Fan blade deicing method, device and equipment based on blade material analysis

Abstract

The invention relates to the technical field of wind power generation, and discloses a fan blade deicing method, device and equipment based on blade material analysis, and the fan blade deicing method based on blade material analysis comprises the following steps: judging whether a fan blade triggers icing early warning or not according to the surface temperature and humidity of the fan blade; if the icing early warning is triggered, predicting the icing probability of the fan blade in the prediction time period according to the historical operation data; if the icing probability is greater than a preset probability, determining a loss tangent value according to the electromagnetic characteristic parameters and angular frequencies of the various blade materials; determining a target loss tangent value of the fan blade according to the loss tangent value of each blade material and the volume fraction of each blade material; and determining a target current frequency by using the target loss tangent value so as to deice the fan blade according to the target current frequency. According to the method, the material and the frequency of the blade fan are analyzed, the adaptation degree of the target current frequency and blade deicing is improved, and the deicing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method, device and equipment for deicing wind turbine blades based on blade material analysis. Background Art

[0002] As demand for clean energy continues to grow, wind power, as an important form of renewable energy, continues to expand its installed capacity. However, in cold, high-humidity climates, especially in high-latitude regions, high-altitude mountainous areas, and areas with large temperature differences between day and night in winter, ice is easily formed on the surfaces of wind turbine blades.

[0003] Icing on wind turbine blades can cause numerous serious problems. First, ice can significantly alter the blade's aerodynamic shape, increasing its air resistance and reducing its lift coefficient, significantly reducing the turbine's power generation efficiency. Second, ice can alter the blade's weight distribution, disrupting its dynamic balance and causing abnormal vibrations in components such as the blades and hub. Therefore, timely de-icing of wind turbine blades is crucial when ice forms.

[0004] The de-icing method for wind turbine blades in related technologies is to embed an electromagnetic induction coil on the blade surface, generate an alternating magnetic field by passing an alternating current, generate eddy currents inside the metal material of the blade, and use the thermal effect of the eddy currents to achieve heating and de-icing.

[0005] However, alternating currents of different frequencies generate different magnetic field strengths and penetration depths. If the frequency is not selected properly, the eddy currents may be concentrated mainly in the shallow layer of the blade surface, failing to effectively melt the deep ice layer, or energy may be wasted due to excessive penetration, reducing the de-icing efficiency. Summary of the Invention

[0006] In view of this, the present invention provides a method, device and equipment for deicing wind turbine blades based on blade material analysis to solve the problem of low deicing efficiency caused by the deicing method of wind turbine blades in the related art.

[0007] In a first aspect, the present invention provides a method for deicing fan blades based on blade material analysis, comprising: obtaining the surface temperature and surface humidity of the fan blades, and judging whether the fan blades trigger an icing warning based on the surface temperature and surface humidity; if the fan blades trigger an icing warning, predicting the icing probability of the fan blades within a predicted time period based on the historical operation data of the fan blades; if the icing probability of the fan blades within the predicted time period is greater than a preset probability, determining a loss tangent value for each blade material based on the electromagnetic characteristic parameters and angular frequencies of a plurality of blade materials; the loss tangent value for each blade material is an indicator for characterizing the energy lost by each blade material in an electromagnetic field; determining a target loss tangent value for the fan blades based on the loss tangent value of each blade material and the volume fraction of each blade material; the target loss tangent value for the fan blades is used to characterize the electromagnetic energy conversion efficiency of the fan blades; and determining a target current frequency using the target loss tangent value to de-ice the fan blades based on the target current frequency.

[0008] The present invention monitors the surface temperature and surface humidity of the fan blades in real time, and can timely capture the initial signs of possible freezing before freezing occurs, thereby establishing an freezing early warning mechanism. The present invention uses historical operating data to predict the probability of freezing, and can more accurately determine the possibility of freezing of fan blades in the future, providing a basis for power regulation and operation mode adjustment of fan blades. Different blade materials have different energy loss characteristics in electromagnetic fields. The present invention determines the loss tangent value from the material level by considering the electromagnetic characteristics of the blade material in a targeted manner, which can make the deicing energy more consistent with the properties of the blade material, avoid energy waste, and provide a basis for the precise control of subsequent electromagnetic deicing. The present invention combines the material volume fraction to comprehensively determine the target loss tangent value, which can comprehensively reflect the electromagnetic energy conversion characteristics of the blade as a whole, and more accurately grasp the energy response of the fan blade in the electromagnetic field environment. This invention uses the target loss tangent value to determine the target current frequency, which is then used to de-ice the wind turbine blades. This automatically matches the optimal current frequency, achieving precise control of electromagnetic de-icing. The target current frequency is adapted to the overall electromagnetic energy conversion efficiency of the blades, minimizing energy consumption while ensuring effective de-icing. This reduces additional thermal damage to the blade material and extends the blade's service life. Compared to related technologies, this invention improves the efficiency of electromagnetic de-icing, allowing electromagnetic energy to be more efficiently converted into the heat energy required for de-icing. This achieves effective de-icing, reduces energy loss and maintenance, and lowers wind power operating costs, thereby improving the power generation efficiency of wind turbines.

[0009] In an optional embodiment, whether the fan blade triggers an ice warning is determined based on the surface temperature and surface humidity, including: if the surface temperature is less than or equal to the preset temperature, and the surface humidity is greater than or equal to the preset humidity, then it is determined that the fan blade triggers an ice warning; if the surface temperature is less than or equal to the preset temperature, and the surface humidity is less than the preset humidity, then it is determined that the fan blade does not trigger an ice warning; if the surface temperature is greater than the preset temperature, and the surface humidity is greater than or equal to the preset humidity, then it is determined that the fan blade does not trigger an ice warning; if the surface temperature is greater than the preset temperature, and the surface humidity is less than the preset humidity, then it is determined that the fan blade does not trigger an ice warning.

[0010] In an optional embodiment, the icing probability of the fan blades within a prediction time period is predicted based on the historical operating data of the fan blades, including: inputting the historical operating data of the fan blades into a trained icing probability prediction model to obtain the icing probability of the fan blades within the prediction time period.

[0011] In an optional embodiment, the electromagnetic characteristic parameters of each blade material include the electrical conductivity of each blade material and the dielectric constant of each blade material; the loss tangent value of each blade material is determined based on the electromagnetic characteristic parameters and angular frequency of multiple blade materials, including: obtaining a first product result based on the product of the angular frequency of each blade material and the dielectric constant of each blade material; obtaining the loss tangent value of each blade material based on the quotient of the electrical conductivity of each blade material and the first product result.

[0012] In an optional embodiment, the target loss tangent value of the fan blade is determined based on the loss tangent value of each blade material and the volume fraction of each blade material, including: taking a weighted average of the loss tangent values ​​and corresponding volume fractions of multiple blade materials to obtain the target loss tangent value of the fan blade.

[0013] In an optional embodiment, a target loss tangent value is used to determine a target current frequency to de-ice the wind turbine blades according to the target current frequency, including: obtaining a product according to the target loss tangent value, the dielectric constant of the wind turbine blades, a preset value, and pi to obtain a second product result; obtaining a target current frequency according to the quotient of the conductivity of the wind turbine blades and the second product result, to de-ice the wind turbine blades by passing an alternating current according to the target current frequency into the electromagnetic induction coil embedded in the wind turbine blades.

[0014] In an optional embodiment, after determining the target current frequency using the target loss tangent value, the fan blade de-icing method based on blade material analysis also includes: constructing a fitness function based on the temperature rise performance evaluation index of the fan blade at the target current frequency and the energy consumption evaluation index of the fan blade at the target current frequency; and using the fitness function to evaluate the de-icing effect of the fan blade at the target current frequency.

[0015] The present invention constructs a fitness function that incorporates both heating performance and energy consumption assessment indicators, enabling quantitative evaluation of deicing effectiveness based on these two key dimensions. By using this fitness function to assess deicing effectiveness, the present invention can promptly identify the target current frequency during the deicing process, enabling timely adjustments and continuous optimization of the deicing process to enhance deicing effectiveness.

[0016] In a second aspect, the present invention provides a fan blade de-icing device based on blade material analysis, comprising: an icing judgment module for obtaining the surface temperature and surface humidity of the fan blade, and judging whether the fan blade triggers an icing warning based on the surface temperature and surface humidity; an icing probability prediction module for predicting the icing probability of the fan blade within a predicted time period based on the historical operation data of the fan blade when the fan blade triggers an icing warning; a loss determination module for determining the loss tangent value of each blade material based on the electromagnetic characteristic parameters and angular frequencies of multiple blade materials when the icing probability of the fan blade within the predicted time period is greater than a preset probability; the loss tangent value of each blade material is an indicator for characterizing the energy lost by each blade material in the electromagnetic field; a target loss determination module for determining a target loss tangent value of the fan blade based on the loss tangent value of each blade material and the volume fraction of each blade material; the target loss tangent value of the fan blade is used to characterize the electromagnetic energy conversion efficiency of the fan blade; and a blade de-icing unit for determining a target current frequency using the target loss tangent value, so as to de-ice the fan blade according to the target current frequency.

[0017] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the wind turbine blade de-icing method based on blade material analysis of the above-mentioned first aspect or any corresponding embodiment thereof.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the wind turbine blade deicing method based on blade material analysis of the above-mentioned first aspect or any corresponding embodiment thereof.

[0019] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the wind turbine blade deicing method based on blade material analysis according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 4 is a flow chart of a method for deicing wind turbine blades based on blade material analysis according to an embodiment of the present invention.

[0022] Figure 2 1 is a flow chart of another wind turbine blade deicing method based on blade material analysis according to an embodiment of the present invention.

[0023] Figure 3 4 is a structural block diagram of a fan blade deicing device based on blade material analysis according to an embodiment of the present invention.

[0024] Figure 4 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0026] In the related art, de-icing of fan blades is carried out by the following methods: the first is mechanical de-icing, which uses a vibrator installed inside the fan blade to break the ice layer, but long-term use can easily cause micro cracks on the surface of the fan blade; the second is hot air de-icing, which melts the ice layer by circulating hot air inside the fan blade, but the energy consumption is high and the heat conduction efficiency is limited; the third is chemical coating de-icing, which achieves de-icing by reducing the adhesion of the ice layer, but the coating has poor durability and requires regular maintenance; the fourth is electromagnetic pulse technology de-icing, which uses electromagnetic pulses to vibrate the internal structure of the fan blade to achieve de-icing, but the energy loss is large and the adaptability to composite materials is insufficient, and frequency adaptive adjustment cannot be achieved; the fifth is intelligent control de-icing, which is a de-icing control method based on sensor data, but does not integrate electromagnetic induction heating technology; the sixth is microwave heating / hot air de-icing, which uses microwave radiation to directly heat the ice layer on the blade surface, has strong penetration and does not require contact, but requires directional antennas and power regulation modules, and has high energy consumption. There is also a method of circulating hot air through the internal cavity of the fan blades, combining aerodynamic optimization design to improve heat conduction efficiency, and using regional temperature control technology, but it relies on external heat sources and is less economical.

[0027] An embodiment of the present invention provides a fan blade deicing method based on blade material analysis, which analyzes the material and frequency of the fan blade to improve the compatibility of the target current frequency with the blade deicing, thereby improving the deicing efficiency.

[0028] According to an embodiment of the present invention, an embodiment of a method for deicing wind turbine blades based on blade material analysis is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] In this embodiment, a method for deicing fan blades based on blade material analysis is provided, which can be used for controlling a computer device of a fan blade. Figure 1 FIG. 1 is a flow chart of a method for deicing a wind turbine blade based on blade material analysis according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0030] Step S101: obtaining the surface temperature and surface humidity of the fan blades, and determining whether the fan blades trigger an icing warning based on the surface temperature and surface humidity.

[0031] Among them, a temperature sensor is installed every two meters on the leading edge surface and inside of the fan blade to collect the surface temperature of the fan blade in real time. The temperature sensor can be a platinum resistance thermometer (accuracy ±5°C); a humidity sensor is integrated at the root of the fan blade to collect the surface humidity of the fan blade in real time. The humidity sensor can be a capacitive humidity sensor (range 0-100% relative humidity).

[0032] In some optional embodiments, if the surface temperature is less than or equal to the preset temperature, and the surface humidity is greater than or equal to the preset humidity, it is judged that the fan blades trigger an ice warning; if the surface temperature is less than or equal to the preset temperature, and the surface humidity is less than the preset humidity, it is judged that the fan blades do not trigger an ice warning; if the surface temperature is greater than the preset temperature, and the surface humidity is greater than or equal to the preset humidity, it is judged that the fan blades do not trigger an ice warning; if the surface temperature is greater than the preset temperature, and the surface humidity is less than the preset humidity, it is judged that the fan blades do not trigger an ice warning.

[0033] The preset temperature may be -5°C (degrees Celsius), and the preset humidity may be 80%.

[0034] Step S102: If the wind turbine blade triggers an icing warning, the icing probability of the wind turbine blade within the predicted time period is predicted based on the historical operating data of the wind turbine blade.

[0035] The historical operation data of the wind turbine blades is time-series data related to the operation of the wind turbine blades and the environment in which they are located. For example, the historical operation data includes data such as temperature, humidity, wind speed, and blade rotation speed.

[0036] In some optional embodiments, the icing probability of the fan blades within a prediction time period is predicted based on the historical operating data of the fan blades, including: inputting the historical operating data of the fan blades into a trained icing probability prediction model to obtain the icing probability of the fan blades within the prediction time period.

[0037] Among them, the icing probability prediction model is trained through the historical operation sample data of the wind turbine blades, and a model of the mapping relationship between the historical operation sample data and the icing probability is established. The icing probability prediction model can use a variety of algorithm architectures, such as decision trees, random forests, support vector machines in machine learning, or neural networks in deep learning.

[0038] In step S103, if the probability of icing of the wind turbine blades within the predicted time period is greater than a preset probability, a loss tangent value of each blade material is determined based on the electromagnetic characteristic parameters and angular frequencies of the various blade materials; the loss tangent value of each blade material is an indicator used to characterize the energy lost by each blade material in the electromagnetic field.

[0039] Among them, the electromagnetic characteristic parameters of each blade material include the electrical conductivity of each blade material and the dielectric constant of each blade material. Among them, the unit of electrical conductivity is Siemens / meter (S / m), which can be obtained through experimental measurement or provided by the material supplier, and the unit of dielectric constant is Farad / meter (F / m), which can be obtained through experimental measurement or provided by the material supplier; the angular frequency can be obtained according to the speed sensor, or by obtaining the frequency of the blade material.

[0040] In some optional embodiments, the electromagnetic characteristic parameters of each blade material include the electrical conductivity of each blade material and the dielectric constant of each blade material; determining the loss tangent value of each blade material based on the electromagnetic characteristic parameters and angular frequency of multiple blade materials includes: obtaining a first product result based on the product of the angular frequency of each blade material and the dielectric constant of each blade material; obtaining the loss tangent value of each blade material based on the quotient of the electrical conductivity of each blade material and the first product result.

[0041] Among them, loss tangent is an indicator of the energy loss of a material in an electromagnetic field. The formula for determining the loss tangent value of each blade material is:

[0042]

[0043] Among them, tanδ i is the loss tangent value of the i-th blade material, σ i is the conductivity of the i-th blade material, ω i is the angular frequency of the i-th blade material, ω i =2πf i , f i is the frequency, ε i is the dielectric constant of the i-th blade material.

[0044] Step S104 , determining a target tangent loss value of the fan blade according to the tangent loss value of each blade material and the volume fraction of each blade material; the target tangent loss value of the fan blade is used to characterize the electromagnetic energy conversion efficiency of the fan blade.

[0045] In some optional embodiments, the target loss tangent value of the fan blade is determined based on the loss tangent value of each blade material and the volume fraction of each blade material, including: taking a weighted average of the loss tangent values ​​and corresponding volume fractions of multiple blade materials to obtain the target loss tangent value of the fan blade.

[0046] The formula for determining the target loss tangent value is:

[0047]

[0048] Among them, tanδ targetis the target loss tangent value of the fan blade, n is the total number of blade material types, V i is the volume fraction of the i-th blade material, tanδ i is the loss tangent value of the i-th blade material.

[0049] The sum of the volume fractions of n types of blade materials is 1.

[0050] Step S105 : determining a target current frequency using the target loss tangent value, so as to de-ice the wind turbine blades according to the target current frequency.

[0051] In some optional embodiments, a target loss tangent value is used to determine a target current frequency to de-ice the wind turbine blades according to the target current frequency, including: multiplying the target loss tangent value, the dielectric constant of the wind turbine blades, a preset value, and pi to obtain a second product result; obtaining the target current frequency based on the quotient of the conductivity of the wind turbine blades and the second product result, to de-ice the wind turbine blades by passing an alternating current through the electromagnetic induction coil embedded in the wind turbine blades according to the target current frequency.

[0052] Among them, the preset value can be 2.

[0053] Exemplarily, the formula for determining the target current frequency is:

[0054]

[0055] Where f is the target current frequency, σ is the conductivity of the fan blade, ε is the dielectric constant of the fan blade, and tanδ target is the target loss tangent value of the fan blade.

[0056] In some optional embodiments, the electromagnetic induction coils of the fan blades are distributed in a serpentine shape along the leading edge and middle of the fan blades, with a spacing of 20 cm. Six groups of electromagnetic induction coils can be embedded in the fan blades, and each group of electromagnetic induction coils covers a length of 1.5 m of the fan blades. The electromagnetic induction coils are bonded to the internal structure of the fan blades through a carbon fiber substrate (thickness 2 mm), and the surface of the substrate is grooved to adapt to the shape of the coils. The material of the electromagnetic induction coils is copper enameled wire (diameter 0.5 mm), and the outer layer is wrapped with a high-temperature resistant insulation layer (polyimide material). The high-frequency power supply in the fan blades is connected in series with the electromagnetic induction coils through a high-voltage cable, and every two groups of electromagnetic induction coils share an independent power supply channel. The input of the high-frequency power supply is 48 V DC (provided by the fan power supply system), and the output is an alternating current according to the target current frequency (for example, the frequency is adjustable from 10 to 50 kHz and the power is continuously adjustable from 0.5 to 3 kW).

[0057] For example, if the target current frequency is 30kHz (kilohertz), the high-frequency power supply is controlled to output an alternating current of 30kHz and 2kW (kilowatts). The electromagnetic induction coil generates eddy currents, causing the surface temperature of the wind turbine blades to rise to 5°C within 5 minutes and maintain this temperature for 10 minutes. The temperature change rate is monitored in real time. If the preset temperature rise curve is not reached, the frequency is automatically increased to 50kHz or the power to 3kW. After the ice is removed, the system switches to low-power mode (0.5kW to maintain a temperature of 3°C).

[0058] According to the fan blade deicing method based on blade material analysis provided in this embodiment, the deicing effect obtained is: deicing efficiency: clearing a 2mm thick ice layer within 5 minutes at -10°C; energy consumption: energy consumption of a single deicing cycle is ≤1.5kWh, which is 45% lower than that of the traditional hot air method; compatibility: suitable for carbon fiber and fiberglass blades, without material fatigue risk.

[0059] The fan blade deicing method based on blade material analysis provided in this embodiment can timely capture the initial signs of possible icing before icing occurs by real-time monitoring of the surface temperature and surface humidity of the fan blades, and establish an icing early warning mechanism. The embodiment of the present invention uses historical operating data to predict the probability of icing, which can more accurately determine the possibility of icing on the fan blades in the future and provide a basis for power regulation and operation mode adjustment of the fan blades. Different blade materials have different energy loss characteristics in the electromagnetic field. The embodiment of the present invention determines the loss tangent value from the material level by considering the electromagnetic characteristics of the blade material in a targeted manner, which can make the deicing energy more consistent with the properties of the blade material, avoid energy waste, and provide a basis for the precise control of subsequent electromagnetic deicing. The embodiment of the present invention combines the material volume fraction to comprehensively determine the target loss tangent value, which can comprehensively reflect the electromagnetic energy conversion characteristics of the blade as a whole and more accurately grasp the energy response of the fan blade in the electromagnetic field environment. This embodiment of the present invention utilizes the target loss tangent value to determine the target current frequency, which is then used to de-ice the wind turbine blades. This automatically matches the optimal current frequency, achieving precise control of electromagnetic de-icing. The target current frequency is adapted to the overall electromagnetic energy conversion efficiency of the blades, minimizing energy consumption while ensuring effective de-icing, reducing additional thermal damage to the blade material, and extending the service life of the blades. Compared to related technologies, this embodiment of the present invention improves the efficiency of electromagnetic de-icing, allowing electromagnetic energy to be more efficiently converted into the heat energy required for de-icing. This achieves effective de-icing, reduces energy loss and maintenance, and lowers wind power operating costs, thereby improving the power generation efficiency of wind turbines.

[0060] In this embodiment, a method for deicing fan blades based on blade material analysis is provided, which can be used for controlling a computer device of a fan blade. Figure 2 FIG. 1 is a flow chart of a method for deicing a wind turbine blade based on blade material analysis according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0061] Step S201: Obtain the surface temperature and surface humidity of the fan blades, and determine whether the fan blades trigger an icing warning based on the surface temperature and surface humidity. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0062] Step S202: If the fan blade triggers an icing warning, the probability of icing on the fan blade within the predicted time period is predicted based on the historical operating data of the fan blade. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0063] Step S203: If the probability of icing of the wind turbine blades within the predicted time period is greater than a preset probability, the loss tangent value of each blade material is determined based on the electromagnetic characteristic parameters and angular frequencies of the various blade materials. The loss tangent value of each blade material is an indicator used to characterize the energy lost by each blade material in the electromagnetic field. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0064] Step S204: Determine the target tangent loss value of the fan blade based on the tangent loss value of each blade material and the volume fraction of each blade material; the target tangent loss value of the fan blade is used to characterize the electromagnetic energy conversion efficiency of the fan blade. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0065] Step S205: Target current frequency is determined using the target loss tangent value, so as to de-ice the wind turbine blades according to the target current frequency. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0066] Step S206 , constructing a fitness function according to the temperature rise performance evaluation index of the fan blades at the target current frequency and the energy consumption evaluation index of the fan blades at the target current frequency.

[0067] For example, the fitness function is expressed as:

[0068]

[0069] Where F(f) is the fitness function of the fan blade under the target current frequency f, w1 is the first weight coefficient, w1 can be 0.5, T rise (f) is the temperature rise rate of the fan blade surface when the target current frequency is f, T rise_maxis the maximum temperature rise rate under all frequencies, w2 is the second weight coefficient, w2 can be taken as 0.5, P(f) is the energy consumption of the fan blade when the target current frequency is f, P max is the maximum energy consumption of the fan blades at all frequencies.

[0070] Step S207 : evaluating the de-icing effect of the wind turbine blades at the target current frequency using a fitness function.

[0071] Among them, the larger the fitness value obtained according to the fitness function, the better the deicing effect is, which can effectively increase the temperature of the fan blades to de-ice while better controlling energy consumption.

[0072] This embodiment provides a wind turbine blade deicing method based on blade material analysis. By constructing a fitness function that includes both heating performance and energy consumption evaluation indicators, this method can quantitatively evaluate the deicing effectiveness based on two key dimensions: heating capacity and energy consumption. By using this fitness function to evaluate the deicing effectiveness, this embodiment of the present invention can promptly identify the target current frequency during the deicing process, enabling timely adjustments and continuous optimization of the deicing process to enhance the deicing effect.

[0073] This embodiment also provides a wind turbine blade deicing device based on blade material analysis. This device is used to implement the above-mentioned embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0074] This embodiment provides a fan blade deicing device based on blade material analysis, such as Figure 3 Shown, including:

[0075] The icing judgment module 301 is used to obtain the surface temperature and surface humidity of the fan blades, and judge whether the fan blades trigger an icing warning based on the surface temperature and surface humidity.

[0076] The icing probability prediction module 302 is used to predict the icing probability of the wind blades within a predicted time period based on the historical operating data of the wind blades when an icing warning is triggered by the wind blades.

[0077] The loss determination module 303 is used to determine the loss tangent value of each blade material based on the electromagnetic characteristic parameters and angular frequency of the multiple blade materials, based on the probability of icing of the wind turbine blades within the predicted time period being greater than the preset probability; the loss tangent value of each blade material is an indicator used to characterize the energy lost by each blade material in the electromagnetic field.

[0078] The target loss determination module 304 is used to determine the target loss tangent value of the fan blade according to the loss tangent value of each blade material and the volume fraction of each blade material; the target loss tangent value of the fan blade is used to characterize the electromagnetic energy conversion efficiency of the fan blade.

[0079] The blade deicing unit 305 is configured to determine a target current frequency using a target loss tangent value, so as to de-ice the wind turbine blades according to the target current frequency.

[0080] In some optional embodiments, the icing determination module 301 includes:

[0081] The icing warning unit is used to determine whether the fan blades trigger the icing warning based on the surface temperature being less than or equal to the preset temperature and the surface humidity being greater than or equal to the preset humidity; to determine whether the fan blades do not trigger the icing warning based on the surface temperature being less than or equal to the preset temperature and the surface humidity being less than the preset humidity; to determine whether the fan blades do not trigger the icing warning based on the surface temperature being greater than the preset temperature and the surface humidity being greater than or equal to the preset humidity; and to determine whether the fan blades do not trigger the icing warning based on the surface temperature being greater than the preset temperature and the surface humidity being less than the preset humidity.

[0082] In some optional implementations, the icing probability prediction module 302 includes:

[0083] The icing probability prediction unit is used to input the historical operating data of the fan blades into the trained icing probability prediction model to obtain the icing probability of the fan blades within the prediction time period.

[0084] In some optional implementations, the loss determination module 303 includes:

[0085] The first product unit is configured to obtain a first product result according to the product of the angular frequency of each blade material and the dielectric constant of each blade material.

[0086] The loss determination unit is configured to obtain a loss tangent value of each blade material according to a quotient of the electrical conductivity of each blade material and a result of the first multiplication.

[0087] In some optional implementations, the target loss determination module 304 includes:

[0088] The weighted averaging unit is used to perform weighted averaging on the loss tangent values ​​and corresponding volume fractions of various blade materials to obtain a target loss tangent value of the fan blade.

[0089] In some optional embodiments, the blade de-icing unit 305 includes:

[0090] The second product unit is used to obtain a product according to the target loss tangent value, the dielectric constant of the wind turbine blade, the preset value and the pi to obtain a second product result.

[0091] The deicing unit is used to obtain a target current frequency according to the quotient of the conductivity of the fan blade and the second product result, so as to pass an alternating current into the electromagnetic induction coil embedded in the fan blade according to the target current frequency for deicing.

[0092] In some optional embodiments, a wind turbine blade deicing device based on blade material analysis includes:

[0093] The function construction module is used to construct a fitness function according to a temperature rise performance evaluation index of the fan blades under a target current frequency and an energy consumption evaluation index of the fan blades under a target current frequency.

[0094] De-icing evaluation module, which is used to evaluate the de-icing effect of wind turbine blades at the target current frequency using the fitness function

[0095] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0096] The fan blade de-icing device based on blade material analysis in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0097] The embodiment of the present invention also provides a computer device having the above Figure 3 The fan blade deicing device shown is based on blade material analysis.

[0098] See also Figure 4 , Figure 4 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 4As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0099] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0100] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0101] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0102] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0103] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0104] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0105] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0106] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A wind turbine blade deicing method based on blade material analysis, characterized in that: The method comprises: Obtaining the surface temperature and surface humidity of the fan blade, and determining whether the fan blade triggers an icing warning based on the surface temperature and the surface humidity; If the fan blade triggers an icing warning, predicting the icing probability of the fan blade within a predicted time period based on the historical operating data of the fan blade; If the probability of icing of the wind turbine blades within the predicted time period is greater than a preset probability, determining a loss tangent value of each blade material based on electromagnetic characteristic parameters and angular frequencies of the multiple blade materials; the loss tangent value of each blade material is an indicator for characterizing the energy lost by each blade material in the electromagnetic field; Determining a target tangent loss value of the fan blade according to the tangent loss value of each blade material and the volume fraction of each blade material; the target tangent loss value of the fan blade is used to characterize the electromagnetic energy conversion efficiency of the fan blade; The target loss tangent value is used to determine a target current frequency, so as to de-ice the wind turbine blades according to the target current frequency.

2. The method according to claim 1, characterized in that The determining, based on the surface temperature and the surface humidity, whether the fan blade triggers an icing warning includes: If the surface temperature is less than or equal to a preset temperature, and the surface humidity is greater than or equal to a preset humidity, it is determined that the fan blade triggers an icing warning; If the surface temperature is less than or equal to the preset temperature, and the surface humidity is less than the preset humidity, it is determined that the fan blade does not trigger an icing warning; If the surface temperature is greater than the preset temperature and the surface humidity is greater than or equal to the preset humidity, it is determined that the fan blade does not trigger an icing warning; If the surface temperature is greater than the preset temperature and the surface humidity is less than the preset humidity, it is determined that the fan blade does not trigger an icing warning.

3. The method according to claim 1 or 2, characterized in that The predicting, based on the historical operating data of the fan blades, the probability of icing of the fan blades within a predicted time period includes: The historical operating data of the wind turbine blade is input into a trained icing probability prediction model to obtain the icing probability of the wind turbine blade within the prediction time period.

4. The method according to claim 1 or 2, characterized in that The electromagnetic characteristic parameters of each blade material include the electrical conductivity of each blade material and the dielectric constant of each blade material; and determining the loss tangent value of each blade material based on the electromagnetic characteristic parameters and angular frequency of the plurality of blade materials includes: Obtaining a first product result according to the product of the angular frequency of each blade material and the dielectric constant of each blade material; The loss tangent value of each of the blade materials is obtained according to the quotient of the electrical conductivity of each of the blade materials and the first product result.

5. The method according to claim 1 or 2, characterized in that Determining the target tangent loss value of the fan blade according to the tangent loss value of each blade material and the volume fraction of each blade material includes: The loss tangent values ​​and corresponding volume fractions of the plurality of blade materials are weighted averaged to obtain a target loss tangent value of the fan blade.

6. The method according to claim 1 or 2, characterized in that The step of determining a target current frequency by using the target loss tangent value, so as to de-ice the wind turbine blades according to the target current frequency, includes: A second product result is obtained by multiplying the target loss tangent value, the dielectric constant of the fan blade, a preset value, and pi; The target current frequency is obtained according to the quotient of the conductivity of the wind turbine blade and the second product result, so as to pass an alternating current into the electromagnetic induction coil embedded in the wind turbine blade according to the target current frequency for de-icing.

7. The method according to claim 1 or 2, characterized in that After determining the target current frequency using the target loss tangent value, the method further includes: Constructing a fitness function according to a temperature rise performance evaluation index of the fan blade at the target current frequency and an energy consumption evaluation index of the fan blade at the target current frequency; The fitness function is used to evaluate the deicing effect of the wind turbine blades at the target current frequency.

8. A fan blade deicing device based on blade material analysis, characterized in that: The device comprises: an icing judgment module, configured to obtain the surface temperature and surface humidity of the fan blades, and determine whether the fan blades trigger an icing warning based on the surface temperature and the surface humidity; an icing probability prediction module, configured to predict the icing probability of the fan blades within a predicted time period based on historical operating data of the fan blades when an icing warning is triggered by the fan blades; a loss determination module, configured to determine, based on the probability of icing of the wind turbine blades within the predicted time period being greater than a preset probability, a loss tangent value of each blade material according to electromagnetic characteristic parameters and angular frequencies of the plurality of blade materials; the loss tangent value of each blade material being an indicator for characterizing the energy lost by each blade material in the electromagnetic field; a target loss determination module, configured to determine a target loss tangent value of the fan blade according to the loss tangent value of each blade material and the volume fraction of each blade material; the target loss tangent value of the fan blade is used to characterize the electromagnetic energy conversion efficiency of the fan blade; The blade deicing unit is used to determine a target current frequency by using the target loss tangent value, so as to de-ice the fan blades according to the target current frequency.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the wind turbine blade deicing method based on blade material analysis according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the wind turbine blade deicing method based on blade material analysis according to any one of claims 1 to 7.

11. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the wind turbine blade deicing method based on blade material analysis according to any one of claims 1 to 7.

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