Turbulence identification method based on tower bottom signal, load reduction method and identification system

Abstract

The application discloses a kind of based on tower bottom signal's turbulent flow identification method, load reduction method and identification system, belong to wind power technology field, to solve the current wind power turbulent flow intensity identification cost is high, practicality is not strong and so on technical problems, this identification method includes steps:1) the tower bottom signal and wind turbine state data of wind turbine are obtained;The tower bottom signal includes tower bottom deformation or tower bottom load;2) according to the tower bottom load and wind turbine state data of wind turbine, the turbulent flow intensity of wind turbine is obtained.The application has the advantages of strong practicality, low cost and the like.

Description

Technical Field

[0001] This invention mainly relates to the field of wind power technology, specifically to a turbulence identification method, load reduction method, and identification system based on tower base signals. Background Technology

[0002] With the advent of the grid parity era, the cost pressure on wind turbines is increasing, and owners are demanding higher reliability from their turbines. Currently, most methods for determining turbulence intensity are based on measured loads at the tower and blade roots, or on parameters such as wind speed or thrust. After obtaining the turbulence intensity, the turbine operating parameters are then adjusted to reduce the load. However, these methods are either impractical or prohibitively expensive in real-world engineering applications. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a highly practical turbulence identification method, load reduction method and identification system based on tower bottom signals, which addresses the problems existing in the prior art.

[0004] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0005] A turbulence identification method based on signals from the bottom of a tower includes the following steps:

[0006] 1) Acquire the tower bottom signal and wind turbine status data; the tower bottom signal includes the tower bottom deformation or tower bottom load;

[0007] 2) The turbulence intensity of the fan is obtained based on the tower bottom signal and fan status data.

[0008] As a further improvement to the above technical solution:

[0009] In step 1), the deformation or load at the bottom of the tower is obtained by attaching strain gauges to the bottom of the tower and then acquiring the voltage signal output by the strain gauges.

[0010] In step 1), the wind turbine status data includes one or more of the following: nacelle wind speed, yaw error, wind turbine power, rotor speed, generator speed, generator torque, blade pitch angle, and pitch rate.

[0011] The specific steps for step 2) are as follows:

[0012] By continuously collecting the strain gauge signal at the bottom of the tower, the turbulence intensity A corresponding to the strain signal at the bottom of the tower over a period of time can be obtained;

[0013] Based on the analysis results of the simulation data, the relationship between the first turbulence intensity at the bottom of the tower and the second turbulence intensity obtained from the wind speed data was obtained.

[0014] By applying the relationship between the first turbulence intensity and the second turbulence intensity to turbulence intensity A, the turbulence intensity B during the actual operation of the wind turbine is obtained.

[0015] This invention also discloses a load reduction method based on tower bottom signals, comprising the following steps:

[0016] S1. The turbulence intensity of the wind turbine is obtained by using the turbulence identification method based on the tower bottom signal as described above;

[0017] S2. Compare the real-time turbulence intensity with the preset turbulence intensity, and control the fan's operating mode to reduce load based on the comparison result.

[0018] As a further improvement to the above technical solution:

[0019] In step S2, the preset turbulence intensity corresponds to the planned range of the operating turbulence curve. Specifically, the limit value of the turbulence curve range for safe operation of the wind turbine is planned based on the relationship between the design load of the wind turbine structure and the air density and turbulence intensity.

[0020] In step S2, the real-time turbulence intensity is compared with the turbulence curve planning interval data. By determining the current turbulence intensity range of the wind turbine, the specific operating mode of the wind turbine is determined, and the corresponding control command is output to control the operating mode of the wind turbine. The operating mode includes one or more of the following: reduced power operation, feathering shutdown, increased pitch angle, and high-speed pitch response mode.

[0021] This invention further discloses a turbulence identification system based on signals from the bottom of a tower, comprising:

[0022] The first module is used to acquire tower bottom signals and wind turbine status data; the tower bottom signals include tower bottom deformation or tower bottom load.

[0023] The second module is used to obtain the turbulence intensity of the wind turbine based on the tower bottom signal and wind turbine status data.

[0024] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when run by a processor, executes the steps of the turbulence identification method based on tower bottom signals as described above.

[0025] The present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when run by the processor, executes the steps of the turbulence identification method based on tower bottom signals as described above.

[0026] Compared with the prior art, the advantages of the present invention are as follows:

[0027] This invention verifies or corrects the calibration relationship between tower bottom deformation / load and turbulence intensity by using load measurement data from the same type of unit in advance; thus, when the wind turbine is running normally, it obtains the wind turbine status data in real time based on the tower bottom deformation / load, in order to obtain the turbulence intensity, and then reduces the load by switching the corresponding wind turbine control mode.

[0028] This invention uses a small number of low-cost strain gauges to estimate turbulence intensity using the voltage signals from the strain gauges. This eliminates the need for strain gauge calibration and enables a low-cost turbulence intensity estimation function for generator units, making it highly practical. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating the method of the present invention in a specific application.

[0030] Figure 2 This is a turbulence intensity-wind speed curve diagram of the present invention. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0032] like Figure 1 As shown, the turbulence identification method based on tower bottom signals in this embodiment includes the following steps:

[0033] 1) Acquire tower bottom signals and wind turbine status data; where tower bottom signals include tower bottom deformation or tower bottom load;

[0034] 2) The turbulence intensity of the fan is obtained based on the tower bottom signal and fan status data.

[0035] Since all loads and load fluctuations during wind turbine operation are reflected in the tower base deformation or load, and the key environmental variable affecting wind turbine load and lifespan is turbulence intensity, the turbulence intensity is obtained by acquiring tower base deformation or load and wind turbine status data in real time during normal wind turbine operation. Subsequently, the load is reduced by switching the corresponding wind turbine control mode.

[0036] In this embodiment, in step 1), the deformation or load at the bottom of the tower is obtained by attaching strain gauges to the bottom of the tower and acquiring the voltage signal output by the strain gauges. The deformation x and load y at the bottom of the tower are related by a linear function. Strain deformation is a direct measurement quantity, while the load at the bottom of the tower is an indirect conversion quantity. By adding a small number of very low-cost strain gauges and estimating turbulence intensity using their voltage signals, the calibration work of the strain gauges can be eliminated (where strain gauges measure strain signals, which normally require calibration to convert them into loads using the relationship y = kx + b. However, for turbulence, the solution formula is: standard deviation of a set of data / average value of that set of data. After removing the slope constant k and intercept b, the fluctuation trends of the strain signal and load signal are consistent. For the purpose of calculating turbulence intensity, it is not necessary to know the specific load value; only the turbulence calculation formula needs to be obtained). This achieves a very low-cost unit turbulence intensity estimation function, which is highly practical.

[0037] In this embodiment, in step 1), the wind turbine status data includes one or more of the following: nacelle wind speed, yaw error, wind turbine power, rotor speed, generator speed, generator torque, blade pitch angle, and pitch rate.

[0038] In this embodiment, in step 2), based on the obtained tower bottom strain gauge signal and wind turbine status data, the real-time turbulence intensity of the wind turbine is calculated using the voltage signal fluctuation of the tower bottom strain gauge (such as amplitude, rate of change, etc.) based on the built-in turbulence intensity algorithm.

[0039] The detailed steps are as follows: By continuously collecting the strain gauge signal at the bottom of the tower, using 600s data as the sliding window, the turbulence intensity A corresponding to the strain signal at the bottom of the tower within this time window is obtained according to the turbulence calculation formula;

[0040] By combining the analysis results of a large amount of simulation data, the relationship between the first turbulence intensity at the bottom of the tower and the second turbulence intensity obtained from wind speed data was obtained.

[0041] By applying the relationship between the first turbulence intensity and the second turbulence intensity to turbulence intensity A, the wind speed turbulence intensity B during the actual operation of the wind turbine is obtained.

[0042] Based on the above-mentioned turbulence identification method, the present invention further discloses a load reduction method, specifically including:

[0043] S1. Obtain the real-time turbulence intensity of the fan using the turbulence identification method described above;

[0044] S2. Compare the real-time turbulence intensity with the preset turbulence intensity, and control the fan's operating mode to reduce load based on the comparison result.

[0045] In this embodiment, in step S2, the aforementioned preset turbulence intensity corresponds to a planned interval for the operating turbulence curve. Specifically, based on the relationship between the design load of the wind turbine structure and air density and turbulence intensity, the turbulence curve interval limit for safe operation of the wind turbine is planned. The load on key components of the wind turbine is related in real-time to the air density, turbulence intensity, and wind speed at which the wind turbine is located. During the design phase, a series of curves can be obtained through extensive load calculations, such as... Figure 2 As shown: the X-axis represents wind speed, the Y-axis represents turbulence intensity, and the series of curves represent air density.

[0046] Further, in step S2, the real-time turbulence intensity is compared with the data of the planned turbulence curve interval. By determining the current turbulence intensity interval of the wind turbine, the specific operating mode of the wind turbine is determined (such as reduced power operation, feathering shutdown, increased pitch angle, high-speed pitch response mode, etc.), and corresponding control commands are output to control the wind turbine's operating mode. The above series of curves plan the safe operating range of the wind turbine. When the combination of real-time data points x and y exceeds the range (the area below the curve is the safe range), the wind turbine will take subsequent actions to reduce load.

[0047] Furthermore, the output operating mode is compared with the current operating mode of the wind turbine; if they match, the current operating state is maintained; if they do not match, the specified operating mode is switched to reduce the load on the wind turbine.

[0048] This invention also discloses a turbulence identification system based on signals from the bottom of a tower, comprising:

[0049] The first module is used to acquire tower base load and wind turbine status data;

[0050] The second module is used to obtain the turbulence intensity of the wind turbine based on the tower base load and wind turbine status data.

[0051] The identification system of the present invention is used to perform the identification method described above, and also has the advantages described above.

[0052] This invention also discloses a computer-readable storage medium storing a computer program thereon. When run by a processor, the computer program executes the steps of the turbulence identification method based on tower base signals as described above. This invention further discloses a computer device including a memory and a processor. The memory stores a computer program, which, when run by a processor, executes the steps of the turbulence identification method based on tower base signals as described above. This invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Memory can be used to store computer programs and / or modules. The processor performs various functions by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0053] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for identifying a turbulent flow based on a tower bottom signal, characterized by, The method comprises the steps of: 1) obtaining a tower bottom signal of the wind turbine and wind turbine state data; the tower bottom signal comprises a tower bottom deformation or a tower bottom load; 2) obtaining a turbulence intensity of the wind turbine according to the tower bottom signal of the wind turbine and the wind turbine state data; In step 1), the tower bottom deformation or load is obtained by pasting strain gauges on the bottom of the tower drum and then obtaining the voltage signal output by the strain gauges; The specific steps of step 2) are: By continuously collecting the tower bottom strain gauge signal, the turbulence intensity A corresponding to the tower bottom strain signal in a period of time is obtained; Combining the analysis result of the simulation data, the relationship between the first turbulence intensity of the tower bottom and the second turbulence intensity obtained by the wind speed data is obtained; By applying the relationship between the first turbulence intensity and the second turbulence intensity to the turbulence intensity A, the turbulence intensity B in the real running process of the wind turbine is obtained.

2. The tower bottom signal based turbulence identification method of claim 1, wherein, In step 1), the wind turbine state data comprises one or more of the cabin wind speed, yaw error, wind turbine power, wind wheel speed, generator speed, generator torque, blade pitch angle, and pitch rate.

3. A method of load reduction based on a column bottom signal, characterized by, The method comprises the steps of: S1, obtaining the turbulence intensity of the wind turbine by the turbulence identification method based on the tower bottom signal according to any one of claims 1-2; S2, comparing the real-time turbulence intensity with the preset turbulence intensity, and controlling the operation mode of the wind turbine according to the comparison result to reduce the load.

4. The tower bottom signal based de-rating method of claim 3, wherein, In step S2, the preset turbulence intensity corresponds to an operation turbulence curve planning interval, which is planned according to the corresponding relationship between the design load of the wind turbine structure and the air density and the turbulence intensity, and the turbulence curve interval limit value for safe operation of the wind turbine.

5. The tower bottom signal based de-rating method of claim 4, wherein, In step S2, the real-time turbulence intensity is compared with the turbulence curve planning interval data, the specific operation mode of the wind turbine is determined by determining which turbulence intensity interval the wind turbine is currently in, and the corresponding control instruction is output to control the operation mode of the wind turbine; wherein the operation mode comprises one or more of the reduced power operation, the pitch stop, the increased pitch angle, and the pitch high-speed response mode.

6. A tower-bottom-signal-based turbulence identification system for performing the steps of the tower-bottom-signal-based turbulence identification method according to any one of claims 1 to 2, characterized by The method comprises: A first module for obtaining a tower bottom signal of the wind turbine and wind turbine state data; the tower bottom signal comprises a tower bottom deformation or a tower bottom load; A second module for obtaining a turbulence intensity of the wind turbine according to the tower bottom signal of the wind turbine and the wind turbine state data.

7. A computer readable storage medium having stored thereon a computer program, characterized in that The computer program, when executed by the processor, performs the steps of the turbulence identification method based on the tower bottom signal according to any one of claims 1-2. 8.A computer device, comprising a memory and a processor, wherein a computer program is stored on the memory, and the computer device is characterized in that, The computer program, when executed by the processor, performs the steps of the turbulence identification method based on the tower bottom signal according to any one of claims 1-2.

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