A method for monitoring wind turbine icing based on atmospheric parameters in freezing environments

Abstract

The present invention discloses a method for monitoring wind turbine icing based on atmospheric parameters of a freezing environment, comprising: step 1, determining the atmospheric parameters to be measured according to monitoring requirements; step 2, measuring the real-time atmospheric parameters; step 3, converting the three-dimensional model of wind turbine blades into a two-dimensional model; step 4, constructing a wind turbine blade icing quality monitoring function based on the atmospheric parameters; step 5, reconstructing the three-dimensional ice shape of the synthetic wind turbine blades; step 6, updating the wind turbine blade icing quality function according to the real-time parameters; the method solves the technical problems that the prior art lacks an online monitoring method for wind turbine blade icing, and can only actively shut down the wind turbine in advance in winter, which brings an operating burden to the power grid dispatching and increases economic losses.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind turbine blade icing monitoring, and in particular relates to a wind turbine icing monitoring method based on atmospheric parameters of a freezing environment. Background Art

[0002] In recent years, clean energy, including wind power generation, has developed rapidly. However, in high-latitude and cold regions and some low-latitude inland mountains, wind turbines are susceptible to icing. The icing problem of wind turbines has hindered the development of wind farms in these areas, damaged their aerodynamic performance, and even shut down. In addition, icing of wind turbines will increase the load on the blades and tower structure, resulting in uneven mass distribution of each blade, increased amplitude and even resonance between blades, exceeding the design fatigue load. Operation in a low-temperature environment will affect the low-temperature fluidity of the lubricant, resulting in increased mechanical wear, affecting the life of the gearbox and the power generation of the wind farm.

[0003] Wind turbine icing is a complex physical process involving multiple disciplines such as fluid mechanics, aerodynamics and thermodynamics. Currently, there is a lack of online methods for icing wind turbine blades, and wind turbines can only be shut down proactively in advance in winter, which brings operational burdens to power grid dispatching and increases economic losses. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for monitoring wind turbine icing based on atmospheric parameters of a freezing environment, so as to solve the technical problems that the prior art lacks an online monitoring method for wind turbine blade icing, and the wind turbine can only be shut down proactively in advance in winter, which brings operational burden to power grid dispatching and increases economic losses.

[0005] The technical solution of the present invention is:

[0006] A method for monitoring wind turbine icing based on atmospheric parameters of a freezing environment, comprising:

[0007] Step 1: Determine the atmospheric parameters to be measured according to monitoring requirements;

[0008] Step 2, measuring real-time atmospheric parameters;

[0009] Step 3: Convert the three-dimensional model of the wind turbine blade into a two-dimensional model;

[0010] Step 4: construct a wind turbine blade ice quality monitoring function based on atmospheric parameters;

[0011] Step 5, reconstructing the three-dimensional ice shape of the synthetic wind turbine blade;

[0012] Step 6: Update the wind turbine blade ice mass function according to the real-time parameters.

[0013] The atmospheric parameters to be measured include: ambient temperature, ambient wind speed, liquid water content and median volume diameter of water droplets.

[0014] The method for measuring real-time atmospheric parameters is to complete the measurement of ambient temperature T(t), ambient wind speed v(t), liquid water content w(t) and median volume diameter a(t) of water droplets through a multi-cylinder ice volume analyzer, a six-element meteorological instrument, a cloud radar and a microwave radiometer.

[0015] The range of ambient temperature T is set to -15 to 5°C, with a precision of 0.5°C; the range of wind speed v is set to 0 to 20 m / s, with a precision of 0.5 m / s; the range of liquid water content w is set to 0.2 to 2.6 g / m 3 , value accuracy 0.2g / m 3 ; The median volume diameter a of the water droplet ranges from 10 to 100 μm, with an accuracy of 5 μm; the target wind turbine speed n is set in the range of 5 to 30 r / min, with an accuracy of 0.5 r / min; the distance s from the water droplet collision point to the target wind turbine blade stagnation point is set in the range of -0.05 to 0.30 m, with an accuracy of 0.01 m.

[0016] The method for converting the three-dimensional model of the wind turbine blade into a two-dimensional model in step 3 is: "slicing" the three-dimensional model of the wind turbine along the blade span direction, dividing the blade into countless micro-element control body sections, and performing ice shape simulation on each airfoil section respectively; the number of micro-element control body sections is greater than 10,000.

[0017] The wind turbine blade ice quality monitoring function needs to calculate the stagnation point distance s(t) between the water droplet collision point and the target wind turbine blade and the wind turbine speed n(t) within any time step, which are obtained from the wind turbine blade airfoil parameters and the real-time speed measurement device respectively.

[0018] The method for constructing a wind turbine blade ice quality monitoring function based on atmospheric parameters includes:

[0019] A function of the water drop collision coefficient α1(t) is calculated based on the ambient wind speed v(t), the median volume diameter of the water drop a(t), the distance s(t) between the water drop collision point and the target wind turbine blade stagnation point, and the wind turbine rotation speed n;

[0020] α1(t)=f(v(t),a(t),s(t),n)

[0021] The function of the freezing coefficient α3(t) is calculated based on the ambient temperature T(t), liquid water content w(t), wind speed v(t) and water drop collision coefficient α1(t);

[0022] α3(t)=f(T(t),w(t),v(t),α1(t))

[0023] The calculation function of the ice mass growth of the micro-element control volume on the surface of the wind turbine blade is expressed as:

[0024] Δm(t)=f(v(t),α1(t),α2(t),α3(t),w(t))

[0025] The real-time ice weight m(t+Δt) of the wind turbine blade micro-element control body is calculated:

[0026] m(t+Δt)=m(t)+Δm(t).

[0027] The method for reconstructing the three-dimensional ice shape of the synthetic wind turbine blade is: calculate the ice mass of all cross-sections of the wind turbine blade, and finally reconstruct the two-dimensional ice shape simulation results obtained from all slices to obtain the three-dimensional ice shape result of the entire wind turbine blade.

[0028] Beneficial effects of the present invention:

[0029] The invention further studies the freezing characteristics of water droplets on the surface of wind turbine blades by studying the external airflow field of wind turbine blades, the trajectory of water droplets and their collision process, and constructs a numerical calculation model for ice coverage of wind turbine blades.

[0030] Establishing a wind turbine blade icing development model and a blade three-dimensional icing reconstruction method can realize real-time monitoring of wind turbine blade icing quality and ice shape, which is of great significance for providing a decision-making basis for the operation plan of wind turbines during the icing period and improving the stability of power grid dispatching.

[0031] The invention solves the technical problems that the existing technology lacks an online monitoring method for ice covering of wind turbine blades, and the wind turbine can only be shut down proactively in advance in winter, which brings operational burden to the grid dispatching and increases economic losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of the present invention;

[0033] Figure 2 Flowchart for calculation of ice coverage on wind turbine blades. DETAILED DESCRIPTION

[0034] The present invention provides a method for monitoring wind turbine icing based on atmospheric parameters of a freezing environment (see Figure 1 ), comprising the following steps:

[0035] S1. Determine the atmospheric parameters to be measured according to monitoring requirements;

[0036] Specifically: Based on the physical model of wind turbine blade icing development, the present invention first calculates the water drop collision coefficient and the freezing coefficient, and then constructs a wind turbine blade icing quality monitoring model; and the calculation of the water drop collision coefficient and the freezing coefficient requires at least four strongly correlated environmental parameters, including ambient temperature, wind speed and direction, liquid water content, median volume diameter of water droplets, air dynamic viscosity coefficient, etc. Since some parameters have relatively little effect on the monitoring results, in order to speed up the calculation speed, certain simplifications can be made in some cases, such as the air dynamic viscosity coefficient can be approximated as a constant value without considering the qualitative temperature change of the boundary layer caused by temperature rise.

[0037] In addition, constructing the icing function also requires calculating the stagnation point distance s(t) between the water drop collision point and the target wind turbine blade and the wind turbine speed n(t) within any time step. When converting the wind turbine from a three-dimensional model to a two-dimensional model, uniform "slicing" is started from the stagnation point. Each "slice" corresponds to a coordinate with the stagnation point origin to represent the stagnation point distance. Each coordinate is recorded to obtain the stagnation point distance s(t) between the water drop collision point and the target wind turbine blade. The wind turbine speed n(t) can be obtained through a real-time speed measurement device.

[0038] Furthermore, different atmospheric parameters required for ice monitoring can be measured by combining different measuring equipment. Furthermore, the measuring equipment is not limited to: six-element meteorological instrument, cloud radar, microwave radiometer, etc. Take the six-element meteorological instrument as an example. The six-element meteorological instrument is installed and fixed on the wind tower or the top of the target wind turbine in the wind farm. The wind turbine itself is powered, the wind tower is equipped with a power supply, or the power supply is supplied at the front end of the grid connection.

[0039] S2. Measure real-time atmospheric parameters;

[0040] Specifically: the actual measured ambient temperature T(t), ambient wind speed v(t), liquid water content w(t), and water droplet median volume diameter a(t) are collected respectively.

[0041] A statistical survey of meteorological parameters in most wind power generation areas was conducted. Combined with the actual situation of wind turbine icing, in order to reduce calculation redundancy, the value ranges of ambient temperature T, ambient wind speed v, liquid water content w, and water droplet median diameter a were set as follows: the value range of ambient temperature T was set at -15 to 5 °C, with a value accuracy of 0.5 °C; the value range of wind speed v was set at 0 to 20 m / s, with a value accuracy of 0.5 m / s; the range of liquid water content w was set at 0.2 to 2.6 g / m 3 , value accuracy 0.2g / m 3; The range of the median volume diameter a of the water droplet is 10-100 μm, with a precision of 5 μm. The range of the target wind turbine speed n is set to 5-30 r / min, with a precision of 0.5 r / min; the range of the distance s between the water droplet collision point and the target wind turbine blade stagnation point is set to -0.05-0.30 m, with a precision of 0.01 m.

[0042] S3. Converting the three-dimensional model of the wind turbine blade into a two-dimensional model;

[0043] Specifically: The present invention "slices" the three-dimensional model of the wind turbine along the blade span direction, and divides the blade into more than 10,000 micro-element control body sections on average according to the calculation accuracy and blade size. The higher the accuracy requirement, the more sections there are, and ice shape simulation is performed on each airfoil section separately.

[0044] S4. Construct a wind turbine blade ice quality monitoring function based on atmospheric parameters;

[0045] The function of the water droplet collision coefficient α1(t) is calculated based on the ambient wind speed v(t), the median volume diameter of the water droplet a(t), the stagnation point distance s(t) between the water droplet collision point and the target wind turbine blade, and the wind turbine rotation speed n.

[0046] α1(t)=f(v(t),a(t),s(t),n)

[0047] The function of the freezing coefficient α3(t) is calculated based on the ambient temperature T(t), liquid water content w(t), wind speed v(t) and water droplet collision coefficient α1(t).

[0048] α3(t)=f(T(t),w(t),v(t),α1(t))

[0049] The calculation function of the ice mass growth of the micro-element control volume on the surface of the wind turbine blade can be expressed as:

[0050] Δm(t)=f(v(t),α1(t),α2(t),α3(t),w(t))

[0051] In the process of supercooled water droplets colliding with the surface of wind turbine blades, the influence of water droplet splashing is ignored, so the capture coefficient α2≈1.

[0052] The real-time ice weight m(t+Δt) of the wind turbine blade micro-element control body is further calculated:

[0053] m(t+Δt)=m(t)+Δm(t)

[0054] S5. Reconstructing the three-dimensional ice shape of wind turbine blades;

[0055] Specifically: the ice-covered mass of all cross-sections of the wind turbine blade is calculated, and finally the two-dimensional ice shape simulation results obtained from all slices are reconstructed to obtain the three-dimensional ice-covered result of the entire wind turbine blade.

[0056] S6. Update the ice mass function of the wind turbine blades according to the real-time parameters.

[0057] Specifically: based on the above data parameters measured in real time, the ambient temperature T(t), ambient wind speed v(t), liquid water content w(t), median volume diameter of water droplets a(t), and according to the above process, the ice coverage quality of wind turbine blades is monitored in real time.

Claims

1. A method for monitoring wind turbine icing based on atmospheric parameters in a freezing environment. Features: It includes: Step 1: Determine the atmospheric parameters to be measured according to monitoring requirements; Step 2, measuring real-time atmospheric parameters; Step 3: Convert the three-dimensional model of the wind turbine blade into a two-dimensional model; Step 4: construct a wind turbine blade ice quality monitoring function based on atmospheric parameters; The method for constructing a wind turbine blade ice quality monitoring function based on atmospheric parameters includes: The water droplet collision coefficient α is calculated based on the ambient wind speed v(t), the median volume diameter of the water droplet a(t), the distance s(t) between the water droplet collision point and the target wind turbine blade stagnation point, and the wind turbine speed n. 1 (t); α 1 (t)=f(v(t),a(t),s(t),n) According to the ambient temperature T(t), liquid water content w(t), ambient wind speed v(t) and water droplet collision coefficient α 1 (t) is a function of the freezing coefficient α 3 (t); α 3 (t)=f(T(t),w(t),v(t),α 1 (t)) The calculation function of the ice mass growth of the micro-element control volume on the surface of the wind turbine blade is expressed as: Δm(t)=f(v(t),α 1 (t),α 2 (t),α 3 (t),w(t)) The real-time ice weight m(t+Δt) of the wind turbine blade micro-element control body is calculated: m(t+Δt)=m(t)+Δm(t); α 2 (t) is the capture coefficient, α 2 ≈1; Step 5, reconstructing the three-dimensional ice shape of the synthetic wind turbine blade; Step 6: Update the wind turbine blade ice mass function according to the real-time parameters.

2. A method for monitoring wind turbine icing based on atmospheric parameters in a freezing environment according to claim 1, Features: The atmospheric parameters to be measured include: ambient temperature, ambient wind speed, liquid water content and median volume diameter of water droplets.

3. The method for monitoring wind turbine icing based on atmospheric parameters in a freezing environment according to claim 1, Features: The method for measuring real-time atmospheric parameters is to complete the measurement of ambient temperature T(t), ambient wind speed v(t), liquid water content w(t) and median volume diameter a(t) of water droplets through a multi-cylinder ice volume analyzer, a six-element meteorological instrument, a cloud radar and a microwave radiometer.

4. A method for monitoring wind turbine icing based on atmospheric parameters in a freezing environment according to claim 3, Features: The range of ambient temperature T is set to -15 to 5°C, with a precision of 0.5°C; the range of wind speed v is set to 0 to 20 m / s, with a precision of 0.5 m / s; the range of liquid water content w is set to 0.2 to 2.6 g / m 3 , value accuracy 0.2g / m 3 ; The median volume diameter a of the water droplet ranges from 10 to 100 μm, with an accuracy of 5 μm; the target wind turbine speed n is set in the range of 5 to 30 r / min, with an accuracy of 0.5 r / min; the distance s from the water droplet collision point to the target wind turbine blade stagnation point is set in the range of -0.05 to 0.30 m, with an accuracy of 0.01 m.

5. The method for monitoring wind turbine icing based on atmospheric parameters in a freezing environment according to claim 1, Features: The method for converting the three-dimensional model of the wind turbine blade into a two-dimensional model in step 3 is: "slicing" the three-dimensional model of the wind turbine along the blade span direction, dividing the blade into countless micro-element control body sections, and performing ice shape simulation on each airfoil section; the number of micro-element control body sections is greater than 10,000.

6. The method for monitoring wind turbine icing based on atmospheric parameters in a freezing environment according to claim 1, Features: The wind turbine blade ice quality monitoring function needs to calculate the stagnation point distance s(t) between the water droplet collision point and the target wind turbine blade and the wind turbine speed n(t) within any time step, which are obtained from the wind turbine blade airfoil parameters and the real-time speed measurement device respectively.

7. The method for monitoring wind turbine icing based on atmospheric parameters in a freezing environment according to claim 1, Features: The method for reconstructing the three-dimensional ice shape of the synthetic wind turbine blade is: calculate the ice mass of all cross-sections of the wind turbine blade, and finally reconstruct the two-dimensional ice shape simulation results obtained from all slices to obtain the three-dimensional ice shape result of the entire wind turbine blade.

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