A wind turbine tower health status online monitoring system and method

Abstract

The present invention discloses an online monitoring system and method for the health status of a wind turbine tower, the system comprising: a plurality of MEMS sensors installed on the wind turbine tower and a remote background server, the plurality of MEMS sensors being on the same longitudinal straight line; the plurality of MEMS sensors being used to collect first data of the wind turbine tower in a carrier coordinate system and transmit the first data to the background server; the background server being used to periodically receive the first data collected by each MEMS sensor, and to calculate the second data in a geodetic coordinate system based on the first data, and to judge the health status of the wind turbine tower based on the second data. The technical solution in the embodiment of the present invention can extract characteristic parameters that truly reflect the health status of the wind turbine tower by analyzing the data collected by the MEMS sensor, and effectively monitor and control the health status of the wind turbine tower around the clock.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of wind turbine tower detection, and in particular to a system and method for online monitoring the health status of a wind turbine tower. Background Art

[0002] As the main form of development and utilization of new energy, wind power is being widely developed and utilized worldwide. With the rapid development of wind power generation technology, the scale of wind turbines has gradually increased, and the height of towers has also been increasing. The tower itself bears complex and variable loads (thrust, bending moment, torque, etc.), and at the same time, affected by meteorological and other conditions, the tower will have a certain degree of swing and distortion. Excessive swing will lead to increased structural deformation and create safety hazards. The problem of tower shaking has begun to receive attention.

[0003] At present, the main ways to realize the tower inclination and deformation measurement are as follows: Option 1: Observe the tower inclination and settlement on site through a level and theodolite; Option 2: Install a tilt sensor and an acceleration sensor in the wind turbine top cabin; Option 3: Install a GPS / Beidou receiver in the wind turbine top cabin to measure the displacement of the tower top and convert it into the inclination angle of the tower top.

[0004] However, Plan 1 cannot achieve effective monitoring and control of wind turbine towers in all directions, throughout the entire process, and 24 hours a day, 7 days a week; Plan 2 regards the wind turbine tower as a straight pole, and the results of the sway analysis will deviate greatly from the actual situation; Plan 3 cannot meet the millimeter-level tilt and deformation monitoring of wind turbine towers, and the Beidou receiver is subject to construction restrictions and must be installed on the outer wall of the tower.

[0005] Therefore, how to extract characteristic parameters that truly reflect the health status of wind turbine towers and conduct effective all-weather monitoring and control of the health status of wind turbine towers is a technical problem that needs to be solved urgently. Summary of the invention

[0006] The present invention aims to solve one of the problems in the related art to at least a certain extent. The embodiment of the present invention provides a system and method for online monitoring of the health status of a wind turbine tower, so as to achieve accurate detection of the wind turbine tower around the clock.

[0007] In a first aspect, an embodiment of the present invention provides an online monitoring system for the health status of a wind turbine tower, comprising: a plurality of MEMS sensors installed on the wind turbine tower and a remote background server, wherein the plurality of MEMS sensors are located on the same longitudinal straight line;

[0008] The multiple MEMS sensors are used to collect first data of the wind turbine tower in the carrier coordinate system and transmit the first data to the background server;

[0009] The background server is used to periodically receive the first data collected by each MEMS sensor, calculate the second data in the geodetic coordinate system according to the first data, and determine the health status of the wind turbine tower according to the second data.

[0010] Furthermore, the first data includes: angular velocity data Gn, first acceleration data An, and magnetic data Mn of the nth MEMS sensor.

[0011] Furthermore, the second data includes: Euler angle data En of the nth MEMS sensor, second acceleration data ACCn, shaking distance data Sn, shaking axis angle data θ n ;

[0012] The second data also includes: an installation height difference Hn and a horizontal relative displacement value dn between the nth MEMS sensor and the n+1th MEMS sensor;

[0013] Wherein, the nth MEMS sensor is located above the n+1th MEMS sensor, and,

[0014] dn=Hn*(1-cos(θ n -θ n+1 )) / (θ n -θ n+1 ).

[0015] Furthermore, the backend server calculates the second data in the geodetic coordinate system according to the first data, including:

[0016] The backend server obtains the gravity direction according to the first acceleration data An;

[0017] The backend server obtains the north-south direction according to the magnetic data Mn;

[0018] The backend server constructs a geodetic coordinate system according to the gravity direction and the north-south direction;

[0019] The backend server performs data fusion on the angular velocity data Gn, the first acceleration data An, and the magnetic data Mn to obtain Euler angle data En and second acceleration data ACCn;

[0020] The backend server performs an integration operation on the second acceleration data ACCn to obtain the shaking distance data Sn;

[0021] The backend server performs a least squares operation on the Euler angle data En and the second acceleration data ACCn to obtain the shaking axis angle data θ n .

[0022] Furthermore, the backend server determines the health status of the wind turbine tower according to the second data, including:

[0023] Determine in real time whether the second acceleration ACCn exceeds the preset acceleration threshold. If so, when the second acceleration ACCn exceeds the preset acceleration threshold within a preset continuous time, and / or when the shaking distance data Sn exceeds the preset shaking distance threshold within a preset continuous time, a warning display is performed;

[0024] If it does not exist, the backend server obtains the verticality P according to the installation height difference Hn and the horizontal relative displacement value dn; according to the second acceleration data ACCn and the shaking axis angle data θ n , and obtain the central angle α; according to the central angle α and the initial central angle α0, the inclination rate tan(α-α0) of uneven settlement is obtained;

[0025] When the verticality P exceeds the preset verticality threshold, and / or, when the inclination rate tan(α-α0) of the uneven settlement exceeds the preset inclination rate threshold, and / or, when the shaking axis angle data θ n When the preset shaking axis angle threshold is exceeded, an alarm will be displayed.

[0026] In a second aspect, an embodiment of the present invention provides a method for online monitoring of the health status of a wind turbine tower, comprising:

[0027] A plurality of MEMS sensors collect first data of the wind turbine tower in a carrier coordinate system, and transmit the first data to a background server;

[0028] The backend server calculates second data in a geodetic coordinate system based on the first data, and determines a health status of the wind turbine tower based on the second data.

[0029] Furthermore, the first data includes: angular velocity data Gn, first acceleration data An, and magnetic data Mn of the nth MEMS sensor.

[0030] Furthermore, the second data includes: Euler angle data En of the nth MEMS sensor, second acceleration data ACCn, shaking distance data Sn, shaking axis angle data θ n ;

[0031] The second data also includes: an installation height difference Hn and a horizontal relative displacement value dn between the nth MEMS sensor and the n+1th MEMS sensor;

[0032] Wherein, the nth MEMS sensor is located above the n+1th MEMS sensor, and,

[0033] dn=Hn*(1-cos(θ n -θn+1 )) / (θ n -θ n+1 ).

[0034] Further, the backend server calculates second data in a geodetic coordinate system according to the first data, and determines the health status of the wind turbine tower according to the second data, including:

[0035] S1: Obtain the gravity direction according to the first acceleration data An; obtain the north-south direction according to the magnetic data Mn; and construct a geodetic coordinate system according to the gravity direction and the north-south direction;

[0036] S2: fusing the angular velocity data Gn, the first acceleration data An, and the magnetic data Mn to obtain the Euler angle En and the second acceleration data ACCn;

[0037] S3: Determine in real time whether the second acceleration ACCn exceeds the preset acceleration threshold. If the second acceleration ACCn exceeds the preset acceleration threshold, execute S41-S42;

[0038] S41: performing an integration operation on the second acceleration data ACCn to obtain shaking distance data Sn;

[0039] S42: When the second acceleration ACCn exceeds the preset acceleration threshold within the preset continuous time, and / or when the shaking distance data Sn exceeds the preset shaking distance threshold within the preset continuous time, a warning display is performed.

[0040] Further, S3: determining in real time whether the second acceleration ACCn exceeds the preset acceleration threshold, if the second acceleration ACCn does not exceed the preset acceleration threshold, executing S51-S54;

[0041] S51: Perform least squares calculation on the Euler angle data En and the second acceleration data ACCn to obtain the shaking axis angle data θ n ;

[0042] S52: Obtain verticality P according to the installation height difference Hn and the horizontal relative displacement value dn;

[0043] S53: Based on the second acceleration data ACCn and the shaking axis angle data θ n , and obtain the central angle α; according to the central angle α and the initial central angle α0, the inclination rate tan(α-α0) of uneven settlement is obtained;

[0044] S54: When the verticality P exceeds the preset verticality threshold, and / or when the inclination rate tan(α-α0) of the uneven settlement exceeds the preset inclination rate threshold, and / or when the shaking axis angle data θ nWhen the preset shaking axis angle threshold is exceeded, an alarm will be displayed.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The embodiment of the present invention provides a system and method for online monitoring of the health status of a wind turbine tower. The current technical solution requires the use of a professional total station to detect the wind turbine tower, and the detection can only be performed every few months under good weather conditions. The technical solution of the present invention can detect the parameter data of the wind turbine tower in real time online, determine the health status of the wind turbine tower, and then generate a health parameter report for the life cycle of the wind turbine tower, providing data reference for maintenance or inspection, and avoiding major accidents such as tower collapse.

[0047] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0049] Figure 1 It is a structural schematic diagram of a wind turbine tower health status online monitoring system provided by the present invention;

[0050] Figure 2 A is a schematic diagram of a model of a wind turbine tower swaying around a straight axis provided by the present invention;

[0051] Figure 2 B is a schematic diagram of a model of a wind turbine tower swaying around a bending axis provided by the present invention;

[0052] Figure 3 It is a schematic diagram of installing a MEMS sensor provided by the present invention on a wind turbine tower. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clear, the embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily without conflict.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0055] Embodiment 1

[0056] Figure 1 1 is a schematic diagram of the structure of a wind turbine tower health status online monitoring system according to the first embodiment of the present invention. This embodiment is applicable to the case of online monitoring of the health status of wind turbine towers. The system can be implemented in the form of hardware and / or software. Figure 1 As shown, the system includes: a plurality of MEMS sensors installed on the wind turbine tower and a remote background server, wherein each MEMS sensor is located on the same longitudinal straight line.

[0057] It should be noted that the MEMS sensor is used to collect the first data of the wind turbine tower in the carrier coordinate system and transmit the first data to the background server. The carrier coordinate system refers to the coordinate system established by the MEMS sensor using itself as a reference. When the wind turbine tower shakes, the MEMS sensors at different positions feel different degrees of shaking, and the changes in the collected data also vary greatly.

[0058] Preferably, the MEMS sensor can transmit data to the backend server through the optical fiber ring network.

[0059] The first data includes: angular velocity data Gn of the nth MEMS sensor, first acceleration data An, magnetic data Mn, etc. The first data is the perception data of different MEMS sensors themselves, reflecting the local shaking of the wind turbine tower. When shaking occurs, MEMS sensors at different positions produce small data changes relative to themselves.

[0060] In the embodiment of the present invention, multiple MEMS sensors are installed at different heights of the wind turbine tower, and the MEMS sensors are randomly distributed on the same longitudinal straight line. The longitudinal spacing between adjacent MEMS sensors can be the same or different; and the installation height difference of adjacent MEMS sensors can be the same or different. MEMS is a micro-electromechanical system, which can integrate micro-mechanisms, micro-sensors, micro-actuators, signal processing and control circuits, interfaces, communications and power supplies, etc. into one or more chips. Micro devices or systems.

[0061] The MEMS sensors used in the embodiments of the present invention mainly include accelerometers, gyroscopes, magnetometers, thermometers, and the like.

[0062] The second solution in the prior art is to use a tilt sensor and an acceleration sensor, which can sense the posture or tilt of the wind turbine tower, but cannot reflect the health status of the wind turbine tower. It is also impossible for wind farm staff to watch the data collected by the MEMS sensor all day long.

[0063] Figure 2 A is a schematic diagram of a model of a wind turbine tower swaying around a straight axis provided by the present invention. The second solution in the prior art cannot reflect the health status of the wind turbine tower, because the second solution can only be applied to Figure 2 In the case of the wind turbine tower swaying around the straight axis in A, the calculated displacement will be much larger than the actual displacement. The method of using the acceleration sensor for double integration has the problem that the initial position cannot be accurately obtained and the integral operation is disturbed and has low accuracy.

[0064] Figure 2 B is a schematic diagram of a model of a wind turbine tower swaying around a bending axis provided by the present invention. In actual situations, the wind turbine tower sways around a bending axis, such as Figure 2 As shown in B, due to the heavy weight of the fan and the fan blades, the fan tower will bend under the huge pressure of the fan and the fan blades. Since the structure of the fan tower is that the closer to the ground, the larger the diameter R, and the closer to the top, the smaller the diameter R, the closer to the ground, the smaller the bending degree of the fan tower; the closer to the top, the larger the bending degree. Therefore, it is necessary to solve the first data collected by the MEMS sensor.

[0065] Furthermore, the background server is used to periodically receive the first data collected by each MEMS sensor, calculate the second data in the geodetic coordinate system according to the first data, and determine the health status of the wind turbine tower according to the second data.

[0066] It should be noted that the geodetic coordinate system refers to a unified coordinate system built with the ground as a reference. Since the first data is sensed by each MEMS sensor itself, the MEMS sensors at different locations need to send the collected first data to the background server and restore it to the geodetic coordinate system for unification. The second data reflects the overall shaking of the wind turbine tower. With the ground as a reference, when shaking occurs, the MEMS sensors at different locations will have obvious data changes relative to the ground.

[0067] Optionally, the second data includes: Euler angle data En of the nth MEMS sensor, second acceleration data ACCn, shaking distance data Sn, shaking axis angle data θ n etc.

[0068] It should be noted that if Figure 2 A and Figure 2As shown in the real axis of B, the tower is shaking around a central axis. Each point on the central axis is the axis point. After installation, the MEMS sensor is in the same horizontal plane as the axis point. When shaking occurs, the MEMS sensor tilts with the plane where the axis point is located, and an inclination angle is generated with the horizontal plane. The inclination angle is consistent with the angle of the axis point offset from the original central axis, which is the shaking axis angle data θ n .

[0069] Figure 3 FIG. 1 is a schematic diagram of installing a MEMS sensor provided by the present invention on a wind turbine tower. Figure 3 As shown, five MEMS sensors are installed on the wind turbine tower, from the top to the bottom, they are: MEMS sensor 1, MEMS sensor 2, MEMS sensor 3, MEMS sensor 4 and MEMS sensor 5.

[0070] The MEMS sensors are on the same longitudinal straight line, the installation height difference between MEMS sensor 1 and MEMS sensor 2 is H1, the installation height difference between MEMS sensor 2 and MEMS sensor 3 is H2, the installation height difference between MEMS sensor 3 and MEMS sensor 4 is H3, and the installation height difference between MEMS sensor 4 and MEMS sensor 5 is H4. Similarly, the horizontal relative displacement value between MEMS sensor 1 and MEMS sensor 2 in the horizontal direction is d1.

[0071] Optionally, the second data also includes: an installation height difference Hn between the nth MEMS sensor and the (n+1)th MEMS sensor, a horizontal relative displacement value dn, and the like.

[0072] Furthermore, the nth MEMS sensor is located above the (n+1)th MEMS sensor.

[0073] It should be noted that dn is the horizontal relative displacement value between the nth MEMS sensor and the n+1th MEMS sensor, Hn is the installation height difference between the nth MEMS sensor and the n+1th MEMS sensor, θ n is the shaking axis angle data of the nth MEMS sensor, θ n+1 is the shaking axis angle data of the n+1th MEMS sensor, and the relationship formula is: dn=Hn*(1-cos(θ n -θ n+1 )) / (θ n -θ n+1 ).

[0074] The backend server calculates the second data in the geodetic coordinate system according to the first data, including:

[0075] The backend server obtains the gravity direction according to the first acceleration data An, obtains the north-south direction according to the magnetic data Mn, and constructs a geodetic coordinate system according to the gravity direction and the north-south direction.

[0076] The backend server performs data fusion on the angular velocity data Gn, the first acceleration data An, and the magnetic data Mn. The angular velocity data Gn, the first acceleration data An, and the magnetic data Mn can all detect shaking data, but the scales of different sensors are different, so it is necessary to use a transformation matrix to uniformly transform them to Euler angles, and then perform weighted summation based on the noise ratio of each sensor to obtain the fused Euler angle data En. The Euler angle data En can be used to calculate the coordinate system transformation matrix, and the first acceleration data An and the coordinate system transformation matrix can be used for matrix multiplication to obtain the second acceleration data ACCn. For example, Figure 3 Taking the five MEMS sensors shown as an example, the angular velocity data G1\2\3\4\5 can be used to calculate the shaking angle by integrating the time. The Kalman filter is used to fuse the angular velocity data G1\2\3\4\5, the acceleration data A1\2\3\4\5, and the magnetic data M1\2\3\4\5 to obtain the Euler angle data E1\2\3\4\5 of the MEMS sensor in the geodetic coordinate system and the acceleration data ACC1\2\3\4\5 in the geodetic coordinate system.

[0077] The backend server performs an integral operation on the second acceleration data ACCn to obtain the shaking distance data Sn. Figure 3 Taking the five MEMS sensors shown as an example, the acceleration data ACC1\2\3\4\5 are integrated twice with respect to time, the drift term is calculated using a matched filter, and the shaking distance data S1\2\3\4\5 can be obtained by removing the drift term.

[0078] The backend server performs least squares calculation on the Euler angle data En and the second acceleration data ACCn to obtain the shaking axis angle data θ n For example: Figure 3Taking the five MEMS sensors shown as an example, the time window T1 is selected. T1 generally selects 2-4 inherent shaking periods of the wind turbine tower. According to the Euler angle data E1\2\3\4\5 in the geodetic coordinate system and the acceleration data ACC1\2\3\4\5 in the geodetic coordinate system, the least squares method is used to estimate the shaking axis angle data θ1\2\3\4\5. The least squares method is to find the shaking axis angle θ by iterative traversal, so that the result of Σ|(θ-(a*Ei+(1-a)*arctan(ACCi / g))| is minimized, indicating that all Euler angles E and acceleration ACC in the time window T1, that is, the estimated shaking axis angle θ and all measurement results have the smallest overall error. Among them, a is a weighting factor, g is the acceleration of gravity, i is a time window, and i can be 1, 2...k.

[0079] Furthermore, the backend server determines the health status of the wind turbine tower according to the second data, including:

[0080] Determine in real time whether there is a second acceleration ACCn exceeding the preset acceleration threshold. If so, an early warning display is performed when the second acceleration ACCn exceeds the preset acceleration threshold within a preset continuous time and / or when the shaking distance data Sn exceeds the preset shaking distance threshold within a preset continuous time.

[0081] If it does not exist, the backend server obtains the verticality P according to the installation height difference Hn and the horizontal relative displacement value dn; according to the second acceleration data ACCn and the shaking axis angle data θ n , when the second acceleration data ACCn is less than the threshold, the shaking axis angle data θ is counted n According to the probability distribution curve, the center of the probability distribution is found and the central angle α is obtained; according to the central angle α and the initial central angle α0, the inclination rate tan(α-α0) of the uneven settlement is obtained.

[0082] When the verticality P exceeds the preset verticality threshold, and / or, when the inclination rate tan(α-α0) of the uneven settlement exceeds the preset inclination rate threshold, and / or, when the shaking axis angle data θ n When the preset shaking axis angle threshold is exceeded, an alarm will be displayed.

[0083] Specifically, Figure 3Taking the five MEMS sensors shown as an example, the processing is performed according to whether the second acceleration ACC1\2\3\4\5 in the geodetic coordinate system exceeds the threshold value. If the second acceleration ACC1\2\3\4\5 in the geodetic coordinate system are all less than the threshold value, it means that the wind turbine is not generating electricity and the wind force is small, which meets the conditions for measuring the verticality of the wind turbine tower, the uneven settlement inclination rate and the bending deformation angle of the wind turbine tower. Then: calculate the horizontal relative displacement value d1\2\3\4 between the five MEMS sensors, the horizontal relative displacement value D=d1+d2+d3+d4 between the wind turbine tower and the MEMS sensor 1 and the MEMS sensor 5, and the verticality P=arctan(D / (H1+H2+H3+H4)); according to the corresponding sway axis angle data θ1\2\3\4\5 within the time window T2 when the second acceleration ACC1\2\3\4\5 is less than the preset acceleration threshold, use the least squares method to estimate the center angle α. The time window T2 is generally selected to be relatively long, and the sway axis angle data θ1\2\3\4\5 are required. There are enough numbers to ensure the calculation accuracy of the center angle α; calculate tan(α-α0) based on the currently calculated center angle α and the center angle α0 during initial installation. It is assumed here that there is no uneven settlement or the uneven settlement is small during initial installation, and tan(α-α0) is the inclination rate of uneven settlement; determine whether the verticality P, the inclination rate of uneven settlement and the sway axis angle data θ1\2\3\4\5 exceed the safety threshold. If the safety threshold is exceeded, an alarm is issued, indicating that there are safety hazards in the wind turbine tower, such as loose bolts, bending deformation of the tower, metal fatigue or cracks, etc., and a safety inspection of the wind turbine tower is required to avoid major accidents such as tower collapse.

[0084] For example: When a wind turbine is in different states, its verticality will vary within a certain range. The maximum verticality within a certain period of time is calculated to be less than ±0.08 degrees, while the preset verticality threshold is ±0.5 degrees. In addition, the verticality is evenly distributed around zero, and there is no uneven settlement. This indicates that the wind turbine tower is currently in a very healthy state.

[0085] If the second acceleration ACC1\2\3\4\5 in the geodetic coordinate system exceeds the threshold value, it means that the fan is shaking too much at this time, and an early warning is performed. At the same time, the second acceleration ACC1\2\3\4\5 in the geodetic coordinate system is integrated twice, and the drift term is removed to obtain the shaking distance data S1\2\3\4\5; according to the second acceleration ACC1\2\3\4\5 in the geodetic coordinate system and the shaking distance data S1\2\3\4\5, it is judged in real time whether there is a situation that exceeds the early warning threshold. If the preset continuous time t exceeds the early warning threshold, an early warning display is performed, indicating that the fan is shaking too much at this time, and there is a safety risk. The fan state can be adjusted to reduce the fan shaking amplitude to ensure the safety of the fan.

[0086] Preferably, the second acceleration data ACCn, the shaking distance data Sn, the verticality detection result P, the uneven settlement inclination rate tan(α-α0) and the shaking axis angle data θ in the geodetic coordinate system are n The data is stored in the database in real time. The wind turbine tower health status report is generated regularly, and the differences between the various parameter indicators of the current wind turbine tower and the various parameter indicators of the wind turbine tower during initial installation are compared to provide data reference for maintenance or inspection.

[0087] The main problem solved by the embodiment of the present invention is to further analyze and process the data collected by the MEMS sensor, and use numerical modeling to calculate parameter indicators that can directly reflect the health status of the wind turbine tower. The main parameter indicators include uneven settlement inclination rate, wind turbine tower verticality, and wind turbine tower curvature. These parameter indicators are clearly defined during the production and construction process and can directly reflect the health status of the wind turbine tower.

[0088] Embodiment 2

[0089] An embodiment of the present invention provides a method for online monitoring of the health status of a wind turbine tower. This embodiment is applicable to the case of online monitoring of the health status of a wind turbine tower. The method includes:

[0090] A plurality of MEMS sensors collect first data of a wind turbine tower in a carrier coordinate system, and transmit the first data to a background server.

[0091] The first data includes: angular velocity data Gn, first acceleration data An, magnetic data Mn, and the like of the nth MEMS sensor.

[0092] The backend server calculates second data in the geodetic coordinate system based on the first data, and determines the health status of the wind turbine tower based on the second data.

[0093] The second data includes: Euler angle data En of the nth MEMS sensor, second acceleration data ACCn, shaking distance data Sn, shaking axis angle data θ n etc.

[0094] The second data also includes: an installation height difference Hn between the nth MEMS sensor and the (n+1)th MEMS sensor, a horizontal relative displacement value dn, and the like.

[0095] Further, the nth MEMS sensor is located above the n+1th MEMS sensor, and,

[0096] dn=Hn*(1-cos(θ n -θ n+1 )) / (θ n -θ n+1 ).

[0097] Furthermore, the backend server calculates second data in the geodetic coordinate system according to the first data, and determines the health status of the wind turbine tower according to the second data, including:

[0098] S1: Obtain the gravity direction according to the first acceleration data An; obtain the north-south direction according to the magnetic data Mn; and construct a geodetic coordinate system according to the gravity direction and the north-south direction;

[0099] S2: fusing the angular velocity data Gn, the first acceleration data An, and the magnetic data Mn to obtain the Euler angle En and the second acceleration data ACCn;

[0100] S3: Determine in real time whether the second acceleration ACCn exceeds the preset acceleration threshold. If the second acceleration ACCn exceeds the preset acceleration threshold, execute S41-S42;

[0101] S41: performing an integration operation on the second acceleration data ACCn to obtain shaking distance data Sn;

[0102] S42: when the second acceleration ACCn exceeds the preset acceleration threshold within the preset continuous time, and / or when the shaking distance data Sn exceeds the preset shaking distance threshold within the preset continuous time, a warning display is performed;

[0103] The method further includes: S3: determining in real time whether the second acceleration ACCn exceeds a preset acceleration threshold, and if the second acceleration ACCn does not exceed the preset acceleration threshold, executing S51-S54;

[0104] S51: Perform least squares calculation on the Euler angle data En and the second acceleration data ACCn to obtain the shaking axis angle data θ n ;

[0105] S52: Obtain verticality P according to the installation height difference Hn and the horizontal relative displacement value dn;

[0106] S53: Based on the second acceleration data ACCn and the shaking axis angle data θ n , and obtain the central angle α; according to the central angle α and the initial central angle α0, the inclination rate tan(α-α0) of uneven settlement is obtained;

[0107] S54: When the verticality P exceeds the preset verticality threshold, and / or when the inclination rate tan(α-α0) of the uneven settlement exceeds the preset inclination rate threshold, and / or when the shaking axis angle data θ n When the preset shaking axis angle threshold is exceeded, an alarm will be displayed.

[0108] The embodiment of the present invention further analyzes the data collected by the MEMS sensor, solves the collected data according to the model of the wind turbine tower shaking around the bending axis, extracts characteristic parameters that can truly reflect the health status of the wind turbine tower, and realizes 7*24 hours effective monitoring and control of the health status of the wind turbine tower. It can also provide wind tower health status early warning (the wind turbine tower may have problems) and wind tower health status alarm (the wind turbine tower has problems), provide data reference for maintenance or inspection, and avoid major accidents such as tower collapse.

[0109] In the description of the embodiments of the present invention, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0110] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0111] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted to facilitate understanding of the present invention and are not intended to limit the present invention. Any technician in the field to which the present invention belongs can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. An online monitoring system for the health status of a wind turbine tower, characterized in that: The system comprises: a plurality of MEMS sensors installed on a wind turbine tower and a remote backend server, wherein the plurality of MEMS sensors are located on the same longitudinal straight line; The multiple MEMS sensors are used to collect first data of the wind turbine tower in the carrier coordinate system and transmit the first data to the background server; The background server is used to periodically receive the first data collected by each MEMS sensor, calculate the second data in the geodetic coordinate system according to the first data, and determine the health status of the wind turbine tower according to the second data.

2. The system according to claim 1, characterized in that The first data includes: angular velocity data Gn, first acceleration data An, and magnetic data Mn of the nth MEMS sensor.

3. The system according to claim 1, characterized in that The second data includes: Euler angle data En of the nth MEMS sensor, second acceleration data ACCn, shaking distance data Sn, shaking axis angle data θ n ; The second data also includes: an installation height difference Hn and a horizontal relative displacement value dn between the nth MEMS sensor and the n+1th MEMS sensor; Wherein, the nth MEMS sensor is located above the n+1th MEMS sensor, and, dn=Hn*(1-cos(θ n -θ n+1 )) / (θ n -θ n+1 )。 4. The system according to claim 1, characterized in that The backend server calculates second data in a geodetic coordinate system according to the first data, including: The backend server obtains the gravity direction according to the first acceleration data An; The backend server obtains the north-south direction according to the magnetic data Mn; The backend server constructs a geodetic coordinate system according to the gravity direction and the north-south direction; The backend server performs data fusion on the angular velocity data Gn, the first acceleration data An, and the magnetic data Mn to obtain Euler angle data En and second acceleration data ACCn; The backend server performs an integration operation on the second acceleration data ACCn to obtain the shaking distance data Sn; The backend server performs a least squares operation on the Euler angle data En and the second acceleration data ACCn to obtain the shaking axis angle data θ n .

5. The system according to claim 4, characterized in that The backend server determines the health status of the wind turbine tower according to the second data, including: Determine in real time whether the second acceleration ACCn exceeds the preset acceleration threshold. If so, when the second acceleration ACCn exceeds the preset acceleration threshold within a preset continuous time, and / or when the shaking distance data Sn exceeds the preset shaking distance threshold within a preset continuous time, a warning display is performed; If it does not exist, the backend server obtains the verticality P according to the installation height difference Hn and the horizontal relative displacement value dn; according to the second acceleration data ACCn and the shaking axis angle data θ n , and obtain the central angle α; according to the central angle α and the initial central angle α0, the inclination rate tan(α-α0) of uneven settlement is obtained; When the verticality P exceeds the preset verticality threshold, and / or, when the inclination rate tan(α-α0) of the uneven settlement exceeds the preset inclination rate threshold, and / or, when the shaking axis angle data θ n When the preset shaking axis angle threshold is exceeded, an alarm will be displayed.

6. A method for online monitoring of wind turbine tower health status, characterized in that: The method comprises: A plurality of MEMS sensors collect first data of the wind turbine tower in a carrier coordinate system, and transmit the first data to a background server; The backend server calculates second data in a geodetic coordinate system based on the first data, and determines a health status of the wind turbine tower based on the second data.

7. The method according to claim 6, characterized in that The first data includes: angular velocity data Gn, first acceleration data An, and magnetic data Mn of the nth MEMS sensor.

8. The method according to claim 6, characterized in that The second data includes: Euler angle data En of the nth MEMS sensor, second acceleration data ACCn, shaking distance data Sn, shaking axis angle data θ n ; The second data also includes: an installation height difference Hn and a horizontal relative displacement value dn between the nth MEMS sensor and the n+1th MEMS sensor; The nth MEMS sensor is located above the n+1th MEMS sensor, and, dn=Hn*(1-cos(θ n -θ n+1 )) / (θ n -θ n+1 )。 9. The method according to claim 6, characterized in that The backend server calculates second data in a geodetic coordinate system according to the first data, and determines a health status of the wind turbine tower according to the second data, including: S1: Obtain the gravity direction according to the first acceleration data An; obtain the north-south direction according to the magnetic data Mn; and construct a geodetic coordinate system according to the gravity direction and the north-south direction; S2: fusing the angular velocity data Gn, the first acceleration data An, and the magnetic data Mn to obtain the Euler angle En and the second acceleration data ACCn; S3: Determine in real time whether the second acceleration ACCn exceeds the preset acceleration threshold. If the second acceleration ACCn exceeds the preset acceleration threshold, execute S41-S42; S41: performing an integration operation on the second acceleration data ACCn to obtain shaking distance data Sn; S42: When the second acceleration ACCn exceeds the preset acceleration threshold within the preset continuous time, and / or when the shaking distance data Sn exceeds the preset shaking distance threshold within the preset continuous time, a warning display is performed.

10. The method according to claim 9, characterized in that S3: Determine in real time whether the second acceleration ACCn exceeds the preset acceleration threshold. If the second acceleration ACCn does not exceed the preset acceleration threshold, execute S51-S54; S51: Perform least squares calculation on the Euler angle data En and the second acceleration data ACCn to obtain the shaking axis angle data θ n ; S52: Obtain verticality P according to the installation height difference Hn and the horizontal relative displacement value dn; S53: Based on the second acceleration data ACCn and the shaking axis angle data θ n , and obtain the central angle α; according to the central angle α and the initial central angle α0, the inclination rate tan(α-α0) of uneven settlement is obtained; S54: When the verticality P exceeds the preset verticality threshold, and / or when the inclination rate tan(α-α0) of the uneven settlement exceeds the preset inclination rate threshold, and / or when the shaking axis angle data θ n When the preset shaking axis angle threshold is exceeded, an alarm will be displayed.

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