A wind turbine blade icing monitoring and de-icing control system and method

Abstract

This invention provides a wind turbine blade icing monitoring and de-icing control system and method. The system includes a noise acquisition unit, a temperature acquisition unit, an actual calculation unit, a de-icing scheme generation unit, and a de-icing scheme selection unit. By combining these with the wind turbine's operating parameters, the system monitors and analyzes these parameters, including wind speed, air temperature, humidity, blade rotation speed, and power generation. Simultaneously, through the monitoring system and sensors, it performs real-time monitoring, acquisition, processing, and storage of the wind turbine's operating parameters and environmental data. This enables the monitoring, alarming, and analysis of blade icing conditions, determining the optimal de-icing strategy and timing to maximize de-icing effectiveness and reduce unit energy consumption, thereby improving unit power generation efficiency and economy, and achieving the goal of reliable, efficient, and economical wind turbine operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and in particular to a wind turbine blade icing monitoring and de-icing control system and method. Background Technology

[0002] As a clean and renewable energy power generation device, wind turbines have received increasing attention and importance. However, in winter or low-temperature environments, wind turbine blades are susceptible to icing, which can lead to a decrease in the performance of the wind turbine and even cause the unit to shut down. In response to the problem of wind turbine blade icing, some related technical solutions have emerged. One common solution is to spray anti-icing fluid on the blade surface to prevent icing. However, anti-icing fluid is expensive and needs to be replenished and replaced regularly, which has a certain impact on power generation costs and environmental protection.

[0003] Another approach is to use mechanical vibration or heating for de-icing. However, this method suffers from high energy consumption, complex equipment, and high operating costs, and it also cannot accurately predict and monitor blade icing. A commonly used active de-icing method is blade heating de-icing, which uses the measurement of blade surface temperature to determine if the blade is icy. When the blade surface temperature falls below a certain threshold, it is considered icy and de-icing is required. This is achieved by installing electric heating wires or films inside the blade, heating the interior and conducting the heat to the surface, thus melting the ice. However, the single-sensor detection technology of electric heating de-icing has significant limitations for wind turbine blades. Firstly, due to the uneven icing of wind turbine blades and the limited number of sensors, there are large detection blind spots. Secondly, it consumes a large amount of electricity, with energy consumption depending on the blade heating power, de-icing time, and the number of de-icing cycles. The de-icing time and cycles depend on the icing condition and environmental conditions, making precise, adjustable, and controllable heating de-icing difficult to achieve. This leads to increased de-icing energy consumption and affects the economic efficiency of power generation.

[0004] Currently, existing technologies in this field cannot effectively monitor blade icing or take timely measures for precise de-icing. There are relatively few wind turbine blade icing monitoring and de-icing control systems and data acquisition and processing methods. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a wind turbine blade icing monitoring and de-icing control system and method to solve the problem that existing technical solutions cannot accurately monitor wind turbine blade icing and cannot help staff take appropriate measures to de-ic it in a timely manner.

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

[0007] Noise acquisition unit: used to collect aerodynamic noise data of blade aerodynamics, set noise threshold according to the normal operating state of blade aerodynamics, compare noise data with noise threshold, and determine icing status based on comparison results;

[0008] Temperature acquisition unit: used to collect temperature parameters of the blade surface, set 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, extract historical temperature parameters for analysis to obtain the temperature change trend of the blade surface.

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

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

[0011] De-icing scheme selection unit: This unit combines the icing conditions determined by the noise acquisition unit, the temperature change trend of the blade surface obtained by the temperature acquisition unit, and the actual values ​​obtained by the actual calculation unit to perform de-icing analysis. The de-icing analysis results are then combined with the de-icing scheme list obtained by the de-icing scheme generation unit for adjustment and selection. The adjusted and selected de-icing scheme is then sent to the wind farm computer terminal, which controls the de-icing of the blades according to the de-icing scheme.

[0012] Furthermore, the noise acquisition unit processes the aerodynamic noise data using a noise data processing method, specifically including 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 the frequency domain signal, as shown below:

[0014]

[0015] Where X[k] is the value of the frequency domain signal at the k-th frequency component, x[n] is the value of the discrete-time signal at the n-th 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. Based on the frequency domain signal, find the frequency components and calculate the amplitude of each frequency component, as shown below:

[0017]

[0018] Where |X[k]| is the magnitude, and 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 based on the index, as shown below:

[0020]

[0021] Where, k max For index, f is the main frequency, f s The sampling frequency of the signal.

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

[0023] The noise threshold setting module is used to filter historical aerodynamic noise data according to the aerodynamic operating state of the blade, obtain historical aerodynamic noise data under normal blade aerodynamic operating state, and then filter out the maximum historical aerodynamic noise data according to the historical aerodynamic noise data under normal blade aerodynamic operating state 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 icing has occurred on the blade surface. Conversely, when the real-time aerodynamic noise data does not exceed the noise threshold, it is determined that icing has not occurred 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 icing threshold according to the computer terminal staff in the wind farm control room;

[0027] The temperature change acquisition module is used to compare temperature parameters with icing thresholds. When the temperature parameter is lower than the icing threshold, historical temperature parameters are extracted and analyzed to obtain the temperature change trend of the blade surface. Conversely, when the temperature parameter is not lower than the icing threshold, normal monitoring is maintained.

[0028] Furthermore, the actual calculation unit corrects and converts the values ​​to reflect the actual wind conditions directly facing the wind turbine, specifically including the following steps:

[0029] S31. Calculate the actual inflow angle of the blade, as shown below:

[0030] AOA=α-β

[0031] Where 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. Based on the actual inflow angle, find the corresponding lift coefficient and drag coefficient, and calculate 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 of actual inflow angle, and CD is drag coefficient of actual inflow angle.

[0035] S33. The local lift and drag of the blades are correlated with the wind speeds in front of and behind the blades, as shown below:

[0036]

[0037] D = (V1 - V2)Q

[0038] Where V1 is the wind speed in front of the blade, and 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] Where V1 is the wind speed in front of the blade and V2 is the wind speed behind the blade.

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

[0043] S101. The icing situation is determined by the noise acquisition unit, the temperature change trend is obtained by the temperature acquisition unit, and the actual value is obtained by the actual calculation unit.

[0044] S102, The de-icing scheme generation unit obtains the de-icing scheme list of the wind turbine;

[0045] S103. The de-icing scheme selection unit combines the noise acquisition unit, temperature acquisition unit, and actual calculation unit to perform de-icing analysis. The de-icing analysis results are combined with the de-icing scheme generation unit for adjustment and selection. The adjusted and selected de-icing scheme is sent to the wind farm computer terminal, which then controls the blades to de-ic according to the de-icing scheme.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] This invention proposes a wind turbine blade icing monitoring and de-icing control system and method. By integrating with the wind turbine's operating parameters, the system monitors and analyzes these parameters, including wind speed, air temperature, humidity, blade rotation speed, and power generation. Simultaneously, through a monitoring system and sensors, it performs real-time monitoring, data acquisition, processing, and storage of the wind turbine's operating parameters and environmental data. This enables the monitoring, alarming, and analysis of blade icing conditions, determining the optimal de-icing strategy and timing to maximize de-icing effectiveness and reduce unit energy consumption, thereby improving power generation efficiency and economy, and achieving the goal of reliable, efficient, and economical wind turbine operation and maintenance. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 The schematic diagram of the overall structure of the wind turbine blade icing monitoring and de-icing control system provided in this embodiment of the invention. Detailed Implementation

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

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

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

[0053] Noise acquisition unit: Used to collect aerodynamic noise data of the blades, set noise thresholds based on the normal operating state of the blades, compare the noise data with the noise thresholds, and determine the icing status based on the comparison results. Specifically, it includes:

[0054] The noise acquisition unit establishes a data connection with a noise detector installed at the tip of the wind turbine blade to acquire 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 blade tip passes through the tower. At the same time, the icing situation is determined by detecting the magnitude of the blade aerodynamic noise. When the leading edge of the blade is iced, the aerodynamic shape will generate a turbulent boundary layer, which will emit noise with higher volume and frequency. The more severe the icing, the higher the aerodynamic noise and audio frequency.

[0055] The noise acquisition unit processes the aerodynamic noise data using a noise data processing method, specifically including 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 the frequency domain signal, as shown below:

[0057]

[0058] Where X[k] is the value of the frequency domain signal at the k-th frequency component, x[n] is the value of the discrete-time signal at the n-th 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. Based on the frequency domain signal, find the frequency components and calculate the amplitude of each frequency component, as shown below:

[0060]

[0061] Where |X[k]| is the magnitude, and 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 based on the index, as shown below:

[0063]

[0064] Where, k max For index, f is the main frequency, f s The sampling frequency of the signal.

[0065] The noise acquisition unit includes a noise threshold setting module and a noise comparison module. The noise threshold setting module is used to filter historical aerodynamic noise data according to the blade aerodynamic operating state, acquire historical aerodynamic noise data under normal blade aerodynamic operating state, and then filter out the maximum historical aerodynamic noise data based on the historical aerodynamic noise data under normal blade aerodynamic operating state, and set it as the noise threshold. Specifically, it includes:

[0066] Historical aerodynamic noise data screening: From the historical aerodynamic noise data, the data of the blade aerodynamics under normal operating conditions are first screened out, and then determined by analyzing other parameters of the blade operating state, such as blade speed and wind speed.

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

[0068] Set a 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 according to statistical methods.

[0069] The noise comparison module is used to compare the collected real-time aerodynamic noise data with a noise threshold. When the real-time aerodynamic noise data exceeds the noise threshold, it is determined that icing has occurred on the blade surface; conversely, when the real-time aerodynamic noise data does not exceed the noise threshold, it is determined that icing has not occurred on the blade surface. Specifically, this includes:

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

[0071] Determine icing status: If the current aerodynamic noise data is higher than the set noise threshold, there is icing on the blades. Combine this with other monitoring data, such as temperature and humidity, to further confirm whether de-icing is necessary.

[0072] Temperature acquisition unit: Used to collect temperature parameters of 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, historical temperature parameters are extracted and analyzed to obtain the temperature change trend of the blade surface, specifically including:

[0073] The temperature acquisition unit establishes a data transmission connection with an icing detector installed at the root of the wind turbine blade. The icing detector measures the temperature parameters and distribution on the blade surface. The icing monitor includes accessories such as thermal sensors or thermocouples installed at key locations on the blade surface from the tip to the root, such as the leading and trailing edges, to measure the surface temperature and distribution. The thermal sensors detect changes in the blade surface temperature parameters to monitor icing in real time. By analyzing the temperature distribution and trends, a preliminary judgment is made as to whether icing exists on the surface.

[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 an icing threshold according to the computer terminal settings of the personnel in the wind farm control room, specifically including:

[0075] Icing threshold definition: The icing threshold refers to the critical point at which icing occurs when the surface temperature of a wind turbine blade and other environmental factors such as humidity and wind speed reach a certain level. Typically, this threshold is set based on historical data, meteorological studies, and manufacturer recommendations.

[0076] Temperature data acquisition: Use icing detectors at the base of the blades to collect relevant temperature data. Other meteorological data, such as humidity, wind speed and precipitation, are also needed, as these factors can affect icing.

[0077] Parameter settings: Staff input the icing threshold through the computer terminal in the central control room. This typically includes the following parameters:

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

[0079] Humidity threshold: Higher relative humidity generally 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: Staff need to program the control system, set the parameter input interface, and configure these parameters into the monitoring system.

[0082] Implementation and monitoring: Once the threshold is set, the system will monitor and compare the actual environmental data in real time.

[0083] The temperature change acquisition module is used to compare temperature parameters with icing thresholds. When the temperature parameter is lower than the icing threshold, historical temperature parameters are extracted and analyzed to obtain the temperature change trend of the blade surface. Conversely, when the temperature parameter is not lower than the icing threshold, normal monitoring is maintained. Specifically, this includes:

[0084] Temperature parameter comparison with freezing threshold: When the monitored temperature parameter is lower than the preset freezing threshold, the system will trigger the freezing risk detection and analysis program. If the temperature parameter is not lower than the freezing threshold, the system will continue to monitor normally without further processing.

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

[0086] Temperature change trend analysis: Historical temperature parameter data is extracted and analyzed to determine the trend of leaf surface temperature change. 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 temperature change trend analysis, the icing risk is assessed. If the temperature change trend indicates that the temperature on the blade surface may drop below the freezing point, and other environmental conditions also meet the conditions for icing, then the system determines that there is an icing risk.

[0088] Actual calculation unit: Used to collect real-time wind speed, ambient temperature and humidity at the hub height of the wind turbine, and atmospheric pressure at the hub height of the wind turbine. Then, it corrects and converts these values ​​into the actual values ​​of the wind turbine facing the oncoming wind. Specifically, it includes:

[0089] The actual calculation unit establishes a data transmission connection with the anemometer, air temperature and humidity sensor and atmospheric pressure sensor. It uses the anemometer to collect the real-time wind speed at the hub height of the wind turbine, the air temperature and humidity sensor to collect the ambient temperature and humidity at the hub height of the wind turbine, and the atmospheric pressure sensor to collect the atmospheric pressure value at the hub height of the wind turbine.

[0090] The anemometer is installed at the rear of the wind turbine nacelle and is a cup-type anemometer.

[0091] The air temperature and humidity sensor is installed at the front end of the top of the wind turbine nacelle, near the hub of the wind turbine blades; 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's frontal wind. The wind speed behind the blades measured by the anemometer on the upper part of the nacelle is affected by the aerodynamic effect of the blades, which will cause the wind speed to decrease and cannot accurately reflect the actual free blowing direction in front of the blades and the airflow speed that drives the wind turbine. The specific steps include:

[0093] S31. Calculate the actual inflow angle of the blade, as shown below:

[0094] AOA=α-β

[0095] Where 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 unit design parameters;

[0096] S32. Based on the aerodynamic characteristic curve CL-CD of the blade, find the corresponding lift coefficient and drag coefficient based on the actual inflow angle, and calculate the local lift and drag of the blade based on the lift coefficient and drag coefficient, as shown below:

[0097]

[0098] Where L is lift, D is drag, ρ is air density, V2 is the wind speed behind the blade, and c is the blade reference area (in m²). 2 ), where CL is the lift coefficient of the actual inflow angle and CD is the drag coefficient of the actual inflow angle, calculated using data measured by air temperature and humidity sensors and atmospheric pressure sensors;

[0099] S33. The local lift and drag of the blades are correlated with the wind speeds in front of and behind the blades, as shown below:

[0100]

[0101] Where V1 is the wind speed in front of the blade, and 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] Where 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 iterative method or numerical solution method.

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

[0106] Data acquisition: 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 rotational speed, which can be obtained through the pitch system of the wind turbine.

[0107] De-icing scheme generation: A blade de-icing scheme is developed by combining heating power and pitch parameters. This scheme may involve the following considerations:

[0108] Blade condition monitoring: Sensors are used to monitor the temperature and icing status of the blades to determine whether de-icing is required;

[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 heating power required.

[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 pitch parameters. For example, adjusting the blade angle to change the blade's air intake angle can reduce the likelihood of icing.

[0111] List of feasible de-icing solutions: The de-icing solutions generated based on heating power and pitch parameters are compiled into a list, including different solutions, corresponding operating conditions, and expected effects, as shown below:

[0112]

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

[0114] First, by monitoring the temperature difference ΔT between the blade surface and the ambient temperature, it is determined whether the blade is icing. If the temperature difference is large enough, it indicates that the blade is already icing and de-icing is required. The heat transfer rate of the blade is calculated using the heat conduction equation, as shown below:

[0115]

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

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

[0118] P heating =Q·ρ·c·V

[0119] Finally, the influence of pitch angle on heating power is incorporated, which is reflected in the f(θ) function. Based on 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 based on information such as ambient temperature, blade condition, and wind turbine parameters. Based on real-time monitoring data and preset strategies, the corresponding blade de-icing scheme is implemented.

[0120] The de-icing scheme selection unit combines the icing conditions determined by the noise acquisition unit, the temperature change trend of the blade surface acquired by the temperature acquisition unit, and the actual values ​​acquired by the actual calculation unit to perform de-icing analysis. It then adjusts and selects the de-icing scheme based on the de-icing scheme generation unit's list of schemes, and sends the adjusted and selected scheme to the wind farm computer terminal. The wind farm computer terminal then controls the blade de-icing according to the scheme. Specifically, this includes:

[0121] De-icing solution list acquisition: Retrieves a predefined list of de-icing solutions, which includes specific operational plans for different icing conditions;

[0122] De-icing analysis: Based on the icing situation, temperature change trend and actual values, a de-icing analysis is conducted to determine the optimal de-icing solution;

[0123] Adjust the selected de-icing scheme: Based on the de-icing analysis results, adjust and select the de-icing scheme that is most suitable for the current situation, and prioritize or adjust the de-icing schemes according to the analysis results;

[0124] Send the de-icing plan to the wind farm computer terminal: Send the selected de-icing plan to the wind farm computer terminal so that the de-icing operation can be carried out;

[0125] Wind farm computer terminal controls blade de-icing: Based on the received de-icing plan, the wind farm computer terminal performs de-icing control operations on the blades.

[0126] Another embodiment of the present invention provides a method for monitoring and controlling icing on wind turbine blades, the method comprising the following steps:

[0127] S101. The icing situation is determined by the noise acquisition unit, the temperature change trend is obtained by the temperature acquisition unit, and the actual value is obtained by the actual calculation unit.

[0128] S102, The de-icing scheme generation unit obtains the de-icing scheme list of the wind turbine;

[0129] S103. The de-icing scheme selection unit combines the noise acquisition unit, temperature acquisition unit, and actual calculation unit to perform de-icing analysis. The de-icing analysis results are combined with the de-icing scheme generation unit for adjustment and selection. The adjusted and selected de-icing scheme is sent to the wind farm computer terminal, which then controls the blades to de-ic 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wind turbine blade icing monitoring de-icing control system, characterized in that, The application relates to a wind turbine blade icing detection and deicing scheme generation method. The noise acquisition unit is used for acquiring aerodynamic noise data of a blade, setting a noise threshold value according to a normal operation state of blade aerodynamics, comparing the aerodynamic noise data with the noise threshold value, and determining an icing condition according to a comparison result; the noise acquisition unit uses a noise data processing method to process the aerodynamic noise data, and the method specifically comprises the following steps: S11. A discrete time signal of the aerodynamic noise data is acquired, and a frequency domain signal is obtained by performing fast Fourier transform on the discrete time signal, and is expressed as follows: Wherein, 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 signal sampling points, e and j are the phase information of the signal, and k is the index of the frequency component; S12. The frequency component is searched based on the frequency domain signal, the amplitude of each frequency component is calculated, and is expressed as follows: Wherein, |X[k]| is the amplitude, Re(X[k]) and Im(X[k]) are the real part and the imaginary part of X[k] respectively; S13. The index of the frequency component with the maximum amplitude is searched based on step S12, and the main frequency is calculated according to the index, and is expressed as follows: where k max is an index, f is the fundamental frequency, and f s is the sampling frequency of the signal. The temperature acquisition unit is used for acquiring temperature parameters of a blade surface, setting an icing threshold value for the temperature parameters, comparing the temperature parameters with the icing threshold value, and intercepting historical temperature parameters for analysis when the temperature parameters are lower than the icing threshold value to obtain a temperature change trend of the blade surface; The actual calculation unit is used for acquiring real-time wind speed, temperature and humidity and atmospheric pressure values at a hub height of a wind turbine generator, and then correcting and converting the values into actual values of wind power machine front wind, and the actual calculation unit specifically comprises the following steps: S31. The actual inflow angle of the blade is calculated, and is expressed as follows: AOA = alpha-beta Wherein, AOA is the actual inflow angle of the blade, alpha is the relative inflow angle of the blade, and beta is the installation angle of the blade; S32. The corresponding lift coefficient and drag coefficient of the actual inflow angle are searched 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, and are expressed as follows: Wherein, L is the lift, D is the drag, rho is the air density, V2 is the blade rear wind speed, c is the blade reference area, CL is the lift coefficient of the actual inflow angle, and CD is the drag coefficient of the actual inflow angle; S33. The local lift and drag of the blade are associated with the blade front and rear wind speeds, and are expressed as follows: D = (V1-V2)Q Wherein, V1 is the blade front wind speed, T and Q are respectively the axial thrust and the tangential torque; S34. The nonlinear equation set of the blade front wind speed and the blade rear wind speed is calculated based on step S33, and is expressed as follows: Wherein, V1 is the blade front wind speed, and V2 is the blade rear wind speed; The deicing scheme generation unit is used for acquiring heating power and variable pitch parameters of the wind turbine generator, generating a blade deicing scheme by combining the heating power and the variable pitch parameters, and obtaining an implementable deicing scheme list. The deicing scheme selection unit is used to combine the icing condition determined by the noise collection unit, the temperature variation trend obtained by the temperature collection unit, and the actual value obtained by the actual calculation unit, and to adjust and select the deicing scheme based on the deicing analysis result and the deicing scheme list obtained by the deicing scheme generation unit, and to send the adjusted and selected deicing scheme to the wind farm computer terminal for deicing control of the blade according to the deicing scheme.

2. A wind turbine blade icing monitoring and de-icing control system according to claim 1, characterised in that, The noise collection unit comprises 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 running state of the blade, obtain historical aerodynamic noise data under the normal aerodynamic running state of the blade, then screen out the maximum historical aerodynamic noise data 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, and when the real-time aerodynamic noise data exceeds the noise threshold, it is determined that the blade surface is in icing condition, otherwise, when the real-time aerodynamic noise data does not exceed the noise threshold, it is determined that the blade surface is not in icing condition.

3. A wind turbine blade icing monitoring and de-icing control system according to claim 1, wherein, The temperature collection unit comprises a temperature threshold setting module and a temperature variation collection module. The temperature threshold setting module is used to set the icing threshold according to the computer terminal staff in the wind farm control room. The temperature variation collection module is used to compare the temperature parameter with the icing threshold, and when the temperature parameter is lower than the icing threshold, the historical temperature parameter is intercepted for analysis to obtain the temperature variation trend of the blade surface, otherwise, when the temperature parameter is not lower than the icing threshold, normal monitoring is maintained.

4. A wind turbine blade icing monitoring and deicing control method, the method comprising the following steps: S101, determining the icing condition by the noise collection unit, obtaining the temperature variation trend by the temperature collection unit, and obtaining the actual value by the actual calculation unit, wherein the noise collection unit uses a noise data processing method to process aerodynamic noise data, which specifically comprises the following steps: S11, obtaining the discrete time signal of the aerodynamic noise data and performing fast Fourier transform to obtain the frequency domain signal, which is expressed as follows: wherein 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 signal sampling points, e and j are the phase information of the signal, and parameter k is the index of the frequency component; S12, finding the frequency component based on the frequency domain signal, calculating the amplitude of each frequency component, which is expressed as follows: wherein |X[k]| is the amplitude, Re(X[k]) and Im(X[k]) are the real part and imaginary part of X[k], respectively; S13, finding the index of the frequency component with the maximum amplitude based on step S12, and finally calculating the main frequency according to the index, which is expressed as follows: where k max is an index, f is the fundamental frequency, f s is the sampling frequency of the signal; The actual calculation unit calculates the actual value of the wind turbine facing the incoming wind by correction and conversion, which specifically comprises the following steps: S31, calculating the actual inflow angle of the blade, which is expressed as follows: AOA = a - b Wherein, AOA is the actual inflow angle of the blade, a is the relative inflow angle of the blade, and β is the installation angle of the blade; S32, based on the actual inflow angle, the corresponding lift coefficient and drag coefficient are searched, and the local lift and drag of the blade are calculated according to the lift coefficient and the drag coefficient, which are represented as follows: Wherein, L is the lift, D is the drag, ρ is the air density, V2 is the blade back wind speed, c is the blade reference area, CL is the lift coefficient of the actual inflow angle, and CD is the drag coefficient of the actual inflow angle; S33, the local lift and drag of the blade are associated with the front and back wind speed of the blade, which are represented as follows: D = (V1-V2)Q Wherein, V1 is the front wind speed of the blade, T and Q are the axial thrust and tangential torque respectively; S34, based on step S33, the nonlinear equation set of the front wind speed of the blade and the back wind speed of the blade is calculated, which is represented as follows: Wherein, V1 is the front wind speed of the blade, and V2 is the back wind speed of the blade; S102, the deicing scheme generation unit obtains a deicing scheme list of the wind turbine; S103, the deicing analysis result is combined with the deicing scheme generation unit for adjustment and selection, and the adjusted and selected deicing scheme is sent to the wind farm computer terminal, and the wind farm computer terminal controls the deicing of the blade according to the deicing scheme.

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