Wind turbine generator blade running state online monitoring system and method

Abstract

The invention discloses a wind turbine generator blade running state online monitoring system and method, and belongs to the technical field of wind power generation. A voiceprint and laser displacement sensor integrated assembly is creatively installed at the bottom of a cabin, and the design of an extension rod and a vibration reduction hanging disc is adopted; while the environmental reliability of the sensor is remarkably improved and the installation and maintenance cost is reduced, the distance between the sensor and the blade is further shortened, and the acquisition of high-quality voiceprint signals is effectively ensured; according to the system, voiceprint data of each blade is accurately segmented by means of laser trigger signals, and online processing and three-blade cross-band energy comparative analysis are realized by means of an edge terminal system, so that early abnormality of the blade can be efficiently and accurately identified, and continuous intelligent monitoring of the blade state of the wind turbine generator with low cost, high reliability and strong anti-interference capability is realized.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, specifically relating to an online monitoring system and method for the operating status of wind turbine blades. Background Technology

[0002] As a clean and renewable energy source, wind power generation involves wind turbines operating in harsh natural environments for extended periods. Wind turbine blades are the core components for capturing wind energy; their complex structure and enormous size, coupled with the long-term effects of alternating loads and extreme weather, make them prone to surface damage, crack propagation, leading-edge corrosion, and icing.

[0003] Currently, frequent blade failures in wind turbines are causing significant losses to wind farm operations. Blade failures not only reduce power generation efficiency but can also lead to serious accidents such as blade breakage and tower collapse, resulting in huge economic losses and safety risks. Implementing online monitoring of wind turbine blades and keeping abreast of their operating status is of great significance for the safe and reliable operation of the turbines.

[0004] Currently, the main online monitoring technologies for blade operating status in the industry include the following: Monitoring methods based on sensors installed inside the blade include vibration sensors, fiber optic strain sensors, and acceleration sensors. These methods typically require implanting sensors during blade manufacturing or maintenance, resulting in complex installation processes, high costs, and challenges in the long-term reliability of the sensors within rotating components, as well as difficulties in signal transmission.

[0005] Acoustic emission (AE)-based monitoring methods diagnose damage by capturing stress waves generated by the propagation of internal cracks in the material. While this method is sensitive to localized damage, sensors typically need to be placed close to the damage location, and environmental noise and signal attenuation significantly impact long-distance monitoring, making it difficult to achieve a comprehensive assessment of the entire blade's condition.

[0006] Vision- or image-based monitoring methods, such as drone inspections and camera surveillance, are highly susceptible to changes in lighting and weather conditions, making continuous, real-time online monitoring difficult and requiring significant data analysis effort.

[0007] Voiceprint-based (sound signal) monitoring methods: This is currently the most widely researched and applied area. Existing voiceprint monitoring schemes mainly fall into two categories: External tower mounting: The microphone is placed on the outer wall of the tower. This method is susceptible to interference from environmental factors such as wind, rain, snow, and background noise. Furthermore, when the unit yaws, the sensor may move to the leeward side, resulting in signal loss or a sharp drop in quality. The sensor itself also faces the problem of aging and damage.

[0008] Internal blade mounting: The sensor is placed at the blade root or inside the cavity. This method is effective for loose blade bolts and abnormal noises from the internal structure, but it is less effective at capturing changes in external aerodynamic noise and acoustic characteristics caused by surface damage. Installation and maintenance are also inconvenient.

[0009] In summary, existing technologies suffer from drawbacks such as high installation and maintenance costs, poor sensor reliability, weak environmental adaptability, and insufficient sensitivity to typical blade faults. There is a lack of comprehensive online monitoring solutions that are easy to install, reliable in operation, can continuously and stably acquire high-quality acoustic signals, and can effectively identify early blade anomalies. Summary of the Invention

[0010] The purpose of this invention is to provide an online monitoring system and method for the operating status of wind turbine blades, so as to overcome the technical problems of poor signal quality and susceptibility to environmental influences in existing wind turbine blade monitoring systems.

[0011] To solve the above problems, the present invention adopts the following technical solution: An online monitoring system for the operating status of wind turbine blades includes an acoustic fingerprint sensor assembly and an edge terminal system connected to the acoustic fingerprint sensor assembly. The acoustic signature sensor assembly includes an acoustic signature sensor, an extension rod, and a connector. The connector is installed on the outer bottom surface of the wind turbine nacelle. One end of the extension rod is fixed to the connector, and the other end of the extension rod is equipped with an acoustic signature sensor. A laser displacement sensor is also installed on the extension rod. The edge terminal system is installed inside the wind turbine nacelle, and the acoustic sensor and laser displacement sensor are electrically connected to the edge terminal system.

[0012] Furthermore, the laser displacement sensor is positioned directly above the acoustic signature sensor.

[0013] Furthermore, the connector adopts a rubber-lined hanging plate.

[0014] Furthermore, the edge terminal system includes a signal acquisition module, a data processing module, and a communication module: The signal acquisition module is used to acquire acoustic signature signals and laser displacement signals; The data processing module is used to segment the acoustic signature signal based on the laser displacement signal; The communication module is used to transmit the processed data to a remote server.

[0015] Furthermore, it also includes a remote server, which is communicatively connected to the edge terminal system.

[0016] Secondly, a method for online monitoring of the operating status of wind turbine blades is provided, comprising the following steps: Acoustic data of the wind turbine blades is obtained by the acoustic sensor of the acoustic sensor assembly, and the moment when the wind turbine blades rotate to the set position is obtained by the laser displacement sensor installed on the extension rod. The acoustic data is segmented based on the time difference of rotation between the blades of two adjacent wind turbine units. The segmented voiceprint data is transformed in the time domain and divided into multiple frequency bands. The energy value of the voiceprint data in each frequency band is calculated. Based on the energy values ​​of acoustic signature data in each frequency band, the energy deviation ratio of each frequency band of the wind turbine blades is calculated. If the energy deviation ratio of any frequency band exceeds the threshold, an early warning will be issued.

[0017] Furthermore, taking the moment when the wind turbine blade reaches the set position as the center, the time difference extended forward and backward by 0.5 times is used as the acoustic fingerprint data of the wind turbine blade.

[0018] Furthermore, the multiple frequency bands include low-frequency band, mid-frequency band, and high-frequency band; The low-frequency band ranges from 20 to 200 Hz, the mid-frequency band ranges from 200 to 2000 Hz, and the high-frequency band ranges from 2000 Hz to 10000 Hz.

[0019] Furthermore, the energy deviation ratio is the ratio of the maximum to the minimum energy value within the same frequency band.

[0020] Furthermore, when an alert is issued, the voiceprint data and energy value data of each frequency band within the corresponding time period are saved; when no alert is issued, only the energy value data of each frequency band is saved.

[0021] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides an online monitoring system for the operating status of wind turbine blades. By innovatively installing components integrating acoustic signature and laser displacement sensors at the bottom of the nacelle, and employing an extension rod and vibration damping mounting plate design, the system significantly improves the environmental reliability of the sensors, reduces installation and maintenance costs, and further shortens the distance to the blades, effectively ensuring the acquisition of high-quality acoustic signature signals. The system relies on laser trigger signals to accurately segment the acoustic signature data of each blade, and utilizes an edge terminal system to achieve online processing and cross-frequency energy comparison analysis of the three blades. This enables efficient and accurate identification of early blade anomalies, achieving low-cost, high-reliability, and highly anti-interference continuous intelligent monitoring of wind turbine blade status.

[0022] This invention provides an online monitoring method for the operating status of wind turbine blades. It uses a laser displacement sensor to accurately obtain the arrival time of the blades and dynamically segments the continuous acoustic data stream accordingly. Then, it performs time-frequency analysis and characteristic energy extraction on the data of each blade. It innovatively introduces a multi-cycle averaging and cross-frequency energy lateral comparison mechanism for three blades, which effectively suppresses common-mode noise and random interference. This enables sensitive and accurate identification and early warning of early anomalies of individual blades, greatly improving the reliability and practicality of condition diagnosis. Attached Figure Description

[0023] Figure 1 This is a structural diagram of an online monitoring system for the operating status of wind turbine blades according to an embodiment of the present invention; Figure 2 This is a structural diagram of the voiceprint component in an embodiment of the present invention; Figure 3 This is a flowchart of an online monitoring method for the operating status of wind turbine blades according to an embodiment of the present invention.

[0024] In the diagram, 1. Wind turbine blade; 2. Acoustic sensor assembly; 3. Edge terminal system; 4. Acoustic sensor; 5. Laser displacement sensor; 6. Extension rod; 7. Rubber base plate. Detailed Implementation

[0025] To make the technical problems solved by the present invention, the technical solutions, and the beneficial effects clearer, the following specific embodiments provide a further detailed description of the present invention. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of the invention.

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0027] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0028] An online monitoring system for the operating status of wind turbine blades, such as Figure 1 As shown, it includes: Acoustic sensor component 2 is installed at the bottom of the wind turbine nacelle and is used to collect acoustic signals generated when the wind turbine blade 1 rotates. The edge terminal system 3 is installed inside the cabin and connected to the voiceprint sensor assembly 2. It is used to process, analyze and extract features from the collected voiceprint signals. It also includes a remote server, which communicates with the edge terminal system 3 to receive and store the processed monitoring data; The acoustic sensor assembly 2 includes an acoustic sensor 4, a laser displacement sensor 5, an extension rod 6, and a rubber base plate 7. The laser displacement sensor 5 is used to detect the moment signal when the blade rotates to the set position.

[0029] Specifically, the acoustic signature sensor assembly 2, as a signal sensing unit, is installed on the bottom outer shell of the wind turbine nacelle and extends towards the rotor's rotation plane. This assembly is designed as an integrated structure, such as... Figure 2 As shown, it specifically includes: Acoustic sensor 4: Used to collect broadband air acoustic signals (acoustic fingerprints) generated when the wind turbine blade 1 rotates through a specific area. The acoustic signature sensor 4 is a high-precision sensor specifically designed for collecting, recording, and analyzing sound characteristics. In this embodiment, the sound being monitored is the aerodynamic noise generated by the interaction between the wind turbine blade 1 and the air during rotation, as well as the sound radiated by the structural vibration of the wind turbine blade 1 itself due to damage, cracks, imbalance, etc.

[0030] Laser displacement sensor 5: Installed coaxially or adjacent to acoustic sensor 4, used for non-contact and precise detection of the moment when the wind turbine blade 1 reaches the preset position directly in front of acoustic sensor 4, and outputs a synchronous trigger signal; A laser displacement sensor is a precision instrument that uses laser technology for non-contact, high-precision distance measurement. It emits a laser beam to the surface of the object being measured, receives the reflected light signal, and calculates the distance or position change between the sensor and the surface of the object being measured through an internal optical system and processor. In this embodiment, it is used to detect whether the wind turbine blade 1 has rotated to the front of the acoustic sensor 4.

[0031] Extension rod 6: Used to fix and support the above-mentioned sensor, extending it outward from the nacelle wall by a certain distance (preferably ≥0.5 meters) to shorten the distance to the sound source of the wind turbine blade 1 and improve the signal-to-noise ratio; Rubber-lined mounting plate 7: Used to mount and fix the entire acoustic sensor assembly 2 to the cabin shell. The mounting plate uses flexible damping materials such as rubber as a substrate to effectively isolate and attenuate the transmission of mechanical vibrations of the cabin body to the acoustic sensor, avoiding vibration noise contamination of the acoustic signal.

[0032] The component extends through a pre-set mounting hole (the hole diameter is smaller than the diameter of the mounting plate) at the bottom of the cabin. The mounting plate is used to achieve a sealed fixation, so that the core sensor is in a relatively sealed external space of the cabin but is still protected to a certain extent, thus balancing signal quality and equipment protection.

[0033] The edge terminal system 3, serving as a local data processing unit, is installed in an electrical cabinet inside the cabin. It is connected to the acoustic signature sensor assembly 2 via cables, and its main functional modules include: Signal acquisition module: synchronously acquires analog / digital audio signals from acoustic sensor 4 and pulse signals from laser displacement sensor 5; Data processing module: Embedded with dedicated algorithms, responsible for real-time processing of acquired signals, including: segmenting data of each blade based on laser signals, performing time-frequency domain analysis, calculating feature energy, and executing three-blade comparison logic judgment, etc. Communication module: Uploads the processed feature data, early warning information, and necessary raw data to the remote server.

[0034] The setup of edge terminals enables data preprocessing and feature extraction at the source, greatly reducing the bandwidth requirements for remote data transmission and the pressure on cloud storage, making continuous and long-term online monitoring possible.

[0035] Optionally, a remote server system can be set up and deployed at the wind farm monitoring center or cloud platform. This system is responsible for receiving and storing data from edge terminals of multiple turbine units, providing a human-machine interface for historical data querying, status trend analysis, early warning information display, and generating operation and maintenance reports.

[0036] In one optional embodiment, the system is implemented on a 2MW wind turbine generator. A flat area is selected at the bottom of the nacelle, near the center of the hub, and a cable hole with a diameter of approximately 50mm is created. The sensor head, integrating a sound signature sensor 4 (using a wideband condenser microphone with a frequency response of 20Hz-20kHz) and a laser displacement sensor 5 (range range 0.5-10m, accuracy ±1mm), is fixed using a rigid stainless steel extension rod 6 with a length of 0.8 meters and a diameter of 30mm. The end of the extension rod 6 is connected to an aluminum alloy mounting plate with a diameter of 150mm and a center lining of 15mm thick neoprene rubber. During installation, the extension rod 6 is passed through the opening from inside the nacelle outwards, ensuring the mounting plate is flush against the outer wall of the nacelle. Bolts and sealant are used to firmly seal and fix the mounting plate to the nacelle skin, ensuring waterproofing and dustproofing. The sensor cable is introduced into the nacelle through the cable hole and connected to an edge terminal system 3 (using an industrial-grade embedded computer) installed in the nacelle control cabinet. Edge terminal system 3 connects to the wind farm's local area network through the wind turbine's original ring network switch and communicates with a remote server located in the substation monitoring center.

[0037] The online monitoring system for wind turbine blade operation provided by this invention eliminates the need for operation on the rotating wind turbine blade 1 by mounting the sensor assembly 2 on the bottom of a fixed nacelle, simplifying power supply and communication wiring. The rubber mounting plate for vibration damping and the partial protection of the nacelle shell significantly improve the sensor's environmental adaptability and service life under harsh weather conditions. The sensor, via an extension rod, approaches the sound source (blade) and simultaneously utilizes laser signals for precise synchronization and segmentation, effectively acquiring the acoustic signal primarily from the target wind turbine blade 1. Flexible installation and local data processing reduce vibration and electrical noise interference.

[0038] This invention also provides a method for online monitoring of the operating status of wind turbine blades, comprising the following steps: The acoustic data of the wind turbine blade 1 is obtained by the acoustic sensor 4 of the acoustic sensor assembly 2, and the moment when the wind turbine blade 1 rotates to the set position is obtained by the laser displacement sensor 5 installed on the extension rod 6. The acoustic data is segmented based on the time difference of rotation between two adjacent wind turbine blades 1. The segmented voiceprint data is transformed in the time domain and divided into multiple frequency bands. The energy value of the voiceprint data in each frequency band is calculated. Based on the energy values ​​of acoustic signature data in each frequency band, the energy deviation ratio of each frequency band of the wind turbine blades is calculated. If the energy deviation ratio of any frequency band exceeds the threshold, an early warning will be issued.

[0039] Detailed, such as Figure 3 As shown, the specific steps include: 1. When the wind turbine blade 1 rotates to overlap with the extension rod 6, the laser displacement sensor 5 measures the distance value. Otherwise, there is no valid measurement value, but a pulse signal appears. This moment is recorded as... i = 1, 2, 3...; 2. Taking the moment when wind turbine blade 1 reaches the set position as the center, extend forward and backward by 0.5 times the time difference to obtain the acoustic fingerprint data segment for wind turbine blade 1, and so on. The segmented acoustic fingerprint data is denoted as data. Where b represents the blade number, which is 1, 2, 3, and i is 1, 2, 3... The main function of this step is to segment and extract the acoustic signature data of each blade. The basic logic of segmentation is: the moment when a blade reaches the position of the rod is taken as the center point, and the data is pushed forward and backward by 0.5*. The time is used as the acoustic data for this blade; 3. Perform time-frequency conversion on the segmented voiceprint data to convert it into frequency domain data. Based on the segmentation, calculate the energy of the low-frequency, mid-frequency, and high-frequency bands, i.e., calculate the effective value of each frequency band, denoted as... , , Where L represents the low-frequency band, M represents the mid-frequency band, and H represents the high-frequency band. (Frequency band division rule: 20-200Hz is the low-frequency band, 200-2000Hz is the mid-frequency band, and 2000Hz-10000Hz is the high-frequency band.) 4. Every 5 consecutive Calculate separately , , The average values ​​are denoted as Lb, Mb, and Hb. That is, the average energy of each frequency band is calculated for every 5 revolutions of the impeller. Using these average values ​​can reduce the impact of abnormal noise and interference from other equipment in the unit. 5. Calculate the maximum energy deviation ratio of wind turbine blade 1 in each frequency band, denoted as . , , The calculation method is as follows: select the maximum and minimum values ​​among Lb, Mb, and Hb respectively, and t = maximum value / minimum value; 6. If the deviation ratio of any frequency band is greater than 1.5, an early warning will be issued, indicating to the maintenance personnel that the blade is operating abnormally, and the monitoring waveform data and energy value data of each frequency band will be saved; if the deviation ratio of all frequency bands is not greater than 1.5, no early warning will be issued, the blade is operating normally, and only the energy value data of each frequency band will be saved.

[0040] The method of this invention cleverly offsets the combined effects of wind speed, rotational speed, and ambient background noise on the three blades, focusing the analysis on the relative differences between the three blades. It is exceptionally sensitive to individual faults in wind turbine blades, with a low false alarm rate. By monitoring energy in different frequency bands (especially mid-to-high frequencies), it can capture early fault characteristics such as blade surface damage, leading edge defects, and initial icing that have not yet caused significant vibration or performance degradation, providing a valuable time window for preventive maintenance.

[0041] The preferred embodiments of the present invention have been described in detail above; however, the present invention is not limited thereto. Within the scope of the inventive concept, various simple modifications can be made to the technical solutions of the present invention, including combinations of various technical features in any other suitable manner. These simple modifications and combinations should also be considered as the content disclosed in the present invention and are all within the protection scope of the present invention.

Claims

1. An online monitoring system for the operating status of wind turbine blades, characterized in that, Includes a voiceprint sensor assembly (2) and an edge terminal system (3) connected to the voiceprint sensor assembly (2); The acoustic sensor assembly (2) includes an acoustic sensor (4), an extension rod (6) and a connector. The connector is installed on the outer bottom surface of the wind turbine nacelle. One end of the extension rod (6) is fixed to the connector, and the other end of the extension rod (6) is equipped with the acoustic sensor (4). A laser displacement sensor (5) is also installed on the extension rod (6). The edge terminal system (3) is installed inside the wind turbine nacelle, and the acoustic sensor (4) and the laser displacement sensor (5) are electrically connected to the edge terminal system (3) respectively.

2. The online monitoring system for the operating status of wind turbine blades according to claim 1, characterized in that, The laser displacement sensor (5) is positioned directly above the acoustic fingerprint sensor (4).

3. The online monitoring system for the operating status of wind turbine blades according to claim 1, characterized in that, The connector uses a rubber-lined hanging plate (7).

4. The online monitoring system for the operating status of wind turbine blades according to claim 1, characterized in that, The edge terminal system (3) includes a signal acquisition module, a data processing module, and a communication module: The signal acquisition module is used to acquire acoustic signature signals and laser displacement signals; The data processing module is used to segment the acoustic signature signal based on the laser displacement signal; The communication module is used to transmit the processed data to a remote server.

5. The online monitoring system for the operating status of wind turbine blades according to claim 1, characterized in that, It also includes a remote server, which is communicatively connected to the edge terminal system (3).

6. A method for online monitoring of the operating status of wind turbine blades, characterized in that, An online monitoring system for the operating status of wind turbine blades according to any one of claims 1-5 includes the following steps: Acoustic data of wind turbine blade (1) is obtained by acoustic sensor (4) of acoustic sensor assembly (2), and the moment when wind turbine blade (1) rotates to the set position is obtained by laser displacement sensor (5) installed on extension rod (6). The acoustic data is segmented based on the time difference of rotation between the blades (1) of two adjacent wind turbine units; The segmented voiceprint data is transformed in the time domain and divided into multiple frequency bands. The energy value of the voiceprint data in each frequency band is calculated. Based on the energy values ​​of the acoustic data of each frequency band, the energy deviation ratio of each frequency band of the wind turbine blade (1) is calculated; If the energy deviation ratio of any frequency band exceeds the threshold, an early warning will be issued.

7. The method for online monitoring of the operating status of wind turbine blades according to claim 6, characterized in that, The acoustic data of the wind turbine blade (1) is obtained by extending the time difference by 0.5 times forward and backward from the moment when the wind turbine blade (1) reaches the set position.

8. The method for online monitoring of the operating status of wind turbine blades according to claim 6, characterized in that, The multiple frequency bands include low-frequency band, mid-frequency band, and high-frequency band; The low-frequency band ranges from 20 to 200 Hz, the mid-frequency band ranges from 200 to 2000 Hz, and the high-frequency band ranges from 2000 Hz to 10000 Hz.

9. The method for online monitoring of the operating status of wind turbine blades according to claim 6, characterized in that, The energy deviation ratio is the ratio of the maximum to the minimum energy value within the same frequency band.

10. The method for online monitoring of the operating status of wind turbine blades according to claim 6, characterized in that, When an alert is issued, the voiceprint data and energy value data of each frequency band within the corresponding time period are saved; when no alert is issued, only the energy value data of each frequency band is saved.

Images