A method and system for monitoring ice coverage status of wind turbine blades

Abstract

The present invention discloses a method and system for monitoring the icing status of wind turbine blades, which are applied to the field of wind power generation technology. The method includes: S1, collecting historical blade vibration data of wind turbine blades in different icing states; S2, generating an icing diagnosis function based on the historical blade vibration data; S3, acquiring real-time vibration data of the wind turbine blades; S4, generating an icing diagnosis matrix based on the real-time vibration data; S5, judging the icing status of the wind turbine blades based on the icing diagnosis matrix and the icing diagnosis function, and obtaining an icing diagnosis result. The invention provides a method and system for monitoring the icing status of wind turbine blades, which can monitor the icing status of wind turbine blades in real time, have high accuracy and can perform specific diagnostic analysis on the icing status.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and more particularly to a method and system for monitoring the icing status of blades of a wind turbine generator. Background Art

[0002] Large-scale wind power plants are mainly located in humid, cold and high-altitude areas. Low temperatures in winter can easily cause blade ice, which seriously restricts the development of the wind power industry. Blade ice will change the lift coefficient and drag coefficient of the blade, greatly affecting its aerodynamic characteristics and leading to serious wind turbine power loss. On the other hand, blade ice is usually uneven, which will cause unbalanced rotation of the wind rotor, intensified fan vibration, and even resonance, increasing fan load and reducing fan service life. When the fan blades rotate at high speed, the ice attached to the blades is thrown off due to centrifugal force during rotation, posing a great threat to the safety of the unit and nearby personnel. Therefore, it is necessary to be able to monitor the ice coverage of the fan blades in real time.

[0003] Currently, ice measurement can be divided into two types. One is to collect images through sensor equipment for analysis, but the accuracy of image analysis is low when the wind turbine is working; the other is to estimate ice based on ice-related factors such as wind speed, humidity and temperature and the change in wind turbine power generation under ice conditions, but this method is greatly affected by the environment, and the existing prediction model can only make preliminary predictions and cannot give specific diagnostic results.

[0004] Therefore, it is an urgent problem for those skilled in the art to propose a method and system for monitoring the ice coating status of wind turbine blades to solve the difficulties existing in the prior art. Summary of the invention

[0005] In view of this, the present invention provides a method and system for monitoring the icing status of wind turbine blades, which can monitor the icing status of wind turbine blades in real time with high accuracy and can perform specific diagnosis and analysis on the icing status.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for monitoring the ice coating status of wind turbine blades comprises the following steps:

[0008] S1. Collect historical blade vibration data of wind turbine blades under different icing conditions;

[0009] S2. generating an icing diagnosis function based on the historical blade vibration data;

[0010] S3, obtaining real-time vibration data of the wind turbine blades;

[0011] S4, generating an icing diagnosis matrix based on the real-time vibration data;

[0012] S5. Determine the icing state of the wind turbine blade based on the icing diagnosis matrix and the icing diagnosis function to obtain an icing diagnosis result.

[0013] Preferably, S2 is specifically:

[0014] Taking the minimum ice coverage value of the wind turbine blade under different ice coverage conditions as a threshold, an ice coverage diagnosis function is generated: x =-5.362x 6 +2.544x 5 +10.635x 4 -12.026x 3 +3.752x 2 +0.458x;

[0015] In the formula, y x is the ice coverage value at the blade x position.

[0016] Preferably, the ice diagnosis matrix P in S4 (m) for:

[0017]

[0018] Where m is the highest-order mode number, m∈[1,3], n∈[1,m], is the ice cover value at point i, i∈[1,k], k≥0, and n is a positive integer.

[0019] Preferably, the ice coverage value In for:

[0020]

[0021] In the formula, is the curvature change rate of the n-order mode shape of the fan blade at point i, is the n-order modal vibration curvature of the fan blade at point i measured in real time, is the n-order modal vibration curvature of the wind turbine blade at point i in the non-icing state; where, for:

[0022]

[0023] In the formula, is the n-order vibration displacement at point i of the fan blade, x i-1 is the distance from point 1 to point i, x i is the distance from point i to point i+1.

[0024] Preferably, the S4 further includes calculating the ice thickness, specifically:

[0025] d=-3.06x+147.71ε (n) +3.77

[0026] Where d is the ice thickness, x is the center position of the ice area, and ε (n) is the eigenvalue of ice thickness in the nth mode.

[0027] Preferably, the n-order modal ice thickness characteristic value ε (n) The calculation formula is:

[0028]

[0029] In the formula, is the n-order modal vibration curvature of the fan blade at point i measured in real time, is the curvature of the nth-order mode of the wind turbine blade at point i in the non-icing state.

[0030] Preferably, after S5, the step further includes:

[0031] S6. Determine the accuracy of the icing diagnosis result based on the environmental data, specifically: collect current environmental data, the environmental data including: temperature data, humidity data, air pressure data, wind speed data; when the environmental data is within a preset icing threshold range, the icing diagnosis result is accurate.

[0032] Preferably, after S6, the step further includes:

[0033] S7, calculating the severity of icing, issuing a corresponding warning according to the severity of icing and displaying a specific icing diagnosis result, wherein the severity of icing is calculated by: calculating the vibration frequency deviation rate Δf: Wherein, f is the current blade vibration frequency, f' is the initial blade vibration frequency, when 0≤Δf≤5%, the icing severity is no icing; when 5%<Δf≤10%, the icing severity is slight icing; when 10%<Δf≤15%, the icing severity is moderate icing; when Δf>15%, the icing severity is severe icing.

[0034] Preferably, after collecting the historical blade vibration data in S1, the historical blade vibration data is preprocessed to eliminate invalid data, and the invalid data includes: vibration data when the fan system fails, vibration data when the fan power is limited, and vibration data when the environmental data is abnormal.

[0035] The present invention also discloses a wind turbine blade icing state monitoring system, comprising: a data acquisition module, a data processing module, a vibration sensor, a modal analysis module, and an icing diagnosis module;

[0036] The data acquisition module is connected to the data processing module and is used to collect historical blade vibration data of wind turbine blades under different icing conditions;

[0037] The data processing module is connected to the icing diagnosis module and is used to generate an icing diagnosis function based on the historical blade vibration data;

[0038] The vibration sensor is connected to the modal analysis module and is used to obtain real-time vibration data of the wind turbine blade;

[0039] The modal analysis module is connected to the icing diagnosis module and is used to generate an icing diagnosis matrix based on the real-time vibration data;

[0040] The icing diagnosis module is used to determine the icing state of the wind turbine blade based on the icing diagnosis matrix and the icing diagnosis function to obtain an icing diagnosis result.

[0041] A storage medium includes stored instructions, wherein when the instructions are executed, a device where the storage medium is located is controlled to execute the above-mentioned wind turbine blade ice coverage monitoring method.

[0042] An electronic device comprises a memory and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by one or more processors to implement the above-mentioned wind turbine blade ice coverage monitoring method.

[0043] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a method and system for monitoring the icing status of wind turbine blades, which has the following beneficial effects: a diagnostic function is established through historical data, and data is collected in real time for diagnosis, which has high diagnostic accuracy and can monitor the icing situation in real time; the diagnostic results are calculated by collecting data, and the icing situation can be specifically analyzed to obtain the icing area and ice thickness; the diagnostic results are further judged by subsequent environmental data to ensure the reliability of the diagnostic results; data preprocessing is performed on the historical data of the wind farm, and invalid data that affects the diagnosis of the icing status is proposed, so that the prediction results are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0045] Figure 1 This is a flow chart of the blade icing monitoring method of the present invention;

[0046] Figure 2 It is a structural diagram of the blade ice monitoring system of the present invention;

[0047] Figure 3 This is a schematic diagram of the arrangement position of the vibration sensor of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The embodiment of the present invention discloses a method for monitoring the ice coating status of wind turbine blades. Figure 1 As shown, the following steps are included:

[0050] S1. Collect historical blade vibration data of wind turbine blades under different icing conditions;

[0051] S2, generating an icing diagnosis function based on historical blade vibration data;

[0052] S3, obtaining real-time vibration data of wind turbine blades;

[0053] S4, generating an icing diagnosis matrix based on real-time vibration data;

[0054] S5. Determine the icing state of the wind turbine blades based on the icing diagnosis matrix and the icing diagnosis function to obtain an icing diagnosis result.

[0055] Furthermore, S2 is specifically:

[0056] Taking the minimum ice value of wind turbine blades under different ice conditions as the threshold, an ice diagnosis function is generated: x =-5.362x 6 +2.544x 5 +10.635x 4 -12.026x3 +3.752x 2 +0.458x;

[0057] In the formula, y x is the ice coverage value at the blade x position.

[0058] Furthermore, the icing diagnosis matrix P in S4 (m) for:

[0059]

[0060] Where m is the highest-order mode number, m∈[1,3], n∈[1,m], is the ice coverage value at point i, i∈[1,k], k≥0, n is a positive integer, and in this embodiment, m=3.

[0061] Furthermore, the ice value In for:

[0062]

[0063] In the formula, is the curvature change rate of the n-order mode shape of the fan blade at point i, is the n-order modal vibration curvature of the fan blade at point i measured in real time, is the n-order modal vibration curvature of the wind turbine blade at point i in the non-icing state; where, for:

[0064]

[0065] In the formula, is the n-order vibration displacement at point i of the fan blade, x i-1 is the distance from point 1 to point i, x i is the distance from point i to point i+1.

[0066] Furthermore, S4 also includes calculating the ice thickness, specifically:

[0067] d=-3.06x+147.71ε (n) +3.77

[0068] Where d is the ice thickness, x is the center position of the ice area, and ε (n) is the eigenvalue of ice thickness in the nth mode.

[0069] Furthermore, the n-order mode ice thickness eigenvalue ε (n) The calculation formula is:

[0070]

[0071] In the formula, is the n-order modal vibration curvature of the fan blade at point i measured in real time, is the curvature of the nth-order mode of the wind turbine blade at point i in the non-icing state.

[0072] Furthermore, S5 and later also include:

[0073] S6. Determine the accuracy of the icing diagnosis result based on the environmental data, specifically: collect current environmental data, the environmental data includes: temperature data, humidity data, air pressure data, wind speed data; when the environmental data is within the preset icing threshold range, the icing diagnosis result is accurate.

[0074] Furthermore, after S6, it also includes:

[0075] S7. Calculate the severity of icing, issue a corresponding warning based on the severity of icing, and display specific icing diagnosis results. Calculate the severity of icing by: Calculate the vibration frequency deviation rate Δf: Wherein, f is the current blade vibration frequency, f' is the initial blade vibration frequency, when 0≤Δf≤5%, the severity of icing is no icing; when 5%<Δf≤10%, the severity of icing is slight icing; when 10%<Δf≤15%, the severity of icing is moderate icing; when Δf>15%, the severity of icing is severe icing.

[0076] Furthermore, after collecting the historical blade vibration data in S1, the historical blade vibration data is preprocessed to eliminate invalid data, which includes: vibration data when the fan system fails, vibration data when the fan power is limited, and vibration data when the environmental data is abnormal.

[0077] and Figure 1 Corresponding to the method described above, the embodiment of the present invention further provides a wind turbine blade icing status monitoring system for monitoring Figure 1 In the specific implementation of the method, the wind turbine blade ice monitoring system provided by the embodiment of the present invention can be applied to a computer terminal or various mobile devices, and its structural schematic diagram is as follows Figure 2 As shown, it includes: a data acquisition module, a data processing module, a vibration sensor, a modal analysis module, and an ice diagnosis module;

[0078] The data acquisition module is connected to the data processing module and is used to collect historical blade vibration data of wind turbine blades under different icing conditions;

[0079] The data processing module is connected to the icing diagnosis module and is used to generate an icing diagnosis function based on historical blade vibration data;

[0080] The vibration sensor is connected to the modal analysis module to obtain real-time vibration data of the wind turbine blades;

[0081] The modal analysis module is connected to the icing diagnosis module and is used to generate an icing diagnosis matrix based on real-time vibration data;

[0082] The icing diagnosis module is used to determine the icing state of the wind turbine blades based on the icing diagnosis matrix and the icing diagnosis function to obtain an icing diagnosis result.

[0083] Furthermore, the installation position of the vibration sensor is as follows Figure 3 As shown, a plurality of vibration sensors are respectively installed at the tip, middle and root of the fan blade to measure the real-time vibration data of the blade.

[0084] A storage medium includes stored instructions, wherein when the instructions are executed, a device where the storage medium is located is controlled to execute the above-mentioned wind turbine blade ice coverage monitoring method.

[0085] An electronic device comprises a memory and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by one or more processors to implement the above-mentioned wind turbine blade ice coverage monitoring method.

[0086] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0087] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the ice coating status of wind turbine blades, characterized in that: The following steps are involved: S1. Collect historical blade vibration data of wind turbine blades under different icing conditions; S2. generating an icing diagnosis function based on the historical blade vibration data; S3, obtaining real-time vibration data of the wind turbine blades; S4, generating an icing diagnosis matrix based on the real-time vibration data; S5, judging the icing state of the wind turbine blade based on the icing diagnosis matrix and the icing diagnosis function, and obtaining an icing diagnosis result; The minimum ice coverage value of the wind turbine blade under different ice coverage states is used as a threshold to generate the ice coverage diagnostic function: y x =-5.362x 6 +2.544x 5 +10.635x 4 -12.026x 3 +3.752x 2 +0.458x In the formula, y x is the ice coverage value at the blade x position; The icing diagnosis matrix P in S4 (m) for: Where m is the highest-order mode number, m∈[1,3], n∈[1,m], is the ice cover value at point i, i∈[1,k], k≥0, n is a positive integer; The ice coverage value In for: In the formula, is the curvature change rate of the n-order mode shape of the fan blade at point i, is the n-order modal vibration curvature of the fan blade at point i measured in real time, is the n-order modal vibration curvature of the wind turbine blade at point i in the non-icing state; where, for: In the formula, is the n-order vibration displacement at point i of the fan blade, x i-1 is the distance from point 1 to point i, x i is the distance from point i to point i+1.

2. A method for monitoring the icing status of wind turbine blades according to claim 1, characterized in that: The S4 also includes calculating the ice thickness, specifically: d=-3.06x+147.71e (n) +3.77 Where d is the ice thickness, x is the center position of the ice area, and ε (n) is the eigenvalue of ice thickness in the nth mode.

3. A method for monitoring the icing status of wind turbine blades according to claim 2, characterized in that: The n-order mode ice thickness characteristic value ε (n) The calculation formula is: In the formula, is the n-order modal vibration curvature of the fan blade at point i measured in real time, is the curvature of the nth-order mode of the wind turbine blade at point i in the non-icing state.

4. A method for monitoring the ice coating status of wind turbine blades according to claim 1, characterized in that: The S5 also includes: S6. Determine the accuracy of the icing diagnosis result according to the environmental data, specifically: collect current environmental data, the environmental data including: temperature data, humidity data, air pressure data, wind speed data; when the environmental data is within a preset icing threshold range, the icing diagnosis result is accurate.

5. A method for monitoring the ice coating status of wind turbine blades according to claim 4, characterized in that: The S6 also includes: S7, calculating the severity of icing, issuing a corresponding warning according to the severity of icing and displaying a specific icing diagnosis result, wherein the severity of icing is calculated by: calculating the vibration frequency deviation rate Δn: Wherein, f is the current blade vibration frequency, f' is the initial blade vibration frequency, when 0≤Δf≤5%, the icing severity is no icing; when 5%<Δf≤10%, the icing severity is slight icing; when 10%<Δf≤15%, the icing severity is moderate icing; when Δf>15%, the icing severity is severe icing.

6. A method for monitoring the icing status of wind turbine blades according to claim 1, characterized in that: After the historical blade vibration data is collected in S1, the historical blade vibration data is preprocessed to eliminate invalid data, which includes: vibration data when the fan system fails, vibration data when the fan power is limited, and vibration data when the environmental data is abnormal.

7. A wind turbine blade icing status monitoring system, using the wind turbine blade icing status monitoring method according to any one of claims 1 to 6, characterized in that: include: Data acquisition module, data processing module, vibration sensor, modal analysis module, icing diagnosis module; The data acquisition module is connected to the data processing module and is used to collect historical blade vibration data of wind turbine blades under different icing conditions; The data processing module is connected to the icing diagnosis module and is used to generate an icing diagnosis function based on the historical blade vibration data; The vibration sensor is connected to the modal analysis module and is used to obtain real-time vibration data of the wind turbine blade; The modal analysis module is connected to the icing diagnosis module and is used to generate an icing diagnosis matrix based on the real-time vibration data; The icing diagnosis module is used to determine the icing state of the wind turbine blade based on the icing diagnosis matrix and the icing diagnosis function to obtain an icing diagnosis result.

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