time meteorological data and historical meteorological data, wherein the meteorological data includes temperature, humidity, wind speed, wind direction, etc., and the historical icing data includes the type, thickness, distribution and duration of blade icing; the process of building an icing monitoring system is: obtain real-time meteorological data through the wind tower and micro-meteorological station in the wind farm, obtain historical meteorological data from the database of the meteorological department, and collect the records of past icing of wind turbine blades at the same time, identify the conditions and patterns of icing, including the time, duration, thickness, location and type of icing, etc. The icing types are divided into frost, snow and ice, etc., wherein the real-time meteorological data and historical meteorological data both include wind speed, wind direction, temperature, humidity, atmospheric pressure, optical rainfall, total radiation and other data, and the wind tower can obtain real-time meteorological data through the wind tower and micro-meteorological station in the wind farm, obtain historical meteorological data from the database of the meteorological department, and collect the records of past icing of wind turbine blades at the same time, identify the conditions and patterns of icing, including the time, duration, thickness, location and type of icing, etc. It is often equipped with meteorological instruments such as anemometers, wind direction sensors, temperature sensors, and humidity sensors, which can measure and transmit data in real time, clean and preprocess real-time and historical meteorological data, and remove unnecessary redundant information. Preprocessing includes data integration, data conversion, etc., and analyzes the long-term change trend of meteorological data. At the same time, it builds a blade icing risk prediction model, such as analyzing the seasonal and long-term change trends of parameters such as temperature, humidity, and precipitation. According to the preprocessed meteorological data, the required monitoring objects and monitoring indicators are determined, such as fan blade icing, ice thickness, wind speed, wind direction, etc., and software is deployed in the wind farm, and hardware is deployed at the corresponding location, such as meteorological instruments such as ice thickness sensors, anemometers, wind direction sensors, and icing monitoring system software, such as central monitoring systems, to ensure that all equipment can operate stably and transmit data in real time, so as to analyze meteorological data in real time and predict icing risks;

[0072] Step 2: Analyze the correlation between meteorological data and historical icing data, extract abnormal factors of wind turbine blade icing, and evaluate the severity and scope of wind turbine blade icing, such as mild, moderate, severe and local, regional, and comprehensive, to determine the priority and necessity of de-icing solutions; the process of evaluating the severity and scope of wind turbine blade icing is to extract the records of wind turbine blade icing based on the pre-processed meteorological data and historical icing data, including the time of icing, environmental conditions, degree of icing, and the range of wind turbine blades affected, analyze the correlation between meteorological data and wind turbine blade icing, and output the analysis results. The report includes environmental conditions such as temperature, humidity, wind speed, etc., extracts abnormal factors of blade icing based on the analysis report, determines which meteorological abnormal factors, such as temperature, humidity, precipitation, wind speed, etc., are significantly correlated with the occurrence of wind turbine blade icing, and combines the blade icing risk prediction model to evaluate the thickness, range and duration of wind turbine blade icing, as well as the specific areas affected by wind turbine blade icing. It also comprehensively considers meteorological data and the severity of wind turbine blade icing, and statistically calculates the duration of icing events and the frequency of icing in specific time periods such as seasons, day and night, to evaluate the risks to wind turbine operation.

[0073] Step 3, analyzing the preprocessed meteorological data and historical icing data, obtaining the temperature assessment index, humidity assessment index and wind speed assessment index, and predicting the severity and location of icing on the blades of the wind turbine, and evaluating the operating status of the blades of the wind turbine; the process of evaluating the operating status of the blades of the wind turbine is: integrating the preprocessed meteorological data with the historical icing data to form a unified data set, dividing the integrated data set into a training set, a validation set and a test set, using the training set data to train the blade icing risk prediction model, and using the validation set and the test set data to verify the trained blade icing risk prediction model, evaluating the performance of the blade icing risk prediction model, using the trained blade icing risk prediction model and combining it with real-time meteorological data to predict the severity and location of icing on the blades of the wind turbine under current conditions, and obtaining the temperature assessment index, humidity assessment index and wind speed assessment index, based on the temperature assessment index, humidity assessment index and wind speed assessment index, comprehensively considering the severity and location of icing, and evaluating the impact on the operating status of the blades of the wind turbine, which may include reduced power generation efficiency, increased mechanical stress, and the risk of blade damage;

[0074] The calculation formula of temperature assessment index is:

[0075]

[0076] Among them, TI is the temperature evaluation index, α is the temperature coefficient, β is the temperature adjustment coefficient, T is the real-time temperature, T f Stable at freezing point, usually 0°C, Ts is the standard temperature;

[0077] The calculation formula for the humidity assessment index is:

[0078]

[0079] Among them, HI is the humidity assessment index, γ is the humidity basic coefficient, δ is the temperature fluctuation coefficient, RH is the real-time relative humidity, RH s is the standard relative humidity, Hi is usually between 0 and γ+δ;

[0080] The calculation formula of wind speed assessment index is:

[0081]

[0082] Among them, VI is the wind speed assessment index, ∈ is the wind speed influence coefficient, ζ is the wind speed fluctuation coefficient, V is the real-time wind speed m / s, V min is the minimum wind speed threshold, v max is the maximum wind speed threshold;

[0083] Step 4, based on the temperature assessment index, humidity assessment index and wind speed assessment index, obtain the icing warning assessment coefficient, and determine the different warning levels of wind turbine blade icing conditions in combination with the preprocessed historical data, set warning assessment thresholds for different warning levels, and obtain the warning assessment sequence table to facilitate rapid response and decision-making; the process of obtaining the warning assessment sequence table is: combining the temperature assessment index, humidity assessment index and wind speed assessment index, analyzing the correlation between the abnormal factors of wind turbine blade icing and meteorological data, assigning different weights to the temperature assessment index, humidity assessment index and wind speed assessment index, and weighting the three indexes. The weight calculation is used to obtain the icing warning assessment coefficient. The icing conditions of the wind turbine blades in the historical icing data are matched according to the pre-processed meteorological data to determine different warning levels, namely low-risk warning, medium-risk warning and high-risk warning. Based on the warning levels of the icing conditions of the wind turbine blades, combined with the icing warning assessment coefficient, the warning assessment thresholds of different warning levels are set. According to the meteorological data, historical icing data and the determined warning assessment thresholds, a warning assessment sequence table is generated, and the trend and pattern of icing risks are identified to provide decision support for wind farm operation and maintenance. The warning assessment sequence table records the icing warning level at each time point.

[0084] The calculation formula of the icing warning assessment coefficient is:

[0085]

[0086] Among them, IWC is the icing warning assessment coefficient, TI is the temperature assessment index, T s is the standard temperature, T ris the temperature range, HI is the humidity evaluation index, H max is the maximum value of humidity, VI is the wind speed evaluation index, v c is the critical value of wind speed, e is the base of the natural logarithm, approximately equal to 2.71828; its value range and trend are as follows: Temperature part: ranges from 0 to infinity. When the temperature evaluation index approaches the standard value, this part tends to 0; when the temperature evaluation index is far from the standard value, this part increases, indicating an increase in the impact of temperature on the icing risk. Humidity part: ranges from 0 to 1, excluding 0, and is amplified to the fourth power. The larger the humidity evaluation index, the more significant the impact of humidity on the icing risk. Wind speed part: ranges from 0 to 1, excluding the extreme cases of 0 and 1, and specifically depends on the ratio of the wind speed evaluation index to the critical value and the value of the temperature evaluation index. As the wind speed increases, this part increases, but the growth rate is affected by temperature. The lower the temperature, the faster or slower the growth rate may be, specifically depending on the properties of the exponential function;

[0087] Furthermore, multiple warning levels correspond to multiple warning evaluation thresholds. Among them, the warning evaluation thresholds include upper thresholds and lower thresholds;

[0088] The multiple warning levels and multiple warning evaluation thresholds satisfy the following relationship:

[0089] Low warning level: IWC < C1. The countermeasures for the low warning level are not to de-ice immediately, but to strengthen monitoring, prepare emergency de-icing equipment, regularly check the blade status, and ensure the normal operation of the monitoring system;

[0090] Medium warning level: C1 < IWC < C2. The countermeasures for the medium warning level are to initiate preventive de-icing measures, such as using heating elements or chemical de-icing agents, select appropriate de-icing methods according to the specific icing conditions of the blades, and arrange personnel and equipment for de-icing operations;

[0091] High warning level: C2 < IWC < C3. The countermeasures for the high warning level are to immediately execute the emergency de-icing procedure to ensure the safe operation of the wind turbine, activate all available de-icing means, including mechanical de-icing, heating de-icing, and / or chemical de-icing, and at the same time reduce or stop the power generation of the wind turbine to avoid mechanical failures or accidents caused by icing;

[0092] Among them, IWC is the icing warning evaluation coefficient, C1 is the upper threshold corresponding to the low warning level and the lower threshold corresponding to the medium warning level, C2 is the upper threshold corresponding to the medium warning level and the lower threshold corresponding to the high warning level, C3 is the upper threshold corresponding to the high warning level and the lower threshold corresponding to the fourth warning level, C1 = 0.2, C2 = 0.5, C3 = 0.8;

[0093] Step 5, evaluate the icing condition of the wind turbine blades in combination with the icing warning assessment coefficient, and output the deicing plan for the wind turbine blades according to the warning level, and formulate a specific deicing plan according to the real-time monitoring data of the deicing effect. For example, increase the heating power, adjust the inflation frequency of the expansion tube, etc., to improve the deicing efficiency; the process of formulating a specific deicing plan is: input the pre-processed meteorological data and historical icing data into the blade icing risk prediction model to obtain the temperature assessment index, humidity assessment index and wind speed assessment index, combine the temperature assessment index, humidity assessment index and wind speed assessment index to obtain the icing warning assessment coefficient, and determine the current warning level of wind turbine blade icing in combination with the preset warning assessment threshold, generate corresponding warning information according to the current warning level of wind turbine blade icing, formulate a suitable deicing plan, allocate necessary human and material resources, and monitor the data in real time to evaluate the deicing effect. The plan should include deicing methods, deicing time, deicing sequence, etc.

[0094] Embodiment 2, as Figure 4 As shown, based on Example 1, the present invention provides a deicing system for wind turbine blades, including a control center, the control center is communicatively connected with a monitoring module, a deicing module, an energy supply module and a safety protection module, wherein electrical signals are connected between the modules;

[0095] The monitoring module is used to monitor the ice coverage of wind turbine blades in real time, as well as the information of wind turbine blades after deicing;

[0096] Including wind turbine blade surface temperature, humidity, wind speed and direction, as well as ice thickness, distribution location and other ice coverage information, providing accurate data support for subsequent de-icing decisions;

[0097] The deicing module is used to remove ice from the blade surface and restore the blade to normal operation; such as electric deicing, gas deicing, vibration deicing, etc., to melt or fall off the ice layer;

[0098] Among them, electric deicing: using heating elements such as resistance wire or electric heating plate to heat the blades to melt the ice layer and fall off; gas deicing: by introducing hot wind or hot air, the blades are heated and blown to accelerate the melting of the ice layer and prevent re-icing; vibration deicing: using a vibration device to generate mechanical vibration to loosen the ice layer on the blades and fall off.

[0099] The energy supply module is used to provide stable energy support for the de-icing system, including power supply and heat supply;

[0100] The safety protection module is used to monitor the safety status of the de-icing module. When abnormal situations occur, such as excessive temperature, excessive current, etc., protective measures are taken in time to ensure that the de-icing system can operate safely under various extreme conditions and prevent equipment damage or safety accidents caused by de-icing operations.

[0101] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A deicing method for wind turbine blades, characterized in that: The following steps are involved: Step 1, collecting meteorological data and historical icing data in the area where the wind farm is located, and preprocessing the meteorological data and historical icing data to build an icing monitoring system, wherein the meteorological data includes real-time meteorological data and historical meteorological data; Step 2: Analyze the correlation between meteorological data and historical icing data, extract abnormal factors of wind turbine blade icing, and evaluate the severity and scope of wind turbine blade icing; Step 3, analyzing the pre-processed meteorological data and historical icing data, obtaining the temperature assessment index, humidity assessment index and wind speed assessment index, and predicting the severity and location of icing on the wind turbine blades, and evaluating the operating status of the wind turbine blades; Step 4, obtaining an icing warning evaluation coefficient based on the temperature evaluation index, the humidity evaluation index and the wind speed evaluation index, and determining different warning levels of icing conditions of wind turbine blades in combination with the preprocessed historical data, setting warning evaluation thresholds for different warning levels, and obtaining a warning evaluation sequence table; Step 5, evaluate the icing condition of the wind turbine blades in combination with the icing warning assessment coefficient, and output a deicing plan for the wind turbine blades according to the warning level. At the same time, formulate a specific deicing plan based on the real-time monitoring data of the deicing effect.

2. A deicing method for wind turbine blades according to claim 1, characterized in that: The process of constructing the icing monitoring system is as follows: Step 101, obtaining real-time meteorological data through a wind tower and a micro-meteorological station at a wind farm, obtaining historical meteorological data from a database, and collecting past records of icing on wind turbine blades to identify conditions and patterns of icing. Step 102, cleaning and preprocessing the real-time meteorological data and historical meteorological data, removing unnecessary redundant information, analyzing the long-term change trend of the meteorological data, and building a blade icing risk prediction model; Step 103, determining the required monitoring objects and monitoring indicators based on the pre-processed meteorological data, and performing software deployment in the wind farm and hardware deployment at the corresponding locations.

3. A deicing method for wind turbine blades according to claim 2, characterized in that: The process of evaluating the severity and extent of icing on wind turbine blades is as follows: Step 201, based on the pre-processed meteorological data and historical icing data, extract the records of wind turbine blade icing, including the time of icing occurrence, environmental conditions, icing degree and affected wind turbine blade range, analyze the correlation between the meteorological data and wind turbine blade icing, and output an analysis report; Step 202, extracting abnormal factors of blade icing based on the analysis report, and determining the correlation between the meteorological abnormal factors and blade icing of the wind turbine; Step 203, combining the blade icing risk prediction model, evaluating the thickness, range and duration of wind turbine blade icing, as well as the specific area affected by wind turbine blade icing, and comprehensively considering meteorological data and the severity of wind turbine blade icing to evaluate the risk to wind turbine operation.

4. A deicing method for wind turbine blades according to claim 3, characterized in that: The process of evaluating the operating status of the wind turbine blades is as follows: Step 301, integrating the pre-processed meteorological data with the historical icing data to form a unified data set; Step 302, dividing the integrated data set into a training set, a validation set, and a test set, using the training set data to train the blade icing risk prediction model, and using the validation set and the test set data to verify the trained blade icing risk prediction model, and evaluating the performance of the blade icing risk prediction model; Step 303, using the trained blade icing risk prediction model and combining it with real-time meteorological data, predict the severity and location of wind turbine blade icing under current conditions, and obtain a temperature assessment index, a humidity assessment index, and a wind speed assessment index; Step 304 , based on the temperature assessment index, the humidity assessment index and the wind speed assessment index, comprehensively considering the severity and location of icing, assess the impact on the operating state of the wind turbine blades.

5. A deicing method for wind turbine blades according to claim 4, characterized in that: The calculation formula of the temperature evaluation index is: Among them, TI is the temperature evaluation index, α is the temperature coefficient, β is the temperature adjustment coefficient, T is the real-time temperature, T f For freezing point stability, T s is the standard temperature; The calculation formula of the humidity assessment index is: Among them, HI is the humidity assessment index, γ is the humidity basic coefficient, δ is the temperature fluctuation coefficient, RH is the real-time relative humidity, RH s is the standard relative humidity; The calculation formula of the wind speed assessment index is: Among them, VI is the wind speed assessment index, ∈ is the wind speed influence coefficient, ζ is the wind speed fluctuation coefficient, V is the real-time wind speed m / s, V min is the minimum wind speed threshold, v max is the maximum wind speed threshold.

6. A deicing method for wind turbine blades according to claim 5, characterized in that: The acquisition process of the early warning evaluation sequence table is as follows: Step 401, combining the temperature assessment index, the humidity assessment index and the wind speed assessment index to analyze the correlation between the abnormal factors of wind turbine blade icing and the meteorological data; Step 402, assigning different weights to the temperature assessment index, the humidity assessment index, and the wind speed assessment index, performing weighted calculation on the three indexes, and obtaining an icing warning assessment coefficient; Step 403, matching the icing conditions of wind turbine blades in historical icing data with the preprocessed meteorological data, and determining different warning levels, namely low risk warning, medium risk warning and high risk warning; Step 404, based on the divided warning levels of wind turbine blade icing conditions and in combination with the icing warning evaluation coefficient, setting warning evaluation thresholds of different warning levels; Step 405 , generating a warning assessment sequence list based on the meteorological data and historical icing data and the determined warning assessment threshold, and identifying trends and patterns of icing risks.

7. A deicing method for wind turbine blades according to claim 6, characterized in that: The calculation formula of the icing warning evaluation coefficient is: Among them, IWC is the icing warning assessment coefficient, TI is the temperature assessment index, T s is the standard temperature, T r is the temperature range, HI is the humidity assessment index, H max is the maximum humidity, VI is the wind speed assessment index, v c is the critical value of wind speed.

8. A deicing method for wind turbine blades according to claim 7, characterized in that: A plurality of the warning levels correspond to a plurality of the warning assessment thresholds, wherein the warning assessment thresholds include an upper threshold and a lower threshold; The multiple warning levels and the multiple warning assessment thresholds satisfy the following relationship: Low alert level: IWC <C1; Medium warning level: C1 <IWC<C2; High warning level: C2 <IWC<C3; Among them, IWC is the icing warning assessment coefficient, C1 is the upper threshold corresponding to the low warning level and the lower threshold corresponding to the medium warning level, C2 is the upper threshold corresponding to the medium warning level and the lower threshold corresponding to the high warning level, and C3 is the upper threshold corresponding to the high warning level.

9. A deicing method for wind turbine blades according to claim 8, characterized in that: The process of formulating a specific de-icing plan is as follows: Step 501: input the pre-processed meteorological data and historical icing data into the blade icing risk prediction model to obtain the temperature assessment index, humidity assessment index and wind speed assessment index. Step 502, combining the temperature assessment index, the humidity assessment index and the wind speed assessment index to obtain an icing warning assessment coefficient, and determining the current wind turbine blade icing warning level in combination with a preset warning assessment threshold; Step 503, according to the current warning level of wind turbine blade icing, generate corresponding warning information, formulate a deicing plan, deploy human and material resources, and monitor data in real time to evaluate the deicing effect.

10. A deicing system for wind turbine blades, used to implement the deicing method for wind turbine blades according to any one of claims 1 to 9, comprising a control center, characterized in that: The control center is communicatively connected with a monitoring module, a deicing module, an energy supply module and a safety protection module, wherein electrical signals are connected between the modules; The monitoring module is used to monitor the ice coverage of the wind turbine blades in real time, as well as the wind turbine blade information after deicing; The deicing module is used to remove ice from the blade surface and restore the blade to normal operation; The energy supply module is used to provide stable energy support for the deicing system, including power supply and heat energy supply; The safety protection module is used to monitor the safety status of the de-icing module and take protective measures in time when an abnormal situation occurs.

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