Fan blade working condition identification method based on magnetic field component analysis

Abstract

The invention discloses a fan blade working condition identification method based on magnetic field component analysis, and the method comprises the steps: S1, installing a three-axis magnetic field sensor on a fan blade, and obtaining the original magnetic field data in the movement process of the blade; s2, on the basis of a preset magnetic field component analysis model, modeling and magnetic field component derivation are conducted on the yawing working condition, the longitudinal swinging working condition and the torsion working condition of the blade; and S3, when the blade has the yawing working condition, the longitudinal swinging working condition and the torsion working condition at the same time, the comprehensive working condition of the blade is recognized and monitored based on a preset multi-working-condition superimposed magnetic field component resolving formula. The invention discloses a fan blade working condition identification method based on magnetic field component analysis, and aims to solve the problems of high cost, poor real-time performance and insufficient precision of an existing blade dynamic analysis method proposed in the background technology.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine blade monitoring technology, specifically relating to a method for identifying the operating conditions of wind turbine blades based on magnetic field component analysis. Background Technology

[0002] During operation, wind turbine blades are subjected to various external forces, including wind load, gravity, and centrifugal force, resulting in complex dynamic responses such as longitudinal oscillation, lateral oscillation, and torsional deformation. These dynamic characteristics not only adversely affect the energy capture efficiency of the turbine but may also accelerate fatigue damage to the blades and their roots, ultimately threatening the operational safety of the turbine. Therefore, accurately identifying the dynamic attitude, oscillation amplitude, and torsional angle of the blades is crucial for online monitoring of blade condition, improving operational efficiency, and preventing malfunctions.

[0003] In existing technologies, common blade dynamic analysis methods often rely on a combination of experimental measurements and numerical simulations. For example, strain distribution is measured using multi-point strain gauges, and then the deformation state of the blade is inverted using finite element analysis. However, these methods have significant limitations: firstly, they require the deployment of multiple sensors, resulting in high system costs and complex setup processes; secondly, the computational load of data fusion and filtering algorithms is large, leading to poor real-time performance; and thirdly, multi-point measurement methods are still susceptible to noise interference and introduce errors during the solution process, making it difficult to obtain high-precision and robust attitude and pitch estimations.

[0004] In contrast, direct derivation based on mathematical formulas and kinematic modeling can decouple the yaw and torsion motions of the blades into analytical mathematical forms. Utilizing mathematical methods such as frequency response, angle analysis, and amplitude calculation, analytical solutions and judgment criteria for yaw amplitude and torsion angle can be established. This method not only reduces reliance on hardware data acquisition but also provides theoretical support for algorithm design, making attitude recognition and yaw amplitude determination more efficient and interpretable. Furthermore, by deriving the kinematic and dynamic equations, the contribution of different frequency components to the system response can be further analyzed, providing a quantitative basis for assessing the health status of wind turbine blades. Based on this, this invention proposes a wind turbine blade operating condition identification method based on magnetic field component analysis to address the problems existing in the prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method for identifying the operating conditions of wind turbine blades based on magnetic field component analysis, in order to solve the problems of high cost, poor real-time performance, and insufficient accuracy of existing blade dynamic analysis methods mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a method for identifying the operating conditions of wind turbine blades based on magnetic field component analysis, comprising the following steps: Step S1: Install a triaxial magnetic field sensor on the wind turbine blades to acquire the raw magnetic field data during the blade movement process; Step S2: Based on the preset analytical model of magnetic field components, model and derive the magnetic field components for the yaw, yaw and torsional conditions of the blade respectively, and obtain the calculation expression of the magnetic field components and the frequency analysis expression for each condition. Step S3: When the blade is simultaneously subjected to yaw, yaw and torsion conditions, the comprehensive operating condition of the blade is identified and monitored based on the preset multi-condition superimposed magnetic field component solution formula.

[0007] As a further preferred technical solution to the above technical solution, the modeling and magnetic field component derivation of the yaw condition in step S2 specifically includes: Under yaw conditions, the yaw angle and magnetic field components of the blade change with time, exhibiting periodic characteristics. The three-dimensional position of the blade under yaw conditions is described by the following formula: ; ; ; in, and This represents the initial position of the leaf feature points in a static state. It is the blade mounting angle. It is the rotation frequency. and It refers to the modulation amplitude and modulation frequency of the yaw. For sensor data under yaw conditions, the expression for frequency analysis is: ; in, This represents the magnetic field quantity that changes in the x-direction with time. , The angle of rotation in real time is used as an intermediate variable; the frequency expression provides the main frequency and modulation frequency characteristics of the yaw. By performing FFT processing on the signal, the characteristic frequency and amplitude information under the yaw condition can be extracted.

[0008] As a further preferred technical solution to the above technical solution, the modeling and magnetic field component derivation of the longitudinal swing condition in step S2 specifically includes: The yaw condition describes the oscillation of the blade in the vertical plane. When the amplitude of the yaw angle of the blade is... At that time, the original angle calculation is corrected in the following way: ; in, It is the longitudinal angle of the leaf. This is the amplitude of the yaw angle. The analytical expression for the change of the yaw angle over time is: ; in, Indicates frequency, Indicates time, The time cutoff point indicating the occurrence of vibration is used to obtain the magnetic field components under the pendulum condition by substituting the corrected angle into the calculation formula for the magnetic field components: ; ; .

[0009] As a further preferred technical solution to the above technical solution, the modeling of the torsional condition and the derivation of the magnetic field components in step S2 specifically include: When the blade twists, the formula for calculating the magnetic field components is as follows: ; ; ; in, It's about twisting the angle. It is a rotation angle. It is the rotation frequency.

[0010] As a further preferred technical solution to the above technical solution, for step S3, when the blade yaw, longitudinal yaw, and torsion occur simultaneously, the formula for calculating the magnetic field components is: ; ; ; in, The frequency of the torsion is represented by these angles. By substituting these angles into the expressions for the three-axis magnetic field components, the final magnetic field components are obtained: ; ; ; The final magnetic field components are given by the following formula: ; .

[0011] Compared with the prior art, the beneficial effects of the present invention are: 1. Strong real-time performance: The algorithm of this invention does not require complex iterative calculations. Through simple formula derivation and frequency analysis, it can run quickly and in real time in embedded hardware, which is suitable for the long-term online monitoring needs of wind turbine units and effectively solves the problems of large data processing and poor real-time performance in the existing technology.

[0012] 2. Multi-condition adaptability: This invention can simultaneously handle three operating conditions: yaw, longitudinal yaw, and torsion. Through mathematical model decoupling analysis, it can not only detect different attitude changes in real time, but also effectively identify different motion states of the blades (such as rotation, abnormal torsion, resonance, etc.), providing more operating status information. Compared with existing single-condition analysis methods, it has a wider range of applications.

[0013] 3. Strong anti-interference capability: Because it employs an analytical formula based on the Earth's magnetic field, this invention does not rely on inertial sensors, thus possessing strong anti-interference capability. Even in environments with strong electromagnetic interference or high signal noise, it can still operate stably, ensuring the accuracy and reliability of operating condition identification and avoiding the shortcomings of existing methods that are susceptible to noise interference.

[0014] 4. Low cost and easy to implement: This invention only requires a single-point triaxial magnetic field sensor to be installed on the blade, without the need to deploy multiple sensors, which greatly reduces the system hardware cost and installation complexity, making it easy to promote and apply in actual wind turbine units, and solving the problems of high cost and complex layout of existing multi-point sensing methods. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the present invention.

[0016] Figure 2 This is a schematic diagram of the yaw condition of the wind turbine blades.

[0017] Figure 3 This is a schematic diagram of the longitudinal oscillation condition of the wind turbine blades.

[0018] Figure 4 This is a schematic diagram of the torsional operation of the wind turbine blades.

[0019] Figure 5 This is a schematic diagram of the magnetic field under the yaw condition of the present invention.

[0020] Figure 6 This is a schematic diagram of the frequency analysis of the longitudinal swing condition sensor of the present invention. Detailed Implementation

[0021] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0022] In the preferred embodiments of the present invention, those skilled in the art should note that the wind turbine blades and the like involved in the present invention can be considered as prior art.

[0023] Preferred embodiment.

[0024] like Figure 1-6 As shown, this invention discloses a method for identifying the operating conditions of wind turbine blades based on magnetic field component analysis, comprising the following steps: Step S1: Install a triaxial magnetic field sensor on the wind turbine blades to acquire the raw magnetic field data during the blade movement process; Step S2: Based on the preset analytical model of magnetic field components, model and derive the magnetic field components for the yaw, yaw and torsional conditions of the blade respectively, and obtain the calculation expression of the magnetic field components and the frequency analysis expression for each condition. Step S3: When the blade is simultaneously subjected to yaw, yaw and torsion conditions, the comprehensive operating condition of the blade is identified and monitored based on the preset multi-condition superimposed magnetic field component solution formula.

[0025] Specifically, the modeling and magnetic field component derivation of the yaw condition in step S2 includes: Under yaw conditions, the yaw angle and magnetic field components of the blade change with time, exhibiting periodic characteristics. The three-dimensional position of the blade under yaw conditions is described by the following formula: ; ; ; in, and This represents the initial position of the leaf feature points in a static state. It is the blade mounting angle. It is the rotation frequency. and These are the modulation amplitude and modulation frequency of the yaw (this formula can effectively describe the magnetic field change of the blade under yaw conditions, which is convenient for subsequent calculation of the blade's yaw angle and amplitude). For sensor data under yaw conditions, the expression for frequency analysis is: ; in, This represents the magnetic field quantity that changes in the x-direction with time. , The angle of rotation in real time is used as an intermediate variable; the frequency expression provides the main frequency and modulation frequency characteristics of the yaw. By performing FFT processing on the signal, the characteristic frequency and amplitude information under the yaw condition can be extracted.

[0026] More specifically, the modeling of the pendulum condition and the derivation of the magnetic field components in step S2 include: The yaw condition describes the oscillation of the blade in the vertical plane. When the amplitude of the yaw angle of the blade is... At that time, the original angle calculation is corrected in the following way: ; in, It is the longitudinal angle of the leaf. This is the amplitude of the yaw angle. The analytical expression for the change of the yaw angle over time is: ; in, Indicates frequency, Indicates time, The time cutoff point indicating the occurrence of vibration is used to obtain the magnetic field components under the pendulum condition by substituting the corrected angle into the calculation formula for the magnetic field components: ; ; This formula effectively models the magnetic field changes under longitudinal oscillation conditions, providing accurate magnetic field components for subsequent calculations, and can be used to extract the amplitude and frequency of the blade during longitudinal oscillation.

[0027] Furthermore, the modeling of the torsional condition and the derivation of the magnetic field components in step S2 specifically include: When the blade twists, the formula for calculating the magnetic field components is as follows: ; ; ; in, It's about twisting the angle. It is a rotation angle. This is the rotational frequency (fundamental rotational frequency). (This formula can accurately describe the torsional dynamics of the blade, and, combined with the magnetic field components, further calculate the torsional amplitude.) The changes in these values ​​can be used to extract the characteristic frequencies and amplitudes of the torsional condition.

[0028] Furthermore, for step S3, when the blade's yaw, yaw, and torsion occur simultaneously, the formula for calculating the magnetic field components is: ; ; ; in, The frequency of the torsion is represented by these angles. By substituting these angles into the expressions for the three-axis magnetic field components, the final magnetic field components are obtained: ; ; ; The final magnetic field components are given by the following formula: ; .

[0029] These formulas effectively decouple and enable real-time monitoring of the three operating conditions of the blade (yaw, yaw, and torsion). The algorithm of this invention accurately describes the yaw, yaw, and torsion conditions of the blade through mathematical modeling and achieves multi-degree-of-freedom attitude calculation based on analytical formulas for the three-axis magnetic field components. Compared with traditional multi-sensor fusion methods, the method of this invention not only significantly reduces hardware costs and installation complexity but also possesses higher real-time performance and robustness, making it suitable for blade health monitoring, dynamic analysis, and fault prediction in complex environments.

[0030] It is worth mentioning that the technical features such as wind turbine blades involved in this patent application should be regarded as prior art. The specific structure, working principle, and possible control methods and spatial arrangement of these technical features can be conventionally selected in the field and should not be regarded as the inventive point of this patent. This patent will not be further elaborated in detail.

[0031] For those skilled in the art, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the protection scope of this invention.

Claims

1. A fan blade working condition identification method based on magnetic field component analysis, characterized in that, The method comprises the following steps: Step S1: installing a three-axis magnetic field sensor on a fan blade to obtain original magnetic field data during blade movement; Step S2: based on a preset magnetic field component analysis model, modeling and deriving magnetic field components for a yaw condition, a pitch condition and a twist condition of the blade respectively to obtain a magnetic field component calculation expression and a frequency analysis expression under each condition; Step S3: when the blade simultaneously exists in the yaw condition, the pitch condition and the twist condition, based on a preset multi-condition superimposed magnetic field component solving formula, identifying and monitoring a comprehensive condition of the blade.

2. The fan blade operating condition recognition method based on magnetic field component analysis according to claim 1, characterized in that, The modeling and magnetic field component derivation of the yaw condition in step S2 specifically include: Under the yaw condition of the blade, the yaw angle of the blade and the magnetic field component change with time, showing periodic characteristics, and the three-dimensional position of the blade under the yaw condition is described by the following formula: ； ； ； wherein and is the initial position of the blade feature point in the stationary state, is the installation angle of the blade, is the rotational frequency, and is the yaw modulation amplitude and modulation frequency; For the sensor data under the yaw condition, the frequency analysis expression is: ； wherein, represents a magnetic field quantity in the x direction that changes over time, , represents the angle of rotation in real time as an intermediate variable; the frequency expression provides the main frequency and modulation frequency characteristics of the yaw, and by performing FFT processing on the signal, the characteristic frequency and amplitude information under the yaw working condition are extracted.

3. The method according to claim 2, wherein, The modeling and magnetic field component derivation of the pitch condition in step S2 specifically include: The pitch plane describes the oscillation of the blade in the vertical plane. When the pitch angle amplitude of the blade is corrected by the following way: ； wherein is the longitudinal angle of the blade, is the longitudinal pitch angle amplitude, the analytical expression of the longitudinal pitch angle as a function of time being: ； wherein, denotes the frequency, denotes the time, denotes the time point of the vibration generation, the magnetic field component in the pitching condition is obtained by substituting the corrected angle into the calculation formula of the magnetic field component: ； ； 。 4. The method according to claim 3, wherein, The modeling and magnetic field component derivation of the twist condition in step S2 specifically include: When the blade twists, the calculation formula of the magnetic field component is as follows: ； ； ； wherein is the torsion angle, is the rotation face angle, is the rotation frequency.

5. The method according to claim 4, wherein, For step S3, when the blade simultaneously exists in the yaw condition, the pitch condition and the twist condition, the solving formula of the magnetic field component is: ； ； ； wherein representing the frequency of the twist, by substituting these angles into the expressions for the three field components, the final field components are obtained: ； ； ； The final magnetic field component is given by the following formula: ； 。

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