Icing monitoring and deicing control system and method for blades of wind driven generator

Abstract

The invention provides a wind driven generator blade icing monitoring and deicing control system and method, and the system comprises a noise collection unit, a temperature collection unit, an actual calculation unit, a deicing scheme generation unit, and a deicing scheme selection unit. The operation parameters of the wind generating set are monitored and analyzed by combining with the operation parameters of the wind generating set, including wind speed, air temperature, humidity, blade rotating speed, power generation power and the like, and meanwhile, real-time monitoring, acquisition, processing and storage are carried out through a monitoring system and a sensor. Operation parameters and environment data of the wind generating set are monitored, collected, processed and stored, monitoring, alarming and analyzing of the icing condition of blades are achieved, the optimal deicing strategy and time arrangement are determined, the deicing effect is improved to the maximum extent, the energy consumption of the set is reduced, the power generation efficiency and economical efficiency of the set are improved, and the economic benefit is increased. The purposes of reliable, efficient and economical operation and maintenance of the wind generating set are achieved.

Description

Technical Field

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

[0002] As a clean and renewable energy generation equipment, wind turbines have received more and more attention and importance. However, in winter or low temperature environment, wind turbine blades are susceptible to ice, which leads to the performance degradation of wind turbines and even causes the unit to shut down. In response to the problem of wind turbine blade ice, some related technical solutions have emerged. A common solution is to prevent blade ice by spraying anti-icing liquid on the blade surface. However, the cost of anti-icing liquid is high and needs to be replenished and replaced regularly, which has a certain impact on power generation costs and environmental protection.

[0003] Another solution is to use mechanical vibration or heating to remove ice, but this method has problems such as high energy consumption, complex equipment, and high operating costs. At the same time, it cannot well predict and monitor the blade icing situation. Among them, the more commonly used active deicing method is blade heating deicing. The method of measuring the surface temperature of the blade is used to determine whether the blade is frozen. When the surface temperature of the blade is lower than a certain threshold, it is considered that the blade is frozen and deicing is required. By installing an electric heating wire or electric heating film inside the blade, the heating inside the blade is conducted to the surface, so that the ice on the blade surface melts. The detection technology of a single sensor in the electric heating deicing method has obvious shortcomings for wind turbine blades. First, due to the uneven ice formation of wind turbine blades and the limited number of sensors installed, there is a large range of detection blind spots. Secondly, it consumes a lot of electricity. Its energy consumption mainly depends on factors such as blade heating power, deicing time and deicing times. The deicing time and deicing times depend on the blade icing situation and environmental conditions. It is difficult to achieve precise, adjustable and controllable heating deicing, which will increase deicing energy consumption and affect the economic benefits of power generation.

[0004] At present, the existing technical solutions in this field are unable to monitor the icing situation of blades well, and are unable to take timely measures for accurate de-icing. Wind turbine blade icing monitoring and de-icing control systems and data acquisition and processing methods are relatively rare. Summary of the invention

[0005] In view of this, an object of the present invention is to provide a wind turbine blade icing monitoring and deicing control system and method to solve the problem that the existing technical solutions cannot accurately monitor the icing of wind turbine blades and cannot help staff take appropriate measures to de-ice in time.

[0006] To achieve the above invention, the first aspect of the present invention provides a wind turbine blade icing monitoring and deicing control system, comprising:

[0007] Noise collection unit: used to collect aerodynamic noise data of blade aerodynamics, set a noise threshold according to the normal operating state of blade aerodynamics, compare the noise data with the noise threshold, and determine the icing condition according to the comparison result;

[0008] Temperature collection unit: used to collect temperature parameters on the blade surface, set an icing threshold for the temperature parameters, compare the temperature parameters with the icing threshold, and when the temperature parameters are lower than the icing threshold, intercept the historical temperature parameters for analysis to obtain the temperature change trend on the blade surface;

[0009] Actual calculation unit: used to collect the real-time wind speed at the hub height of the wind turbine, the temperature and humidity of the environment at the hub height of the wind turbine, and the atmospheric pressure value at the hub height of the wind turbine, and then correct and convert it into the actual value of the wind turbine facing the incoming wind;

[0010] De-icing scheme generation unit: used to collect the heating power and pitch parameters of the wind turbine, generate a de-icing scheme for the blades by combining the heating power with the pitch parameters, and obtain a list of feasible de-icing schemes;

[0011] De-icing scheme selection unit: used to combine the icing conditions determined by the noise collection unit, the temperature change trend of the blade surface obtained by the temperature collection unit, and the actual value obtained by the actual calculation unit to perform de-icing analysis, and adjust and select the de-icing scheme list obtained by the de-icing scheme generation unit based on the de-icing analysis results, and send the adjusted and selected de-icing scheme to the wind farm computer terminal, which controls the de-icing of the blades according to the de-icing scheme.

[0012] Furthermore, the noise collection unit processes the aerodynamic noise data using a noise data processing method, which specifically includes the following steps:

[0013] S11. Obtain the discrete time signal of the aerodynamic noise data and perform a fast Fourier transform on it to obtain a frequency domain signal, which is expressed as follows:

[0014]

[0015] Where X[k] is the value of the frequency domain signal at the kth frequency component, x[n] is the value of the discrete time signal at the nth sampling point, X is the frequency domain signal, N is the total number of sampling points of the signal, e and j are the phase information of the signal, and parameter k is the index of the frequency component;

[0016] S12. Find the frequency components based on the frequency domain signal and calculate the amplitude of each frequency component, which is expressed as follows:

[0017]

[0018] Where |X[k]| is the amplitude, Re(X[k]) and Im(X[k]) are the real and imaginary parts of X[k], respectively;

[0019] S13, based on step S12, find the index of the frequency component with the largest amplitude, and finally calculate the main frequency according to the index, which is expressed as follows:

[0020]

[0021] Among them, k max is the index, f is the main frequency, f s is the sampling frequency of the signal.

[0022] Furthermore, the noise collection unit includes a noise threshold setting module and a noise comparison module;

[0023] The noise threshold setting module is used to filter out historical aerodynamic noise data according to the aerodynamic operation state of the blade, obtain historical aerodynamic noise data under the normal aerodynamic operation state of the blade, and then filter out the maximum historical aerodynamic noise data according to the historical aerodynamic noise data under the normal aerodynamic operation state of the blade, and set it as the noise threshold;

[0024] The noise comparison module is used to compare the collected real-time aerodynamic noise data with the noise threshold. When the real-time aerodynamic noise data exceeds the noise threshold, it is determined that ice has appeared on the blade surface. Conversely, when the real-time aerodynamic noise data does not exceed the noise threshold, it is determined that no ice has appeared on the blade surface.

[0025] Furthermore, the temperature acquisition unit includes a temperature threshold setting module and a temperature change acquisition module;

[0026] The temperature threshold setting module is used to set the freezing threshold according to the computer terminal staff in the central control room of the wind farm;

[0027] The temperature change acquisition module is used to compare the temperature parameters with the freezing threshold. When the temperature parameters are lower than the freezing threshold, the historical temperature parameters are intercepted for analysis to obtain the temperature change trend of the blade surface. Conversely, when the temperature parameters are not lower than the freezing threshold, normal monitoring is maintained.

[0028] Furthermore, the actual calculation unit corrects and converts the actual value of the wind turbine facing the incoming wind, specifically including the following steps:

[0029] S31. Calculate the actual inflow angle of the blade, expressed as follows:

[0030] AOA=α-β

[0031] Among them, AOA is the actual inflow angle of the blade, α is the relative inflow angle of the blade, and β is the installation angle of the blade;

[0032] S32, searching for the corresponding lift coefficient and drag coefficient based on the actual inflow angle, and calculating the local lift and drag of the blade according to the lift coefficient and drag coefficient, as shown below:

[0033]

[0034] Where L is lift, D is drag, ρ is air density, V2 is wind speed behind the blade, c is blade reference area, CL is lift coefficient at actual inflow angle, CD is drag coefficient at actual inflow angle;

[0035] S33, the lift and drag of the blade are related to the wind speed before and after the blade, which is expressed as follows:

[0036]

[0037] D=(V1-V2)Q

[0038] Among them, V1 is the wind speed in front of the blade, T and Q are the axial thrust and tangential torque respectively;

[0039] S34, based on step S33, the nonlinear equations for calculating the wind speed in front of the blade and the wind speed behind the blade are expressed as follows:

[0040]

[0041] Among them, V1 is the wind speed before the blade, and V2 is the wind speed after the blade.

[0042] A second aspect of the present invention provides a method for monitoring and controlling ice formation and deicing of wind turbine blades, the method comprising the following steps:

[0043] S101, determining the icing condition through a noise collection unit, obtaining the temperature change trend through a temperature collection unit, and obtaining the actual value through an actual calculation unit;

[0044] S102, a deicing scheme generating unit obtains a deicing scheme list of a wind turbine;

[0045] S103, using the de-icing scheme selection unit to combine the noise collection unit, the temperature collection unit and the actual calculation unit to perform de-icing analysis, combining the de-icing analysis result with the de-icing scheme generation unit for adjustment and selection, sending the adjusted and selected de-icing scheme to the wind farm computer terminal, and the wind farm computer terminal controls the de-icing of the blades according to the de-icing scheme.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention proposes a wind turbine blade icing monitoring and deicing control system and method, which monitors and analyzes the wind turbine operating parameters by combining them with the wind turbine operating parameters, including wind speed, air temperature, humidity, blade speed, power generation and other parameters, and simultaneously monitors, collects, processes and stores them in real time through a monitoring system and sensors, thereby realizing monitoring, collecting, processing and storing the wind turbine operating parameters and environmental data, monitoring, alarming and analyzing the blade icing situation, and determining the optimal deicing strategy and time arrangement, so as to maximize the deicing effect and reduce the unit energy consumption, improve the unit power generation efficiency and economy, and achieve reliable, efficient and economical wind turbine operation and maintenance purposes. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 This is a schematic diagram of the overall structure of a wind turbine blade icing monitoring and deicing control system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0051] like Figure 1 As shown, the present invention provides an overall structural principle diagram of a wind turbine blade icing monitoring and deicing control system.

[0052] Reference Figure 1 This embodiment provides a wind turbine blade icing monitoring and deicing control system, including:

[0053] Noise collection unit: used to collect aerodynamic noise data of blade aerodynamics, set noise threshold according to the normal operation status of blade aerodynamics, compare the noise data with the noise threshold, and determine the icing situation according to the comparison results, including:

[0054] The noise collection unit establishes a data connection with a noise detector installed at the tip of the wind turbine blade, thereby acquiring the aerodynamic noise data collected by the noise detector. The noise detector is installed near the tip of the wind turbine blade to effectively monitor the noise generated when the tip passes through the tower. At the same time, the icing condition is determined by detecting the size of the aerodynamic noise of the blade. When the leading edge of the blade is frozen, the aerodynamic shape will produce a turbulent boundary layer, emitting noise with a higher volume and frequency. The more severe the icing condition, the corresponding aerodynamic noise and audio frequency will gradually increase.

[0055] The noise collection unit processes the aerodynamic noise data using a noise data processing method, which specifically includes the following steps:

[0056] S11. Obtain the discrete time signal of the aerodynamic noise data and perform a fast Fourier transform on it to obtain a frequency domain signal, which is expressed as follows:

[0057]

[0058] Where X[k] is the value of the frequency domain signal at the kth frequency component, x[n] is the value of the discrete time signal at the nth sampling point, X is the frequency domain signal, N is the total number of sampling points of the signal, e and j are the phase information of the signal, and k is the index of the frequency component;

[0059] S12. Find the frequency components based on the frequency domain signal and calculate the amplitude of each frequency component, which is expressed as follows:

[0060]

[0061] Where |X[k]| is the amplitude, Re(X[k]) and Im(X[k]) are the real and imaginary parts of X[k], respectively;

[0062] S13, based on step S12, find the index of the frequency component with the largest amplitude, and finally calculate the main frequency according to the index, which is expressed as follows:

[0063]

[0064] Among them, k max is the index, f is the main frequency, f s is the sampling frequency of the signal.

[0065] The noise collection unit includes a noise threshold setting module and a noise comparison module. The noise threshold setting module is used to screen historical aerodynamic noise data according to the aerodynamic operation state of the blade, obtain historical aerodynamic noise data under the normal aerodynamic operation state of the blade, and then screen out the maximum historical aerodynamic noise data according to the historical aerodynamic noise data under the normal aerodynamic operation state of the blade, and set it as the noise threshold, specifically including:

[0066] Screening of historical aerodynamic noise data: From the historical aerodynamic noise data, first screen out the data when the blade aerodynamics is in normal operation, and then determine it by analyzing other parameters of the blade operation status, such as blade speed, wind speed, etc.;

[0067] Obtain the maximum historical aerodynamic noise data: in the data of normal operation of blade aerodynamics, find the data with the highest noise level, that is, the maximum historical aerodynamic noise data, by sorting or statistically analyzing the historical data;

[0068] Set noise threshold: Use the maximum historical aerodynamic noise data as the noise threshold to determine whether the current aerodynamic noise is abnormally higher than the normal level. The noise threshold is set as a fixed multiple of the maximum historical noise data or determined by statistical methods.

[0069] The noise comparison module is used to compare the collected real-time aerodynamic noise data with the noise threshold. When the real-time aerodynamic noise data exceeds the noise threshold, it is determined that ice has formed on the blade surface. Conversely, when the real-time aerodynamic noise data does not exceed the noise threshold, it is determined that no ice has formed on the blade surface. Specifically, it includes:

[0070] Real-time monitoring and judgment: In real-time monitoring, the aerodynamic noise data of the current blade is obtained through the noise detector, and the current aerodynamic noise data is compared with the set noise threshold to determine whether there is an abnormality;

[0071] Determine icing conditions: If the current aerodynamic noise data is higher than the set noise threshold, there is blade icing. Combined with other monitoring data, such as temperature and humidity, further confirm whether de-icing operations are required.

[0072] Temperature acquisition unit: used to collect temperature parameters on the blade surface, set an icing threshold for the temperature parameters, and then compare the temperature parameters with the icing threshold. When the temperature parameter is lower than the icing threshold, the historical temperature parameters are intercepted for analysis to obtain the temperature change trend of the blade surface, including:

[0073] The temperature acquisition unit establishes a data transmission connection with an ice detector installed at the root of the wind turbine blade, thereby measuring the temperature parameters and distribution conditions on the blade surface through the ice detector. The ice monitor includes an accessory thermistor or thermocouple installed at key positions on the blade surface from the tip to the root, such as the leading edge and the trailing edge, for measuring the blade surface temperature and distribution conditions. The thermistor detects changes in blade surface temperature parameters to monitor ice in real time, and preliminarily determines whether there is ice on the surface through temperature distribution and change trends.

[0074] The temperature acquisition unit includes a temperature threshold setting module and a temperature change acquisition module. The temperature threshold setting module is used to set the freezing threshold according to the computer terminal staff in the central control room of the wind farm, specifically including:

[0075] Defining the icing threshold: The icing threshold refers to the critical point at which ice will form when the temperature on the surface of the wind turbine blades and other environmental factors such as humidity and wind speed reach certain conditions. Usually, this threshold is set based on historical data, meteorological research and manufacturer's recommendations;

[0076] Temperature data collection: Use the ice detector at the root of the blade to collect relevant temperature data. Other meteorological data such as humidity, wind speed and precipitation are also needed, because these factors will also affect the nature of icing;

[0077] Parameter setting: The staff inputs the icing threshold through the computer terminal in the central control room. This usually includes the following parameters:

[0078] Temperature threshold: such as 0°C or slightly below 0°C, depending on other conditions;

[0079] Humidity threshold: Generally higher relative humidity increases the risk of icing;

[0080] Wind speed threshold: A certain wind speed will reduce the risk of icing because water is less likely to accumulate on the surface when the wind speed is high.

[0081] Programming and configuration: The staff needs to program in the control system, set the input interface of the parameters, and configure these parameters into the monitoring system.

[0082] Implementation and Monitoring: Once the thresholds are set, the system will monitor and compare the actual environmental data in real time.

[0083] The temperature change acquisition module is used to compare the temperature parameter with the freezing threshold. When the temperature parameter is lower than the freezing threshold, the historical temperature parameter is intercepted for analysis to obtain the temperature change trend of the blade surface. On the contrary, when the temperature parameter is not lower than the freezing threshold, normal monitoring is maintained, which specifically includes:

[0084] Comparison of temperature parameters with icing thresholds: When the monitored temperature parameters are lower than the preset icing threshold, the system triggers the icing risk detection and analysis procedure. If the temperature parameters are not lower than the icing threshold, the system continues to monitor normally without further processing;

[0085] Capture of historical temperature parameters: When the icing risk analysis is triggered, historical temperature parameter data over a period of time will be captured, usually data collected from the ice detector at the root of the blade. This data will serve as the basis for analysis to determine the temperature change trend of the blade surface;

[0086] Temperature change trend analysis: Use the intercepted historical temperature parameter data for analysis to determine the change trend of the blade surface temperature. Statistical methods, data mining techniques or machine learning algorithms can be used to analyze the temperature data to identify whether the temperature shows a tendency to freeze;

[0087] Icing risk assessment: Based on the results of the temperature change trend analysis, the icing risk is assessed. If the temperature change trend indicates that the temperature of the blade surface may drop below the freezing point and other environmental conditions also meet the conditions for icing, the system determines that there is an icing risk.

[0088] Actual calculation unit: used to collect the real-time wind speed at the hub height of the wind turbine, the temperature and humidity of the environment at the hub height of the wind turbine, and the atmospheric pressure value at the hub height of the wind turbine, and then correct and convert it into the actual value of the wind turbine facing the incoming wind, including:

[0089] The actual calculation unit establishes a data transmission connection with the anemometer, the air temperature and humidity sensor, and the atmospheric pressure sensor, collects the real-time wind speed at the hub height of the wind turbine generator set by using the anemometer, collects the temperature and humidity of the environment at the hub height of the wind turbine generator set by using the air temperature and humidity sensor, and collects the atmospheric pressure value at the hub height of the wind turbine generator set by using the atmospheric pressure sensor;

[0090] The anemometer is installed at the tail of the nacelle of the wind turbine generator set and adopts a cup anemometer;

[0091] The air temperature and humidity sensor is installed at the front end of the top of the wind turbine nacelle close to the fan blade hub; the atmospheric pressure sensor is installed on the top of the wind turbine nacelle or inside the nacelle.

[0092] The actual calculation unit corrects and converts the actual value of the wind turbine facing the wind. The wind speed behind the blade measured by the anemometer on the upper part of the nacelle is affected by the aerodynamic effect of the blade, which will cause the wind speed to decrease and cannot accurately reflect the actual free airflow speed in front of the blade that drives the wind turbine generator set. Specifically, the following steps are included:

[0093] S31. Calculate the actual inflow angle of the blade, expressed as follows:

[0094] AOA=α-β

[0095] Among them, AOA is the actual inflow angle of the blade, α is the relative inflow angle of the blade, and β is the installation angle of the blade, which is obtained by looking up the design parameters of the unit;

[0096] S32. According to the aerodynamic characteristic curve CL-CD of the blade, the corresponding lift coefficient and drag coefficient are found based on the actual inflow angle, and the local lift and drag of the blade are calculated according to the lift coefficient and the drag coefficient, which are expressed as follows:

[0097]

[0098] Where L is lift, D is drag, ρ is air density, V2 is wind speed behind the blade, and c is blade reference area (in m 2 ), CL is the lift coefficient of the actual inflow angle, CD is the drag coefficient of the actual inflow angle, which is calculated by measuring the data of the air temperature and humidity sensor and the atmospheric pressure sensor;

[0099] S33, the lift and drag of the blade are related to the wind speed before and after the blade, which is expressed as follows:

[0100]

[0101] Among them, V1 is the wind speed in front of the blade, T and Q are the axial thrust and tangential torque respectively;

[0102] S34, based on step S33, the nonlinear equations for calculating the wind speed in front of the blade and the wind speed behind the blade are expressed as follows:

[0103]

[0104] Among them, V1 is the wind speed in front of the blade, and V2 is the wind speed behind the blade. The corrected value of the wind speed in front of the blade is obtained by an iterative method or a numerical solution method.

[0105] De-icing scheme generation unit: used to collect the heating power and pitch parameters of the wind turbine, generate a de-icing scheme for the blades by combining the heating power with the pitch parameters, and obtain a list of feasible de-icing schemes, including:

[0106] Data collection: Collect the heating power and pitch parameters of the wind turbine. The heating power can be obtained through the power sensor of the heating system, while the pitch parameters may include information such as blade angle and speed, which can be obtained through the wind turbine's pitch system.

[0107] De-icing scheme generation: Combine heating power and pitch parameters to develop a blade de-icing scheme, which may involve the following considerations:

[0108] Blade condition monitoring: Use sensors to monitor blade temperature and ice conditions to determine if de-icing operations are needed;

[0109] Heating power adjustment: adjust the heating power according to the degree of icing on the blades and environmental conditions. Generally, the more severe the icing, the greater the required heating power.

[0110] Pitch parameter adjustment: Considering that blade icing may affect the performance of wind turbines, the impact of icing on performance can be mitigated by adjusting the pitch parameters. For example, the blade angle can be adjusted to change the air intake angle of the blade to reduce the possibility of icing.

[0111] List of feasible deicing schemes: The deicing schemes generated according to the heating power and pitch parameters are sorted into a list, including different schemes and corresponding operating conditions and expected effects, as shown below:

[0112]

[0113] Among them, P heating is the heating power, k is the thermal conductivity of the material, A is the surface area of ​​the blade, ΔT is the temperature difference, that is, the difference between the surface temperature of the blade and the ambient temperature, d is the thickness of the blade, ρ is the density of the blade, c is the specific heat capacity of the blade, V is the volume of the blade, f(θ) is a function that represents the effect of the pitch angle on the heating power. It is a function determined according to the control algorithm and the characteristics of the wind turbine. The specific operation steps are as follows:

[0114] First, by monitoring the difference ΔT between the blade surface temperature and the ambient temperature, determine whether the blade is frozen. If the temperature difference is large enough, it indicates that the blade is frozen and needs to be de-iced. The heat conduction equation is used to calculate the heat transfer rate of the blade, which is expressed as follows:

[0115]

[0116] Where Q is the heat transfer rate of the blade;

[0117] Secondly, the required heating power is calculated based on the heat transfer rate and the density, specific heat capacity and volume of the blade, as shown below:

[0118] P heating =Q·ρ·c·V

[0119] Finally, the effect of the pitch angle on the heating power is added, which is reflected in the f(θ) function. According to the control algorithm and wind turbine characteristics, the pitch angle is adjusted to change the air intake angle of the blades, thereby affecting the icing of the blades. The above steps are integrated together, and the required heating power is calculated according to information such as ambient temperature, blade status, and wind turbine parameters. According to the real-time monitoring data and the preset strategy, the corresponding blade deicing plan is implemented.

[0120] De-icing scheme selection unit: used to combine the icing situation determined by the noise collection unit, the temperature change trend of the blade surface obtained by the temperature collection unit, and the actual value obtained by the actual calculation unit to perform de-icing analysis, adjust and select the de-icing scheme list obtained by the de-icing scheme generation unit based on the de-icing analysis results, and send the adjusted and selected de-icing scheme to the wind farm computer terminal, which controls the de-icing of the blades according to the de-icing scheme. Specifically, it includes:

[0121] Obtaining a list of deicing solutions: Obtaining a list of predefined deicing solutions, which includes specific operation plans for different icing conditions;

[0122] De-icing analysis: De-icing analysis is performed based on the icing conditions, temperature change trends and actual values ​​to determine the optimal de-icing solution;

[0123] Adjust and select de-icing schemes: According to the de-icing analysis results, adjust and select the de-icing scheme that best suits the current situation, and prioritize or adjust the de-icing schemes according to the analysis results;

[0124] De-icing scheme is sent to the computer terminal of the wind farm: the adjusted and selected de-icing scheme is sent to the computer terminal of the wind farm so as to implement the de-icing operation;

[0125] Wind farm computer terminals control blade de-icing: According to the de-icing plan received, the wind farm computer terminals perform de-icing control operations on the blades.

[0126] Another embodiment of the present invention provides a wind turbine blade icing monitoring and deicing control method, the method comprising the following steps:

[0127] S101, determining the icing condition through a noise collection unit, obtaining the temperature change trend through a temperature collection unit, and obtaining the actual value through an actual calculation unit;

[0128] S102, a deicing scheme generating unit obtains a deicing scheme list of a wind turbine;

[0129] S103, using the de-icing scheme selection unit to combine the noise collection unit, the temperature collection unit and the actual calculation unit to perform de-icing analysis, combining the de-icing analysis result with the de-icing scheme generation unit for adjustment and selection, sending the adjusted and selected de-icing scheme to the wind farm computer terminal, and the wind farm computer terminal controls the de-icing of the blades according to the de-icing scheme.

[0130] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A wind turbine blade icing monitoring and deicing control system, characterized in that: include: Noise collection unit: used to collect aerodynamic noise data of blade aerodynamics, set a noise threshold according to the normal operating state of blade aerodynamics, compare the aerodynamic noise data with the noise threshold, and determine the icing condition according to the comparison result; Temperature collection unit: used to collect temperature parameters on the blade surface, set an icing threshold for the temperature parameters, compare the temperature parameters with the icing threshold, and when the temperature parameters are lower than the icing threshold, intercept the historical temperature parameters for analysis to obtain the temperature change trend on the blade surface; Actual calculation unit: used to collect the real-time wind speed, temperature, humidity and atmospheric pressure values ​​at the hub height of the wind turbine, and then correct and convert them into the actual values ​​of the wind turbine facing the incoming wind; De-icing scheme generation unit: used to collect the heating power and pitch parameters of the wind turbine, generate a blade de-icing scheme by combining the heating power and pitch parameters, and obtain a list of feasible de-icing schemes; De-icing scheme selection unit: used to combine the icing conditions determined by the noise collection unit, the temperature change trend of the blade surface obtained by the temperature collection unit, and the actual value obtained by the actual calculation unit to perform de-icing analysis, and adjust and select the de-icing scheme list obtained by the de-icing scheme generation unit based on the de-icing analysis results, and send the adjusted and selected de-icing scheme to the wind farm computer terminal, which controls the de-icing of the blades according to the de-icing scheme.

2. A wind turbine blade icing monitoring and deicing control system according to claim 1, characterized in that: The noise collection unit processes the aerodynamic noise data using a noise data processing method, which specifically includes the following steps: S11. Obtain the discrete time signal of the aerodynamic noise data and perform a fast Fourier transform on it to obtain a frequency domain signal, which is expressed as follows: Where X[k] is the value of the frequency domain signal at the kth frequency component, x[n] is the value of the discrete time signal at the nth sampling point, X is the frequency domain signal, N is the total number of sampling points of the signal, e and j are the phase information of the signal, and parameter k is the index of the frequency component; S12. Find the frequency components based on the frequency domain signal and calculate the amplitude of each frequency component, which is expressed as follows: Where |X[k]| is the amplitude, Re(X[k]) and Im(X[k]) are the real and imaginary parts of X[k], respectively; S13, based on step S12, find the index of the frequency component with the largest amplitude, and finally calculate the main frequency according to the index, which is expressed as follows: Among them, k max is the index, f is the main frequency, f s is the sampling frequency of the signal.

3. The wind turbine blade icing monitoring and deicing control system according to claim 1, characterized in that: The noise collection unit includes a noise threshold setting module and a noise comparison module; The noise threshold setting module is used to filter out historical aerodynamic noise data according to the aerodynamic operation state of the blade, obtain historical aerodynamic noise data under the normal aerodynamic operation state of the blade, and then filter out the maximum historical aerodynamic noise data according to the historical aerodynamic noise data under the normal aerodynamic operation state of the blade, and set it as the noise threshold; The noise comparison module is used to compare the collected real-time aerodynamic noise data with the noise threshold. When the real-time aerodynamic noise data exceeds the noise threshold, it is determined that ice has appeared on the blade surface. Conversely, when the real-time aerodynamic noise data does not exceed the noise threshold, it is determined that no ice has appeared on the blade surface.

4. The wind turbine blade icing monitoring and deicing control system according to claim 1, characterized in that: The temperature acquisition unit includes a temperature threshold setting module and a temperature change acquisition module; The temperature threshold setting module is used to set the freezing threshold according to the computer terminal staff in the central control room of the wind farm; The temperature change acquisition module is used to compare the temperature parameters with the freezing threshold. When the temperature parameters are lower than the freezing threshold, the historical temperature parameters are intercepted for analysis to obtain the temperature change trend of the blade surface. Conversely, when the temperature parameters are not lower than the freezing threshold, normal monitoring is maintained.

5. The wind turbine blade icing monitoring and deicing control system according to claim 1, characterized in that: The actual calculation unit corrects and converts the actual value of the wind turbine facing the incoming wind, specifically including the following steps: S31. Calculate the actual inflow angle of the blade, expressed as follows: AOA=α-β Among them, AOA is the actual inflow angle of the blade, α is the relative inflow angle of the blade, and β is the installation angle of the blade; S32, searching for the corresponding lift coefficient and drag coefficient based on the actual inflow angle, and calculating the local lift and drag of the blade according to the lift coefficient and drag coefficient, as shown below: Where L is lift, D is drag, ρ is air density, V2 is wind speed behind the blade, c is blade reference area, CL is lift coefficient at actual inflow angle, CD is drag coefficient at actual inflow angle; S33, the lift and drag of the blade are related to the wind speed before and after the blade, which is expressed as follows: D=(V1-V2)Q Among them, V1 is the wind speed in front of the blade, T and Q are the axial thrust and tangential torque respectively; S34, based on step S33, the nonlinear equations for calculating the wind speed in front of the blade and the wind speed behind the blade are expressed as follows: Among them, V1 is the wind speed before the blade, and V2 is the wind speed after the blade.

6. A wind turbine blade icing monitoring and deicing control method, the method comprising the following steps: S101, determining the icing condition through a noise collection unit, obtaining the temperature change trend through a temperature collection unit, and obtaining the actual value through an actual calculation unit; S102, a deicing scheme generating unit obtains a deicing scheme list of a wind turbine; S103, using the de-icing scheme selection unit to combine the noise collection unit, the temperature collection unit and the actual calculation unit to perform de-icing analysis, combining the de-icing analysis result with the de-icing scheme generation unit for adjustment and selection, sending the adjusted and selected de-icing scheme to the wind farm computer terminal, and the wind farm computer terminal controls the de-icing of the blades according to the de-icing scheme.

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