CN114577333B

CN114577333B - Fan tower drum vibration monitoring system based on mobile sensing and modal parameter extraction method

Info

Publication number: CN114577333B
Application number: CN202210033667.6A
Authority: CN
Inventors: 徐明强; 王树青; 杨宁; 彭潜; 田会元; 黄勇冰; 袁冶; 蒋玉峰; 林旻; 马春可
Original assignee: Ocean University of China
Current assignee: Ocean University of China
Priority date: 2022-01-12
Filing date: 2022-01-12
Publication date: 2023-04-07
Anticipated expiration: 2042-01-12
Also published as: CN114577333A

Abstract

The utility model provides a fan tower section of thick bamboo vibration monitoring system based on remove perception includes: the first carrying device is fixed on the outer wall of the tower of the target fan, and a first vibration sensor and a first optical alignment device are arranged on the first carrying device; the second carrying device can move on the outer wall of the tower of the target fan, and a second vibration sensor and a second optical alignment device are arranged on the second carrying device; and the data processing system is used for identifying the vibration mode of the target fan at least based on the vibration signal acquired by the first vibration sensor and the vibration signal acquired by the second vibration sensor to obtain the frequency, the damping ratio and the mode shape of the target vibration mode. The invention further provides a fan tower modal parameter extraction method, which can guide the second carrying device to rapidly move to the optimal monitoring position, and improve the monitoring efficiency and the reliability of modal shape prediction.

Description

Fan tower drum vibration monitoring system based on mobile sensing and modal parameter extraction method

Technical Field

The disclosure relates to the technical field of fan monitoring, in particular to a fan tower drum vibration monitoring system and method based on mobile sensing.

Background

The tower barrel part of the wind driven generator plays a role in supporting the wind turbine generator and absorbing vibration energy of the wind turbine generator. In order to realize stable and reliable fan power generation, a fan tower often supports a wind generating set at an altitude of more than 60 meters, and the manufacturing cost of the wind generating set can account for about 15 percent of the total cost. During use of a wind turbine, the wind turbine tower is often exposed to extreme wind conditions, and damage to the tower may result from large deflections and repeated stress cycles. As a support member for the unit, its damage may result in catastrophic failure of the structure. While advances have been made in the detection and health monitoring of wind turbine blades and mechanical components, little attention has been paid to the health monitoring of wind turbine towers.

Structural damage can cause changes in mechanical parameters (such as mass and rigidity) so as to change dynamic characteristics (such as modal parameters) of the system, and therefore, structural health monitoring based on the modal characteristics provides a solution for ensuring the safety and usability of the wind turbine tower. The vibration response data can be obtained by carrying out dynamic response measurement on the tower drum, and further the structural frequency, the damping ratio and the modal shape of the tower drum can be extracted from the vibration response data, and the parameters can be used for damage detection and structure identification of the wind turbine tower drum. However, in the prior art, measurement of dynamic response of a tower depends on traditional fixed sensors such as an acceleration sensor and a strain gauge, and at least the following three disadvantages exist:

1. traditional fixed sensor need install in a tower section of thick bamboo inner wall, needs installer to climb to preset position along the staircase and operates, and installation, dismantlement difficulty, the flexibility is poor.

2. In order to ensure high identification precision of modal parameters, a large number of fixed sensors need to be arranged along the height of a tower; and the fixed sensor works in a vibration environment for a long time, the looseness caused by vibration can cause the reliability to be reduced, and meanwhile, the loss rate is high, and the economical efficiency is poor.

3. The traditional fixed sensor has higher requirements on the arrangement positions of the measuring points, cannot realize the full coverage of the measuring points, and cannot ensure the evaluation precision of modal information of all points. Meanwhile, if the sensor is far away from the damage position, the probability of successful detection is obviously reduced.

Disclosure of Invention

In order to solve at least one of the above technical problems, the present disclosure provides a system and a method for monitoring vibration of a tower of a wind turbine based on mobile sensing.

According to an aspect of the present disclosure, a wind turbine tower vibration monitoring system based on mobile sensing is provided, including: the first carrying devices can be fixedly held on the outer wall of the tower of the target fan, and first vibration sensors are arranged on the first carrying devices so as to acquire vibration signals of the positions of the first carrying devices on the outer wall of the tower, wherein N1> =1; a first height acquisition device is arranged on the first carrying device to acquire height information of the first carrying device;

the second carrying devices are movably held on the outer wall of the tower of the target fan, and second vibration sensors are arranged on the second carrying devices so as to acquire vibration signals of the second carrying devices at different height positions on the outer wall of the tower, wherein N2> =1; a second height acquisition device is arranged on the second carrying device to acquire the height information of the second carrying device;

the data processing system is in communication connection with the first vibration sensor and the second vibration sensor, obtains vibration signals collected by the first vibration sensor and vibration signals collected by the second vibration sensor, the vibration signals collected by the second vibration sensor comprise vibration signals of N2 height positions on the outer wall of the tower, and identifies a target vibration mode of the target fan at least based on the vibration signals collected by the first vibration sensor and the vibration signals collected by the second vibration sensor so as to obtain the frequency, the damping ratio and the mode vibration mode of the target vibration mode;

the first carrying device and the second carrying device are arranged on the outer wall of the tower of the target fan along the same vertical direction.

According to the wind turbine tower drum vibration monitoring system based on mobile sensing, the vibration signals of N2 height positions of the outer wall of the tower drum, which are acquired by the second vibration sensor, are the vibration signals of N2 height positions covering the outer wall of the whole tower drum.

According to the wind turbine tower vibration monitoring system based on mobile sensing, the vibration sensor comprises one or more of an acceleration sensor, a displacement sensor and a strain gauge.

According to the wind turbine tower vibration monitoring system based on mobile sensing, a first optical alignment device is arranged on a first carrying device, a second optical alignment device is arranged on a second carrying device, and before a first vibration sensor and a second vibration sensor collect vibration signals, the first carrying device and the second carrying device are optically aligned based on the first optical alignment device and the second optical alignment device, so that the first carrying device and the second carrying device are arranged on the outer wall of the tower of the target wind turbine along the same vertical direction.

According to the wind turbine tower vibration monitoring system based on movement perception, the first optical alignment device comprises a light emitter, a light receiver and a light reflector, and the second optical alignment device comprises a light emitter, a light receiver and a light reflector.

According to the wind turbine tower vibration monitoring system based on mobile sensing, the first carrying device and the second carrying device are both carrying equipment based on vacuum adsorption.

According to the wind turbine tower vibration monitoring system based on mobile sensing of at least one embodiment of the present disclosure, the first height acquisition device and the second height acquisition device are both GPS devices or beidou devices.

According to the wind turbine tower vibration monitoring system based on mobile sensing, the data processing system is an electronic device comprising a processor and a memory.

According to another aspect of the disclosure, a method for extracting modal parameters of a wind turbine tower based on any one of the above systems for monitoring vibration of a wind turbine tower based on mobile sensing is provided, which includes:

s102, keeping the N1 first carrying devices and the N2 second carrying devices on the outer wall of the tower of the target fan, wherein the first carrying devices are fixedly kept on the outer wall of the tower of the target fan, and the second carrying devices are movably kept on the outer wall of the tower of the target fan;

s104, optically aligning the first carrying device and the second carrying device so that the first carrying device and the second carrying device are arranged on the outer wall of the tower of the target fan along the same vertical direction;

s106, calibrating N measuring positions on the outer wall of the tower, fixedly keeping the first carrying device at N1 measuring positions, moving the second carrying device to N2 measuring positions, starting a vibration test, and collecting vibration signals at the N1+ N2 measuring positions; wherein N is much greater than N1+ N2;

and S108, obtaining the vibration frequency, the damping ratio and the mode shape of the target mode at least based on the vibration signals at the N1+ N2 positions.

According to the method for extracting the modal parameters of the wind turbine tower, the vibration frequency, the damping ratio and the modal shape of the target modal are obtained based on a modal identification method.

According to the method for extracting the modal parameters of the wind turbine tower, the modal identification method comprises one of a random subspace method, a feature system implementation algorithm and the like.

According to at least one embodiment of the present disclosure, the method for extracting modal parameters of a wind turbine tower further includes:

and S110, estimating the modal parameters of the obtained N1+ N2 measuring positions by using Gaussian process regression to obtain the vibration frequency, the damping ratio and the mean value and the confidence interval of the modal shapes of the N positions, and repeating the steps S106 to S108 on the modal shapes of which the confidence interval width is larger than the preset threshold value until the confidence interval widths of the modal shapes of the N measuring positions are smaller than or equal to the preset threshold value to obtain the final modal shape of the target fan.

According to the method for extracting the modal parameters of the wind turbine tower, step S110 includes:

s1102, obtaining the modal shape of the N1+ N2 measurement positions

And height information of N1+ N2 measuring positions>

Writing to a Modal-height information database ({ X) ⁽ⁱ⁾ ,Y ⁽ⁱ⁾ }); wherein, the first and the second end of the pipe are connected with each other,

write X ⁽ⁱ⁾ ，/>

Writing Y ⁽ⁱ⁾ ；

S1104, based on the mode shape-height information database ({ X) ⁽ⁱ⁾ ,Y ⁽ⁱ⁾ }) using Gaussian process regression on the N measurement positions (h) of the tower outer wall _1:N ) Mode shape of

Making a prediction in order to obtain a mean value of the modal vibrations in all measurement positions>

And a predicted confidence interval>

The confidence interval represents the uncertainty of prediction of all positions to be detected; wherein superscript (i) represents the ith measurement;

s1106, judging the confidence interval of the prediction

Whether the widths of the two are all less than or equal to a preset threshold value;

if yes, outputting the average value of the modal shape of all the measurement positions

A final mode shape as the target mode;

otherwise, go to step S1108;

s1108, predicting confidence intervals of all the second carrying devices (N2) to the step S1104

Moving the first N2 position points with the maximum width, namely moving to N2 new measurement positions, collecting vibration signals and height information again, acquiring the vibration frequency, the damping ratio and the mode shape of the target fan, executing the steps S1102 to S1106 on the basis of the mode shape of each new measurement position and the height information of each new measurement position which are acquired again until the widths of the predicted N measurement position confidence intervals are smaller than or equal to a preset threshold value, and outputting the mean value of all the measurement position mode shapes +>

As the final mode shape.

According to at least one embodiment of the present disclosure, the method for extracting modal parameters of a wind turbine tower is characterized in that step S112 further includes: and averaging the vibration frequency and the damping ratio obtained in the step S110 to obtain the final vibration frequency and the damping ratio of the target fan.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.

FIG. 1 is a schematic configuration diagram of a wind turbine tower vibration monitoring system based on motion sensing, according to one embodiment of the present disclosure.

FIG. 2 is a schematic structural diagram of a mobile sensor configuration of a wind turbine tower vibration monitoring system based on motion sensing according to one embodiment of the present disclosure.

FIG. 3 shows a first-order modal shape estimated by the method for extracting modal parameters of a wind turbine tower according to one embodiment of the present disclosure.

FIG. 4 shows the first-order mode shape estimated based on the sensor-all-fixing method (the sensor is fixed to 2, 3, 4, 5 tower barrels).

FIG. 5 shows the first order mode shape estimated based on the sensor full fixation method (sensor fixed to 1, 2, 3, 5 tower).

FIG. 6 is a schematic flow chart diagram of a method for extracting modal parameters of a wind turbine tower according to an embodiment of the present disclosure.

Description of the reference numerals

1. Track

2. Tower drum

3. Outer platform

11. Light path

51. A first carrying device

52. Second carrying device

60. Data processing system/data acquisition instrument

70. Head optical alignment module

80. Tail optical alignment module

91. First vibration sensor

92. Second vibration sensor

510. First height acquisition device

520. A second height obtaining device.

Detailed Description

The present disclosure will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the present disclosure are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. Technical solutions of the present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Unless otherwise indicated, the illustrated exemplary embodiments/examples are to be understood as providing exemplary features of various details of some ways in which the technical concepts of the present disclosure may be practiced. Accordingly, unless otherwise indicated, features of the various embodiments may be additionally combined, separated, interchanged, and/or rearranged without departing from the technical concept of the present disclosure.

The use of cross-hatching and/or shading in the drawings is generally used to clarify the boundaries between adjacent components. As such, unless otherwise noted, the presence or absence of cross-hatching or shading does not convey or indicate any preference or requirement for a particular material, material property, size, proportion, commonality between the illustrated components and/or any other characteristic, attribute, property, etc., of a component. Further, in the drawings, the size and relative sizes of components may be exaggerated for clarity and/or descriptive purposes. While example embodiments may be practiced differently, the specific process sequence may be performed in a different order than that described. For example, two processes described consecutively may be performed substantially simultaneously or in reverse order to that described. In addition, like reference numerals denote like parts.

When an element is referred to as being "on" or "on," "connected to" or "coupled to" another element, it can be directly on, connected or coupled to the other element or intervening elements may be present. However, when an element is referred to as being "directly on," "directly connected to" or "directly coupled to" another element, there are no intervening elements present. For purposes of this disclosure, the term "connected" may refer to physically, electrically, etc., and may or may not have intermediate components.

For descriptive purposes, the present disclosure may use spatially relative terms such as "below … …," below … …, "" below … …, "" below, "" above … …, "" above, "" … …, "" upper "and" side (e.g., in "sidewall") to describe the relationship of one component to another (other) component as shown in the figures. Spatially relative terms are intended to encompass different orientations of the device in use, operation, and/or manufacture in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" the other elements or features. Thus, the exemplary term "below … …" can encompass both an orientation of "above" and "below". Further, the devices may be otherwise positioned (e.g., rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, when the terms "comprises" and/or "comprising" and variations thereof are used in this specification, the presence of stated features, integers, steps, operations, elements, components and/or groups thereof are stated but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof. It is also noted that, as used herein, the terms "substantially," "about," and other similar terms are used as approximate terms and not as degree terms, and as such, are used to interpret inherent deviations in measured values, calculated values, and/or provided values that would be recognized by one of ordinary skill in the art.

The wind turbine tower vibration monitoring system and method based on motion sensing of the present disclosure are described in detail below with reference to fig. 1 to 6.

Referring first to fig. 1-2, a wind turbine tower vibration monitoring system based on motion sensing according to one embodiment of the present disclosure includes:

the first carrying devices 51 are capable of being fixedly held on the outer wall of the tower of the target fan, the first carrying devices 51 are provided with first vibration sensors 91 so as to acquire vibration signals of the positions of the first carrying devices 51 on the outer wall of the tower, wherein N1> =1; the first carrier 51 is provided with a first height obtaining device 510 to obtain the height information of the first carrier 51;

the number of the second carrying devices 52 is N2, the second carrying devices 52 are movably held on the outer wall of the tower of the target wind turbine, and the second carrying devices 52 are provided with second vibration sensors 92 to acquire vibration signals of the second carrying devices 52 at different height positions on the outer wall of the tower, where N2> =1; the second carrier 52 is provided with a second height obtaining device 520 to obtain the height information of the second carrier 52;

the data processing system 60 is in communication connection with the first vibration sensor 91 and the second vibration sensor 92, and acquires vibration signals acquired by the first vibration sensor 91 and vibration signals acquired by the second vibration sensor 92, the vibration signals acquired by the second vibration sensor 92 include vibration signals of N2 height positions on the outer wall of the tower, and the data processing system 60 identifies vibration modal parameters of the target fan at least based on the vibration signals acquired by the first vibration sensor 91 and the vibration signals acquired by the second vibration sensor 92 so as to acquire the frequency, the damping ratio and the modal shape of the target fan;

the first carrier 51 and the second carrier 52 are arranged in the same vertical direction on the outer wall of the tower of the target wind turbine.

According to a preferred embodiment of the present disclosure, the first and

second carriers

51 and 52 are vacuum adsorption-based carrier devices.

Wherein, carrying device can be dolly or wall climbing robot, and it can rely on the vacuum negative pressure principle to adsorb on tower section of thick bamboo 2 to can follow the outer wall free movement of tower section of thick bamboo. The wireless sensing technology is preferably adopted in the present disclosure, a double-shaft wireless vibration sensor is installed in the trolley, the double-shaft wireless vibration sensor can be in communication connection with a data processing system (data acquisition instrument) through a ZigBee protocol, and the data acquisition instrument 60 is placed in a fan with good coverage of wireless signals or on the outer platform 3.

According to the wind turbine tower vibration monitoring system based on mobile sensing in the preferred embodiment of the present disclosure, the vibration signals of N2 height positions of the tower outer wall acquired by the second vibration sensor 92 are the vibration signals of N2 height positions of the tower outer wall covering the tower height.

More preferably, a first optical alignment device is disposed on the first carrier 51, a second optical alignment device is disposed on the second carrier 52, and before the first vibration sensor 91 and the second vibration sensor 92 collect vibration signals, the first carrier 51 and the second carrier 52 are optically aligned based on the first optical alignment device and the second optical alignment device, so as to ensure that the first carrier 51 and the second carrier 52 are aligned in the same vertical direction on the outer wall of the tower of the target fan.

In the present disclosure, it is preferable to use N1+ N2 trolleys, where N1 trolley is used as a reference vehicle (a first carrying device, named as a fixed trolley), and the other N2 trolleys (a second carrying device, named as a moving trolley) are arranged in a straight line with the fixed trolley from top to bottom, and all the trolleys can freely move along a predetermined straight track on the tower.

In the present disclosure, the predetermined track 1 is defined as the windward side of the fan to ensure that the trolley is well fixed to the surface of the tower and to reduce the vibration interference caused by vortex shedding due to wind.

For the wind turbine tower vibration monitoring system based on motion sensing of the above embodiments, it is preferable that the number of the second carrying devices 52 is two or more, the first carrying device 51 and the second carrying device 52 adjacent to the first carrying device 51 are optically aligned based on the first optical alignment device and the second optical alignment device, and two second carrying devices 52 adjacent to each other are optically aligned based on the second optical alignment device.

According to the wind turbine tower vibration monitoring system based on movement perception of the vibration sensor in the preferred embodiment of the present disclosure, the first optical alignment device comprises a light emitter, a light receiver and a light reflector, and the second optical alignment device comprises a light emitter, a light receiver and a light reflector;

preferably, for each cart, there is included a head optical alignment module 70, which head optical alignment module 70 includes an optical transmitter and an optical receiver, and a tail optical alignment module 80, which tail optical alignment module includes an optical receiver and an optical reflector.

The first optical alignment device and the second optical alignment device each include a head optical alignment module 70 and a tail optical alignment module 80.

The light emitter at the head of the rear trolley (namely the upper trolley in figure 2) emits a light beam to the front trolley (namely the lower trolley in figure 2), the light receiver at the tail of the front trolley senses the light beam and reflects the light beam backwards through the light reflector at the tail, and the light receiver at the head of the rear trolley receives the reflected light beam to form a light path 11, which indicates that the light emitter and the light receiver are in the same straight line, so that further testing can be carried out.

Preferably, the first height acquiring device 510 and the second height acquiring device 520 described above are both GPS devices or beidou devices.

In the testing process, a target mode is selected, the fixed vehicle is fixed at N1 positions where antinodes of the target mode are located, for example, the first-order and second-order front-back vibration modes are tested, the fixed vehicle is respectively parked at the top end of the fan tower cylinder and at the height of the 2/3 tower cylinder, and the maximum testing signal-to-noise ratio is guaranteed. And then, sequentially adjusting the positions of the moving vehicles along the straight line of the track, and verifying whether the vehicles meet the requirement of following the same straight line through the light receivers of the front vehicle and the rear vehicle when the positions of the moving vehicles are adjusted to the preset height. If the conditions are met, each vehicle stops moving, the height of each vehicle is recorded by using a height acquisition device (preferably a GPS device) installed in the vehicle, and a vibration test is started. And after the test is finished, adjusting the position of the movable vehicle again, and repeating the process until all height positions of the tower drum are completely covered.

And for the data measured each time, using the vibration signals collected by the sensors on the fixed vehicle as reference points, and performing modal identification by adopting a modal identification method to obtain the vibration frequency, the damping ratio and the modal shape. And normalizing the mode shape according to the reference point and storing the normalized mode shape into a measurement database.

And estimating the data of the N measuring positions by adopting a Gaussian process based on the data of the measuring database to obtain the mean value and the confidence interval of the N measuring positions. And then, moving the N2 mobile vehicles to N2 typical positions with the largest uncertainty, repeating the test process until the uncertainty of each position meets the requirement, and terminating the measurement. And normalizing the modal shape estimated from all the test data again to obtain the final modal shape. And averaging all the frequencies and damping ratios obtained by testing to obtain the target frequency and damping ratio.

And replacing the target modes, and repeating the process to obtain the vibration frequency, the damping ratio and the vibration mode of all the target modes.

For the wind turbine tower vibration monitoring system based on motion sensing of each embodiment described above, preferably, the data processing system is an electronic device including a processor and a memory. The data processing system also includes a data acquisition device (e.g., a data acquisition circuit).

The utility model discloses a fan tower section of thick bamboo vibration monitoring system based on remove perception need not to preassemble the sensor in a tower section of thick bamboo inner wall based on portable wireless sensing technique, can arrange in a flexible way at a tower section of thick bamboo outer wall, does not receive the influence of a tower section of thick bamboo height, and predetermined straight line track free motion can be followed to the dolly of carrying on the sensor, convenient to use, and the flexibility is high. The utility model discloses a fan tower section of thick bamboo vibration monitoring system based on remove perception utilizes less mobile vehicle of carrying the vibration sensor to measure to dolly and sensor can reuse in different fans. The fan tower drum vibration monitoring system based on mobile sensing is based on a mobile wireless sensing technology, the problem of selecting measuring points is not needed to be worried about, the measuring points can be guaranteed to be fully covered by moving a vehicle, and vibration mode information of all positions can be directly obtained. And can be flexibly arranged according to the target modes, and all the target modes can be identified most effectively. The fan tower drum vibration monitoring system based on mobile sensing is based on a mobile wireless sensing technology, an optimized selection scheme of the moving position of a sensor in the measuring process is provided, uncertainty of modal shape estimation of each measuring point can be gradually reduced, blindness of movement of the sensor is reduced, and the measuring process is accelerated.

Referring to fig. 6, a method S100 for extracting modal parameters of a wind turbine tower according to this embodiment includes:

s102, keeping the N1 first carrying devices 51 and the N2 second carrying devices 52 on the outer tower wall of the target fan, wherein the first carrying devices 51 are fixedly kept on the outer tower wall of the target fan, and the second carrying devices 52 are movably kept on the outer tower wall of the target fan;

s104, optically aligning the first carrying device 51 and the second carrying device so that the first carrying device 51 and the second carrying device 52 are arranged on the outer wall of the tower of the target fan along the same vertical direction;

s106, calibrating N measuring positions on the outer wall of the tower, fixedly keeping the first carrying device 51 at N1 measuring positions, moving the second carrying device 52 to N2 measuring positions, starting a vibration test, and collecting vibration signals at the N1+ N2 measuring positions;

and S108, obtaining the vibration frequency, the damping ratio and the mode shape of the target mode at least based on the vibration signals at the N1+ N2 measurement positions.

For the wind turbine tower modal parameter extraction method of the above embodiment, preferably, the vibration frequency, the damping ratio and the modal shape of the target mode are obtained based on a random subspace method.

For the method for extracting modal parameters of a wind turbine tower according to the above embodiment, preferably, the method further includes:

and S110, estimating the modal shape of the N measuring positions by using Gaussian process regression to obtain confidence intervals of the modal shape of the N positions, and repeating the steps S106 to S108 (adjusting the N measuring positions) on the modal shape corresponding to the confidence interval with the width larger than the preset threshold until the width of the confidence interval of the modal shape of the N measuring positions is smaller than or equal to the preset threshold to obtain the final modal shape of the target fan.

According to the method for extracting the modal parameters of the wind turbine tower in the preferred embodiment of the present disclosure, step S110 includes:

s1102, at the i (i) th>= 1) measurements, the mode shapes of all acquired N1+ N2 measurement positions are measured

And height information of N1+ N2 measurement positions->

Writing to a Modal mode-height information database { X ⁽ⁱ⁾ ,Y ⁽ⁱ⁾ }; wherein it is present>

Write X ⁽ⁱ⁾ ，/>

Writing Y ⁽ⁱ⁾ ；

S1104, based on the mode shape-height information database { X ⁽ⁱ⁾ ,Y ⁽ⁱ⁾ Using Gaussian process regression for all N measurement positions (h) of the tower outer wall _1:N ) Mode of vibration

Predicting to obtain the average value of modal vibration modes of all measurement positions

And a predicted confidence interval>

The confidence interval represents the uncertainty of prediction of all positions to be measured; wherein the superscript (i) denotesMeasuring for i times;

s1106, judging the predicted confidence interval

A final mode shape as a target mode;

otherwise, go to step S1108;

s1108, all the second carrying devices (N2) are moved to the confidence interval predicted in the step S1104

Moving the first N2 position points with the largest width, namely moving to a new preset position, acquiring vibration signals and height information again, acquiring the vibration frequency, the damping ratio and the modal shape of the target modal, executing the steps S1102 to S1106 based on the re-acquired modal shape of each new preset position and the height information of each new preset position until the widths of the predicted confidence intervals of the N measurement positions are all smaller than or equal to a preset threshold value, and outputting the average value of the modal shapes of all the measurement positions as the final modal shape of the target modal.

Illustratively, four mobile sensors, two stationary and two mobile, are first mounted on the fan. After the sensors reach the target position and align to a straight line, data acquisition is started, modal identification is carried out, and modal vibration modes of four measurement positions are obtained

And height information obtained from GPS>

Wherein, the lower corner mark 1:4 denotes four measurement points and the upper corner (1) denotes the first measurement.

Writing the modal shape and height information of the four points into a database { X } ⁽¹⁾ ,Y ⁽¹⁾ And (c) the step of (c) in which,

write X ⁽¹⁾ ，/>

Write Y ⁽¹⁾ . Using it as known data, using Gaussian process regression for all N measurement locations h _1:N Mode vibration type->

Predicting to obtain the mean value of vibration pattern>

And a predicted confidence interval->

The confidence interval represents the uncertainty of the prediction of the N measurement locations.

In order to reduce the uncertainty of the prediction and to increase the confidence of the modality recognition, in the next measurement, for example i +1, N2 moving sensors are moved towards the confidence interval obtained in the ith measurement

The first N2 position points with the greatest width are moved and again measured, mode-recognized and height-detected, and a decision is made as to whether or not a position is present>

And &>

Will be provided with

And &>

Also written into database { X ⁽ⁱ⁺¹⁾ ,Y ⁽ⁱ⁺¹⁾ The database here contains all the previous measurements, i.e.:

performing Gaussian process regression again to predict the average value of the vibration mode

And a predicted confidence interval->

If it is

Is smaller than a given width threshold value, the movement is stopped and an output->

In order to obtain the final mode shape,

and (4) taking the corresponding confidence interval, and otherwise, continuing the Gaussian regression process.

Preferably, step S112 further includes: and averaging the vibration frequency and the damping ratio obtained in the step S110 to obtain the final vibration frequency and the damping ratio of the target fan.

The following describes a wind turbine tower vibration monitoring system and method based on motion sensing according to the present disclosure with specific examples.

The example is a fan model with the rated power of 5.5MW, the whole machine of the model is 125000kg, the impeller diameter is 170m, the tower height is 102.3m, and the tower comprises 5 sections with the elevations from bottom to top being 16.3m,36.3m,56.3m,78.8m and 102.3m respectively.

Modal analysis is carried out on the natural frequency to obtain the first-order natural frequency of the natural frequency to be 0.211Hz. At the same time, it is analyzed for transient state and setThe wind load is the main load, the influence of turbulent wind on the dynamic response of the wind turbine is considered, the wind model is a Kaimal model, the working condition average wind speed is 15m/s, the turbulent intensity is 0.132, and the standard air density is 1.225kg/m ³ And the positive air is blown to the impeller to cause the blades to rotate to generate electricity. In the simulation process, response of the fan is calculated by adopting a phyllotactic theory, the integral step length is set to be 0.05s, and the total time length is 600s. In order to pick up tower response conveniently, a measuring position is marked on the tower every 2.5m from bottom to top (the last interval is 2.3 m), 41 measuring points coexist, acceleration in two directions (x and y) in the plane of the measuring points is picked up for analysis, and a random subspace method is adopted for modal parameter identification. The signal from each test is added with 30dB of gaussian noise to account for the effect of uncertainty.

In the embodiment, 4 acceleration sensors are used for monitoring the tower barrel, when a fixed sensor is used, each sensor is arranged at a mark position closest to the top end of each section of the tower barrel, and the first scheme is that no sensor is arranged at the position of the bottommost layer, namely measuring point positions 35m,55m,77.5m and 102.3m; and in the second scheme, no sensor is arranged at the position of the tower of the fourth section, namely measuring point positions 15m,35m,55m and 102.3m.

In the method, a first-order mode is used as a target mode, a trolley with the top end of 102.3m is fixed, the maximum test signal-to-noise ratio is guaranteed, meanwhile, in order to prevent the sensor from being excessively concentrated, the trolley with the position point of 55m is also fixed, other two trolleys continuously move to the position with the maximum confidence interval on a tower, the measured mode shape is used as known data, and the mode shape mean value and the confidence interval of all 41 measuring points are predicted by utilizing Gaussian process regression.

After 14 times of sensor movement, the width of the confidence interval of each measurement position is less than 2.5% of the threshold value, and the first-order vibration mode obtained and identified is shown in fig. 3. Therefore, the estimated vibration mode basically accords with the real structural vibration mode, and the uncertainty intervals of all the measuring points can be reduced to a very small range through few sensor movements.

When a stationary sensor is used, 14 repeated tests are also performed for comparison with the results of the method of the present disclosure, and the modal shape obtained from the 14 tests is averaged as known data to predict the modal shape mean and confidence interval of 41 test points by using gaussian process regression.

The results obtained for protocol one are shown in figure 4. As can be seen by comparing fig. 3 and 4, the results of the mobile and stationary evaluations are very similar, indicating that the mobile can achieve a precision consistent with the stationary. However, the mobile type has the advantage that the confidence interval of the untested points is significantly reduced, indicating an improved reliability of the estimation. Meanwhile, the confidence interval can provide prior information for predicting the change range of the vibration mode of the position. Meanwhile, the mobile arrangement is higher in convenience and repeatability, one set of device can be repeatedly applied to monitoring of multiple fans, and the cost is obviously reduced.

The results obtained for protocol two are shown in figure 5. Comparing fig. 3 to 5, it can be seen that the accuracy of the stationary test is much lower than that of the mobile test, which proves that the stationary sensor has a high requirement for the arrangement position, and when the target mode is adapted, a better result can be obtained, otherwise. Meanwhile, the method (fixed type) can be arranged only for a few or single target modes, and when the target modes are more and the available sensors cannot meet the test requirements of all the modes at the same time, the test precision is lower. The disclosed method of using a mobile sensor overcomes this drawback.

In the description of the present specification, reference to the description of "one embodiment/mode", "some embodiments/modes", "example", "specific example" or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/modes or examples and features of the various embodiments/modes or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.

It will be understood by those skilled in the art that the foregoing embodiments are merely for clarity of illustration of the disclosure and are not intended to limit the scope of the disclosure. Other variations or modifications may occur to those skilled in the art, based on the foregoing disclosure, and are still within the scope of the present disclosure.

Claims

1. The utility model provides a fan tower section of thick bamboo vibration monitoring system based on remove perception which characterized in that includes:

the first carrying devices can be fixedly kept on the outer wall of the tower of the target fan, first vibration sensors are arranged on the first carrying devices to acquire vibration signals of the first carrying devices at the positions of the outer wall of the tower, wherein N1> =1; the first carrying device is provided with a first height acquisition device so as to acquire height information of the first carrying device;

the second carrying devices are movably held on the outer wall of the tower of the target fan, and second vibration sensors are arranged on the second carrying devices so as to acquire vibration signals of the second carrying devices at different height positions on the outer wall of the tower, wherein N2> =1; the second carrying device is provided with a second height acquisition device so as to acquire the height information of the second carrying device; and

the first carrying device and the second carrying device are arranged on the outer wall of the tower of the target fan along the same vertical direction;

the monitoring system extracts the modal parameters of the tower of the target fan based on the following steps:

s106, vertically calibrating N measuring positions on the outer wall of the tower, fixedly keeping the first carrying device at N1 measuring positions, moving the second carrying device to N2 measuring positions, starting a vibration test, and collecting vibration signals at the N1+ N2 measuring positions, wherein N is far greater than N1+ N2;

s108, obtaining the vibration frequency, the damping ratio and the mode shape of the target vibration mode at least based on the vibration signals at the N1+ N2 positions; and

s110, estimating the modal shapes of the N measuring positions by using Gaussian process regression to obtain a mean value and confidence intervals of the modal shapes of the N measuring positions, and repeating the steps S106 to S108 on the modal shapes of which the confidence interval widths are larger than a preset threshold position until the confidence interval widths of the modal shapes of the N measuring positions are smaller than or equal to a preset threshold value so as to obtain the final modal shape of the target fan;

wherein, step S110 includes:

s1102, writing the acquired modal shapes of the N1+ N2 measuring positions and the height information of the N1+ N2 measuring positions into a modal shape-height information database;

s1104, predicting the modal shapes of the N measurement positions on the outer wall of the tower barrel by using Gaussian process regression based on the modal shape-height information database to obtain the mean value and confidence interval of the modal shapes of the N measurement positions;

s1106, judging whether the widths of the predicted confidence intervals are all smaller than or equal to a preset threshold value;

if so, outputting the average value of the modal shape of the N measuring positions as the final modal shape of the target vibration mode;

otherwise, go to step S1108;

and S1108, moving all the second carrying devices to the first N2 position points with the maximum confidence interval width predicted in the step S1104, acquiring vibration signals and height information again, acquiring vibration frequency, damping ratio and modal shape, executing the steps S1102 to S1106 on the basis of the modal shape and the height information of the N1+ N2 measurement positions acquired again until the widths of the confidence intervals of the N measurement positions are smaller than or equal to a preset threshold value, and outputting the average value of the modal shapes of the N measurement positions as the final modal shape of the target vibration mode.

2. The system for monitoring the vibration of the wind turbine tower based on the mobile sensing of claim 1, wherein the vibration signals of N2 height positions of the outer wall of the tower collected by the second vibration sensor are vibration signals of N2 height positions covering the entire outer wall of the tower.

3. The wind turbine tower vibration monitoring system based on mobile sensing of claim 1, wherein a first optical alignment device is disposed on the first carrier device, a second optical alignment device is disposed on the second carrier device, and the first carrier device and the second carrier device are optically aligned based on the first optical alignment device and the second optical alignment device before the first vibration sensor and the second vibration sensor collect vibration signals, so as to ensure that the first carrier device and the second carrier device are arranged in the same vertical direction on the outer wall of the tower of the target wind turbine.

4. The wind turbine tower vibration monitoring system based on movement perception according to claim 3, wherein the first optical alignment device includes a light emitter, a light receiver, and a light reflector, and the second optical alignment device includes a light emitter, a light receiver, and a light reflector.

5. The wind turbine tower vibration monitoring system based on movement perception according to claim 4, wherein the first carrying device and the second carrying device are both vacuum adsorption based carrying devices.

6. The wind turbine tower vibration monitoring system based on movement perception according to claim 5, wherein the vibration frequency, the damping ratio and the mode shape of the target vibration mode are obtained based on a mode identification method.

7. The system for monitoring vibration of a wind turbine tower based on mobile sensing of claim 1, wherein step S112 further comprises: and averaging the vibration frequency and the damping ratio obtained in the step S110 to obtain the final vibration frequency and the damping ratio of the target fan.