Online fan blade unbalance detection method and system based on millimeter wave radar technology

Abstract

The invention provides an online fan blade unbalance detection method and system based on a millimeter wave radar array. A radar array is vertically arranged on the outer wall of the tower, and the blades are measured in real time in a non-contact mode. The core method is to analyze the relative deformation difference between the blades in the same period, construct average and maximum deformation indexes, and compare the average and maximum deformation indexes with a threshold value to realize fault diagnosis and positioning. The system adopts a two-stage judgment strategy: a plurality of radar data in a single period are synthesized to judge short-term abnormity, and then instantaneous interference and real faults are distinguished based on abnormal frequencies in a plurality of continuous rotation periods. According to the invention, online, real-time and high-precision blade state monitoring is realized, the method has the advantages of high environmental adaptability, excellent anti-interference capability, perfect early warning mechanism, low maintenance cost and the like, and reliable guarantee is provided for safe and stable operation of the wind generating set. The method is high in environmental adaptability and good in anti-interference performance, the blade state can be monitored on line in a high-precision mode, and safe and stable operation of a unit is guaranteed.

Description

Technical Field

[0001] This invention is applied in the field of wind power generation, specifically relating to an online wind turbine blade imbalance detection and system based on millimeter-wave radar technology. It achieves real-time, high-precision monitoring of blade condition through non-contact measurement. Background Technology

[0002] Wind turbines, as an important carrier of clean energy, are typically installed in harsh environments with poor accessibility, such as in the field and at sea. Their blades, as the core components for capturing wind energy, are subjected to complex and alternating aerodynamic loads over long periods, making them prone to imbalance faults. Blade imbalance is mainly caused by factors such as blade icing, lightning strike damage, surface erosion, structural cracks, and manufacturing and installation errors.

[0003] Currently, the main detection methods include vibration signal analysis, which involves installing vibration sensors in locations such as the gearbox, main shaft, or nacelle to collect vibration signals and analyze their spectral characteristics to indirectly determine whether the rotor is unbalanced. However, this method has drawbacks such as long signal transmission paths, severe signal attenuation and interference after passing through multiple stages of components such as bearings and gears, low signal-to-noise ratio, difficulty in capturing early and weak unbalance characteristics, and inability to directly determine which specific blade is causing the problem. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and system for monitoring and early warning of wind turbine blade misalignment based on millimeter-wave radar. This system achieves real-time monitoring and intelligent early warning of blade misalignment through a non-contact measurement method. To achieve the above objective, this invention adopts the following technical solution: S1. A row of millimeter-wave radar detection arrays is vertically installed in the middle of the outer wall of the wind turbine tower. The arrays are arranged to cover the entire vertical range swept by the blades when they rotate. S2. The millimeter-wave radar detection array transmits millimeter-wave signals to the rotating blades and receives the echoes, and collects dynamic distance data between each blade and the tower in real time. S3. Based on the distance data collected within the same measurement cycle, calculate the relative deformation difference between two blades at the same millimeter-wave radar measurement point; S4. Make a comprehensive judgment based on the monitoring results of multiple radar arrays within a single measurement cycle; S5. Based on the relative deformation difference, simultaneously calculate the average deformation index to reflect the overall offset trend of the blade and the maximum deformation index to capture the maximum local deviation. S6. Establish a multi-level early warning system based on the average deformation, and set an independent advanced alarm triggering mechanism according to the maximum deformation. S7. Combine the warning triggering status within multiple consecutive rotation cycles to determine continuous faults, in order to distinguish between transient interference and real mechanical faults.

[0005] Furthermore, in step S1, the millimeter-wave radar detection array is distributed along the tower axis, and its installation position corresponds to the key monitoring areas of the blade root, blade middle and blade tip during blade operation, so as to form a complete monitoring network for the full-dimensional motion trajectory of the blade.

[0006] Furthermore, the specific implementation method of "calculating the relative deformation difference" in step S3 is as follows: For the j-th millimeter-wave radar detection array, calculate the deformation differences between blade 1 and blade 2, blade 2 and blade 3, and blade 3 and blade 1 respectively: Representing the i The leaf, the first j The millimeter-wave radar measured real-time distance data.

[0007] Furthermore, the specific implementation method of "performing continuous fault judgment" in step S7 is as follows: If the system has a total of a millimeter-wave radar arrays installed, and within the same measurement period, b arrays simultaneously show abnormal indications, and the following conditions are met... If so, it is determined that the blade has experienced a short-term anomaly within that cycle.

[0008] Furthermore, the calculation of the indicator in step S5 includes: Set index one (average deformation) as This is reflected at the measurement point. j The average degree of imbalance of the entire leaf at that location; Set index two (maximum deformation) as follows: .

[0009] Furthermore, the "multi-level early warning system based on average deformation" mentioned in step S5 specifically includes: Normal state: When At that time, the system was within a safe operating range; Level 1 warning: When At that time, a Level 1 warning was issued; Level 2 warning: When At that time, a level-two warning was issued; Level 3 Warning: When At that time, a level-three warning was issued. Attached Figure Description

[0010] Figure 1 This is the algorithm flowchart.

[0011] Figure 2 and Figure 3 In the diagram, 1, 2, and 3 represent three blades, 4 represents a generator, 5 represents a millimeter-wave radar detection array, and 6 represents a tower.

[0012] Figure 2 This is a side view of an embodiment of the present invention.

[0013] Figure 3 This is a front view of an embodiment of the present invention. Specific implementation methods

[0014] This invention provides a method for detecting online wind turbine blade imbalance based on millimeter-wave radar. The detection method includes: Furthermore, multiple millimeter-wave radar arrays can be installed depending on the specific circumstances, but at least three.

[0015] For example, by installing three millimeter-wave radar arrays, at least one millimeter-wave radar is deployed in each monitoring area to form a complete monitoring network for the full-dimensional motion trajectory of the blades.

[0016] The millimeter-wave radar can operate in industrial frequency bands of 24 GHz, 60 GHz, or 77 GHz to meet monitoring needs under different environmental conditions. The radar's installation angle and position should be precisely adjusted according to the actual running trajectory of the blades to ensure accurate detection of changes in the distance between the blades and the tower.

[0017] Furthermore, let the blade designation... i =1, 2, 3, millimeter-wave radar ranging array designation j =1, 2, 3... N , representing the real-time distance data measured by the i-th blade and the j-th millimeter-wave radar.

[0018] For example, three millimeter-wave radar arrays are used to detect the tower. Within the same cycle, the deformation difference between each pair of blades is first calculated. Set index one (average deformation) as This reflects the average degree of imbalance of the entire blade at the measurement point j. Set index two (maximum deformation) as follows: .

[0019] Furthermore, a multi-level early warning system based on average deformation is used to determine the early warning status: Normal state: When At that time, the system was within a safe operating range; Level 1 warning: When At that time, a Level 1 warning was issued; Level 2 warning: When At that time, a level-two warning was issued; Level 3 Warning: When At that time, a level-three warning was issued.

[0020] Further, long-term abnormal warning judgment: if any leaf is in continuous m Within one rotation cycle, n Next (i.e.) If a blade receives a "short-term abnormal warning" of 10 or above, it is determined that the blade has a continuous imbalance fault.

[0021] The beneficial effects of this invention are: 1. The millimeter-wave radar rangefinder used in this invention is low in cost, and only a minimum of three can be deployed for normal operation. The system has low maintenance costs and is easy to maintain. 2. This invention directly monitors the spatial motion trajectory of the blade using a millimeter-wave radar array, fundamentally avoiding the problems of long signal transmission paths, large attenuation, and numerous interferences inherent in traditional vibration analysis methods. The measurement results directly reflect the blade's operating state, unaffected by intermediate components such as the main shaft and gearbox, greatly improving the accuracy and precision of the detection. 3. This invention enables uninterrupted monitoring during normal wind turbine power generation without requiring shutdown, achieving true online real-time monitoring. By capturing minute deviations in the trajectory, it can issue early warnings at the initial stage of imbalance faults, providing maintenance personnel with sufficient response time, thereby preventing the fault from escalating and ensuring the safety of wind farm assets. 4. By analyzing the independent trajectories of the three blades, the system can not only determine whether an imbalance exists, but also uniquely identify the specific blade number that has failed through the phase information of the deviation. This changes the predicament of traditional methods that can only issue alarms but cannot locate the problem, transforming maintenance work from "blind troubleshooting" to "precision operation," greatly reducing downtime for troubleshooting, and significantly lowering operation and maintenance costs and power generation losses. 5. The system has a simple structure, making it easy to install and maintain. All detection modules are mounted on the tower, eliminating the need to install any sensors or power supply devices on the high-speed rotating blades. This simplifies system installation, commissioning, calibration, and maintenance, reducing initial investment and life-cycle maintenance costs.

Claims

1. I A method for online detection of fan blade imbalance based on millimeter wave radar technology, characterized in that, The method comprises the following steps: S1, vertically arranging millimeter wave radar arrays along the axial direction of the outer wall of the wind turbine tower, and the arrangement height of the array should be within the height range covered by the blades during rotation; S2, transmitting millimeter wave radar to the rotating blades through the millimeter wave radar array and receiving the echo, collecting real-time distance data between each blade and the tower, and sending the data to a processing unit; S3, based on the collected data, calculating the relative deformation difference of each pair of blades at the same measuring point within the same measurement period; S4, calculating the comprehensive judgment index of blade imbalance according to the relative deformation difference, and calculating the comprehensive judgment index according to the relative deformation difference, and diagnosing the blade imbalance state by comparing the index with the preset threshold value; S5, by comparing the relationship between the comprehensive judgment index and the preset threshold value, judging whether the blade appears short-term abnormality; S6, establishing a multi-level early warning system based on the average deformation amount, and setting an independent high-level alarm triggering mechanism according to the maximum deformation amount; S7, based on the occurrence of short-term abnormality in a plurality of continuous rotation periods, determining whether the blade has a persistent imbalance fault.

2. The method of claim 1, wherein, In step S1, the number of millimeter wave radar detection arrays is multiple, at least three, arranged vertically along the axial direction of the tower to form a complete monitoring network.

3. The method of claim 1, wherein, In step S3, the "calculation of relative deformation difference" specifically includes: For the jth millimeter wave radar detection array, the deformation difference between blade 1 and blade 2, blade 2 and blade 3, and blade 3 and blade 1 is calculated, respectively: wherein, represents the real-time distance of the i th leaf at the j th millimeter wave radar detection array.

4. The method of claim 3, wherein, In step S4, the "comprehensive judgment index" includes: Average deformation: ; Maximum deformation: .

5. The method of claim 4, wherein, In step S5, the specific standard for "judging whether the blade appears short-term abnormality" is: if the average deformation exceeds its preset threshold Δ j Average In step S5, the "judgment of whether the blade appears short-term abnormality" is a comprehensive short-term abnormality warning, and the judgment logic further includes: or the maximum deformation exceeds its preset threshold In step S6, the "multi-level early warning system based on the average deformation amount" specifically includes: j m a x an abnormal indication of the mmWave radar detection array in the current measurement cycle is determined.

6. The method of claim 6, wherein, In step S7, the specific standard for "judging whether the blade has a persistent imbalance fault" is: If the system has a total of a millimeter wave radar arrays, in the same measurement period, b arrays simultaneously appear as abnormal indication as claimed, and meet Then it is determined that the blade has a short-term anomaly in this period.

7. The method of claim 4, wherein, ​ Normal state: when the system is in a safe operating range; Primary warning: when Primary warning; Secondary warning: when a secondary warning; Level 3 warning: when Level 3 warning; The 50% and 80% values representing the average deformation threshold are only exemplary suggestions, and a person skilled in the art can reasonably adjust the size of the values according to the specific working conditions.

8. The method of claim 6, wherein, ​ If there are n short-term abnormalities in consecutive m rotation periods, it is finally determined that the blade has a persistent unbalance fault; wherein m and n are preset positive integers, and is greater than a preset proportion.

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