Fan tower barrel service life monitoring method and storage medium

Abstract

The invention discloses a fan tower life monitoring method and a storage medium. The method comprises the steps of generating displacement time history data through an acceleration signal, combining a dynamically corrected stress amplitude calculation model, matching an S-N curve to determine a single damage ratio, performing classification statistics on annual accumulated damage values according to working conditions, and triggering hierarchical risk control based on a residual life index. According to the method, the problems of poor environmental adaptability, inaccurate damage quantification and risk response lag in a traditional method are solved, and the safety of the tower drum in the whole life cycle is remarkably improved.

Description

Technical Field

[0001] This invention relates to wind power equipment monitoring technology, and more particularly to a method for monitoring and controlling the life of wind turbine towers based on dynamic response analysis. Background Technology

[0002] Traditional tower life monitoring relies on periodic manual inspections or static stress models, which suffers from problems such as response lag, poor environmental adaptability, and inability to quantify cumulative damage. Summary of the Invention

[0003] In view of the technical defects and drawbacks existing in the prior art, embodiments of the present invention provide a method and storage medium for monitoring the life of wind turbine towers to overcome or at least partially solve the above problems. The specific solution is as follows:

[0004] As a first aspect of the present invention, a method for monitoring the lifespan of a wind turbine tower is provided, comprising:

[0005] S1: Real-time acquisition of acceleration signals at the top of the tower, and secondary integration and filtering of the acceleration signals to generate sway displacement time history data S(t);

[0006] S2: Calculate the sway amplitude A based on the maximum displacement value of the sway displacement time history data S(t), and combine it with the material elastic modulus E of the wind turbine tower and the moment of inertia I of the structural section, according to the stress transformation model. Calculate the stress amplitude at the bottom flange of the tower. ;

[0007] S3: Based on the stress amplitude Match the corresponding SN curve to determine the proportion of damage caused by a single shaking, and statistically analyze the annual cumulative damage value D based on the working condition classification;

[0008] S4: When the product of the annual cumulative damage value D and the design life L reaches the preset life loss threshold, a wind turbine operation control command is generated to avoid the risks of extreme operating conditions.

[0009] Further, step S1 includes:

[0010] Accelerometers were deployed at the point of maximum bending response at the top of the tower, which was defined as the plane of maximum displacement response in the tower's structural dynamics model.

[0011] Multi-axial acceleration time history signals were acquired at a sampling rate exceeding the highest frequency of the fundamental harmonic components of the tower.

[0012] A reference coordinate system is established based on the main direction of the tower bending vibration, and the multi-axial acceleration time history signals are synthesized into a radially dominant vibration component.

[0013] The radial vibration component is used as the input source for kinematic transformation to generate displacement time history data of S1.

[0014] Furthermore, S2 includes:

[0015] The sway displacement time history data S(t) obtained in step S1 is subjected to drift suppression processing to generate an optimized displacement signal S'(t) and eliminate the integral accumulation error.

[0016] The absolute maximum value of the optimized displacement signal S'(t) is determined as the sway amplitude A, which characterizes the maximum dynamic response of the tower.

[0017] Based on the theory of mechanics of materials, the stress amplitude at the bottom flange of the tower is calculated using the bending stress formula: = (E ·A) / I;

[0018] Introducing a deformation compensation factor δ to dynamically correct the moment of inertia of the cross section:

[0019] I* = I · (1 + δ);

[0020] Using the corrected stress amplitude = (E · A) / I* ;

[0021] The deformation compensation factor δ is determined by the following functional relationship:

[0022] δ= α_T · ΔT + β_L · (F / F_y)

[0023] Where: α_T is the material thermal expansion correction coefficient, ΔT is the maximum temperature gradient difference between the inner and outer walls of the tower monitored in real time; β_L is the load state correction coefficient; F is the equivalent bending moment load currently borne by the tower section; F_y is the ultimate bending moment load allowed by the tower design.

[0024] Furthermore, the step based on the stress amplitude Matching the corresponding SN curve, the percentage of damage caused by a single sway is determined, including:

[0025] S401, using a predetermined high survival rate standard SN curve as a reference curve, based on the stress amplitude. Query the SN curve database, including:

[0026] when When ≤ σ_D, take the corresponding fatigue limit cycle number N_D;

[0027] when When σ_D > , the slope k is calculated according to the power law relationship: N = N_D · (σ_D / )^k;

[0028] S402, the number of cycles is corrected by the weld quality coefficient α: N' = N · α, and the proportion of damage per single shaking is calculated as d = 1 / N'.

[0029] Where σ_D is the fatigue limit stress amplitude; N_D is the fatigue limit cycle number; and k is the slope of the high stress zone.

[0030] Furthermore, the annual cumulative damage value D, which is statistically analyzed based on operating conditions, includes:

[0031] S501 classifies wind turbine operating conditions into multiple operating types based on wind speed level thresholds defined by international standards.

[0032] S502, for each working condition i:

[0033] Get the number of valid shaking times per year and the average stress amplitude under working condition i ,according to Obtain the corresponding single-instance damage ratio from the SN curve model. Calculate the annual cumulative damage under a single operating condition: ;

[0034] S503, the total annual cumulative damage value D is calculated using the composite formula:

[0035] ;

[0036] Where Σ represents summation over all operating conditions, is the weighting coefficient for operating condition i.

[0037] Furthermore, the number of effective swaying events per year The statistics are achieved through a joint time-frequency domain detection algorithm, including:

[0038] S601, acquire the acceleration signal with a time window of length T, where T≥1000 seconds and includes the complete operating cycle;

[0039] S602, Perform a fast Fourier transform on the acceleration signal to identify all peak points in the spectrum whose amplitude exceeds three times the standard deviation of the baseline noise;

[0040] S603, when the signal-to-noise ratio at any peak point satisfies the following formula, it is determined to be an effective oscillation frequency component:

[0041]

[0042] Where: P peak P represents the peak acceleration amplitude. mean Γ represents the average acceleration amplitude across the entire frequency band, and Γ is the preset threshold.

[0043] S604, the frequency corresponding to the peak point that satisfies step S603 is recorded as the effective oscillation frequency F;

[0044] S605, calculate the number of swaying events within the time period based on the time window length T and the effective swaying frequency F: n = T×F;

[0045] S606, based on the proportion η of the time window in the whole year, convert n into the number of annual fluctuations: = n·(365×24×3600) / (T·η).

[0046] Furthermore, S4 includes:

[0047] Establish the remaining life index model R_life = 1 - (D × L), where L is the preset design life years;

[0048] Triggering logic for implementing a Level 3 early warning response:

[0049] When R_life is in the first warning interval, a health status report containing the spatial distribution of damage is generated;

[0050] When R_life drops to the second warning range, a power limiting command is sent to the wind turbine control system;

[0051] When R_life enters the third warning zone, the emergency shutdown protocol is triggered;

[0052] The warning intervals satisfy the following conditions: lower limit of the first warning interval > lower limit of the second warning interval > lower limit of the third warning interval;

[0053] The execution of the fan operation control commands satisfies:

[0054] P_output ≤ min(P_rated, κ(R) · P_max)

[0055] Where P_output is the real-time output power of the wind turbine, P_rated is the rated power of the wind turbine, P_max is the theoretical maximum capture power at the current wind speed, κ(R) is the safety margin coefficient, and is a monotonically decreasing function of the remaining life index R_life.

[0056] The rule for determining the safety margin coefficient κ(R) is as follows:

[0057] κ(R) = κ_min + (κ_max - κ_min) · R_life;

[0058] Where κ_max and κ_min are preset coefficients and satisfy 0 < κ_min ≤ κ(R)≤κ_max < 1.

[0059] Furthermore, the execution strategy of the wind turbine operation control command and emergency shutdown protocol is dynamically adapted according to the wind turbine operating status, including:

[0060] When a power limiting command is triggered, a differentiated control path is selected based on the current wind speed conditions, including:

[0061] If the wind turbine is in extreme wind conditions, activate the active yaw system to deviate from the prevailing wind direction and reduce the load on the tower.

[0062] If the wind is strong, reduce the generator torque output to the sub-rated operating range so that the output power does not exceed the current wind energy capture limit;

[0063] When an emergency stop protocol is triggered, a tiered braking strategy is implemented, including:

[0064] Prioritize the use of pneumatic braking, and adjust the blades to a safe position via the pitch control system;

[0065] When the pneumatic brake fails or the tower sway exceeds the critical threshold, the mechanical braking system is triggered.

[0066] The control in the sub-rated operating range must meet power constraints:

[0067] P_lim = min(P_rated, k_safe · P_max(wind));

[0068] Where k_safe is the safety factor for strong wind conditions, and P_max(wind) is the theoretical maximum capture power at the current wind speed.

[0069] The angular offset Δθ of active yaw control is dynamically calculated based on the tower's natural frequency f_n and wind speed v:

[0070] ;

[0071] Where v_rated is the rated wind speed and f is the currently monitored sway frequency.

[0072] Furthermore, the method also includes: introducing a full life-cycle damage correction model in step S3, and dynamically updating the annual cumulative damage value through the following logic:

[0073] Establish environment-structure degradation coupling factor:

[0074] ξ = β · γ;

[0075] Wherein: β is the weld degradation factor, obtained through regression analysis of historical nondestructive testing data, characterizing the fatigue crack propagation rate of the weld; γ is the environmental corrosion factor, calculated based on real-time monitoring data from salt spray concentration and humidity sensors on the tower surface.

[0076] The generation of the weld degradation factor β includes:

[0077] Extract the defect growth rate Δd / Δt from the three most recent nondestructive testing reports, and apply it through the crack propagation equation β ∝(Δd / Δt)·( The calculation is performed using / E)^m, where m is a material constant and d is the percentage of damage caused by a single shaking event.

[0078] The quantification of environmental corrosion factor γ includes:

[0079] γ = 1 + k_corr·(C / )^n;

[0080] Where: C is the real-time salt spray concentration (mg / m³). The baseline corrosion concentration is given by k_corr, where k is the environmental sensitivity coefficient and n is the corrosion nonlinearity exponent.

[0081] Final revised annual damage accumulation:

[0082] D* = D · ξ;

[0083] The process of feeding D* back into the remaining lifespan index R_life is then used in the calculation.

[0084] As a second aspect of the present invention, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores a computer program, which, when executed by a computer, causes the computer to perform the wind turbine tower life monitoring method as described in any of the preceding claims.

[0085] The present invention has the following beneficial effects:

[0086] This invention directly generates sway displacement time history data through the double integration of acceleration signals, avoiding the complex installation problem of traditional displacement sensors. It achieves this by controlling the stress amplitude σ. a By matching the SN curve and combining the annual cumulative damage value D according to the classification of working conditions, a quantitative assessment of life loss can be achieved. When D×L (life years) reaches the threshold, an operation control command is automatically generated to avoid the risks of extreme working conditions in advance and extend the tower life. Attached Figure Description

[0087] Figure 1 This is a flowchart illustrating a wind turbine tower life monitoring method provided in an embodiment of the present invention. Detailed Implementation

[0088] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0089] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0090] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0091] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0092] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0093] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0094] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0095] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a method for monitoring the lifespan of wind turbine towers. Figure 1 A flowchart illustrating a wind turbine tower life monitoring method provided in this embodiment of the invention includes:

[0096] S1: Real-time acquisition of acceleration signals at the top of the tower, and secondary integration and filtering of the acceleration signals to generate sway displacement time history data S(t);

[0097] S2: Calculate the sway amplitude A based on the maximum displacement value of the sway displacement time history data S(t), and combine it with the material elastic modulus E of the wind turbine tower and the moment of inertia I of the structural section, according to the stress transformation model. = (E · A) / I Calculate the stress amplitude at the bottom flange of the tower. ;

[0098] S3: Based on the stress amplitude Match the corresponding SN curve to determine the proportion of damage caused by a single shaking, and statistically analyze the annual cumulative damage value D based on the working condition classification;

[0099] S4: When the product of the annual cumulative damage value D and the design life L reaches the preset life loss threshold, a wind turbine operation control command is generated to avoid the risks of extreme operating conditions.

[0100] This invention directly generates sway displacement time history data through the double integration of acceleration signals, avoiding the complex installation problem of traditional displacement sensors. It achieves this by controlling the stress amplitude σ. a By matching the SN curve and combining the annual cumulative damage value D according to the classification of working conditions, a quantitative assessment of life loss can be achieved. When D×L (life years) reaches the threshold, an operation control command is automatically generated to avoid the risks of extreme working conditions in advance and extend the tower life.

[0101] In some embodiments, step S1 includes:

[0102] Accelerometers were deployed at the point of maximum bending response at the top of the tower, which was defined as the plane of maximum displacement response in the tower's structural dynamics model.

[0103] Multi-axial acceleration time history signals were acquired at a sampling rate exceeding the highest frequency of the fundamental harmonic components of the tower.

[0104] A reference coordinate system is established based on the main direction of the tower bending vibration, and the multi-axial acceleration time history signals are synthesized into a radially dominant vibration component, including;

[0105] 1) Based on the vibration direction of the first bending mode of the tower, establish a cylindrical coordinate system with the tower axis as the reference;

[0106] 2) The X / Y / Z Cartesian coordinate system signals acquired by the sensor are converted into radial, tangential, and axial components through a coordinate transformation matrix;

[0107] 3) Extract the radial component as the dominant vibration signal. This component is perpendicular to the tower axis and characterizes the vibration energy related to bending stress.

[0108] The radial vibration component is used as the input source for kinematic transformation to generate displacement time history data of S1.

[0109] Specifically, the radial vibration component is used as the input source for kinematic transformation, and displacement time history data is generated through the following processing:

[0110] 1) Perform frequency domain integration on the radial acceleration signal to obtain the radial velocity time history;

[0111] 2) Integrate the radial velocity time history twice to obtain the radial displacement time history;

[0112] 3) Filter out the DC offset component in the displacement time history and output the optimized displacement time history data.

[0113] In the above embodiments, the sensor is positioned on the plane of maximum displacement response at the top of the tower to ensure the capture of the dominant vibration component. The radial dominant vibration component is synthesized through multi-axial signals to eliminate noise interference in non-dominant directions and improve the accuracy of displacement calculation. Data is collected at a sampling rate exceeding the highest frequency of the tower's fundamental harmonics to avoid signal distortion caused by frequency aliasing.

[0114] In some embodiments, S2 includes:

[0115] The sway displacement time history data S(t) obtained in step S1 is subjected to drift suppression processing to generate an optimized displacement signal S'(t) and eliminate the integral accumulation error.

[0116] The absolute maximum value of the optimized displacement signal S'(t) is determined as the sway amplitude A, which characterizes the maximum dynamic response of the tower.

[0117] Based on the theory of mechanics of materials, the stress amplitude at the bottom flange of the tower is calculated using the bending stress formula: = (E ·A) / I;

[0118] Introducing a deformation compensation factor δ to dynamically correct the moment of inertia of the cross section:

[0119] I* = I · (1 + δ);

[0120] Using the corrected stress amplitude = (E · A) / I* ;

[0121] The deformation compensation factor δ is determined by the following functional relationship:

[0122] δ= α_T · ΔT + β_L · (F / F_y)

[0123] Where: α_T is the material thermal expansion correction coefficient, which is determined by the linear expansion coefficient of the tower steel; ΔT is the maximum temperature gradient difference between the inner and outer walls of the tower monitored in real time; β_L is the load state correction coefficient, which is related to the yield strength of the material; F is the equivalent bending moment load currently borne by the tower section; and F_y is the ultimate bending moment load allowed by the tower design.

[0124] In the above embodiments, the moment of inertia of the cross section is dynamically corrected by introducing a deformation compensation factor δ. To address the material deformation error caused by temperature gradient ΔT and load F, the method is δ=αT. ΔT+βL (F / Fy) correlates material thermal expansion and yield strength, improving stress calculation. = (E · A) / I*) engineering applicability.

[0125] In some embodiments, the stress amplitude is determined according to the stress amplitude. Matching the corresponding SN curve, the percentage of damage caused by a single sway is determined, including:

[0126] S401, a standard SN curve with a predetermined high survival rate is used as the reference curve (this invention selects a curve with a 95% survival rate; the SN curve (Stress-Number Curve) is the existing fundamental theory in the field of material fatigue), based on the stress amplitude. Query the SN curve database, including:

[0127] when When ≤ σ_D, take the corresponding fatigue limit cycle number N_D;

[0128] when When σ_D > , the slope k is calculated according to the power law relationship: N = N_D · (σ_D / )^k;

[0129] S402, the number of cycles is corrected by the weld quality coefficient α: N' = N · α, and the proportion of damage per single shaking is calculated as d = 1 / N'.

[0130] Where σ_D is the fatigue limit stress amplitude; N_D is the fatigue limit cycle number; k is the slope of the high stress zone; and α is assigned a value according to the ISO 5817 weld inspection standard.

[0131] For example, to find the SN curve parameters: σ_D=220MPa, N_D= k=3.2;

[0132] judge (300)>σ_D(220), enable power-law calculation:

[0133] N = × (220 / 300)^3.2 ≈ 2.1× Second-rate;

[0134] The weld inspection is classified as Level II, corresponding to α=1.0, resulting in N'=2.1× ;

[0135] Calculate single-injury damage: d = 1 / (2.1 × )≈4.76× .

[0136] In the above embodiments, a dual-mode query SN curve is used. In the low-stress region (σa≤σD), the fatigue limit cycle number ND is directly taken; in the high-stress region (…),… > σ_D) According to the power-law relationship N = N_D (σ_D / The calculation, covering the entire stress range, is performed using the weld coefficient a (ISO 5817 standard classification) to correct for the number of cycles (N′=N). a), making the proportion of single damage d=1 / N′ more consistent with actual structural defects.

[0137] In some embodiments, the statistical annual cumulative damage value D based on operating condition classification includes:

[0138] S501 classifies wind turbine operating status into multiple operating conditions based on wind speed level thresholds defined by international standards. Each type of operating condition includes at least normal wind speed condition, strong wind condition, and extreme wind speed condition. Operating condition identification is based on a joint determination of real-time wind speed data and sway frequency characteristics.

[0139] S502, for each working condition i:

[0140] Get the number of valid shaking times per year and the average stress amplitude under working condition i ,according to Obtain the corresponding single-instance damage ratio from the SN curve model. Calculate the annual cumulative damage under a single operating condition: ;

[0141] S503, the total annual cumulative damage value D is calculated using the composite formula:

[0142] ;

[0143] Where Σ represents summation over all operating conditions, is the weighting coefficient for operating condition i.

[0144] The average stress amplitude The value selection rule is as follows: the stress amplitude of all effective swaying events under various working conditions is taken as a weighted average, and the weight is positively correlated with the swaying amplitude. The weighted average algorithm satisfies:

[0145] ;

[0146] in, Let be the displacement amplitude of the j-th swaying event. Let be the stress amplitude corresponding to the j-th swaying event.

[0147] For example:

[0148] Operating Condition ①: Wind Speed ​​Range (Normal wind speed);

[0149] Operating Condition 2: Wind Speed ​​Range (Strong winds);

[0150] Operating Condition 3: Wind Speed ​​> (Extreme wind speed);

[0151] Single-condition damage calculation: Perform the following for each condition i:

[0152] Get the number of valid shaking times per year Calculate the average stress amplitude under the working condition. Determine the percentage of single injuries Calculate annual damage under a single operating condition: ;

[0153] Total damage synthesis: ;

[0154] in The working condition weighting coefficient satisfies ,For example =1.0 (normal operating conditions) =1.2 (strong wind conditions, considering gust effects). =1.5 (Extreme operating conditions, considering turbulence enhancement).

[0155] In the above embodiments, the operating condition type (such as strong wind / extreme wind speed) is identified by combining wind speed level and sway frequency characteristics, avoiding the limitations of a single wind speed criterion. The total damage D = ∑(γi Di), where Di = ni × di, assigns weights (γ) to different operating conditions. i This reflects the differences in contributions from various operating conditions in actual operation.

[0156] In some embodiments, the number of effective swaying events per year The statistics are achieved through a joint time-frequency domain detection algorithm, including:

[0157] S601, acquire the acceleration signal with a time window of length T, where T≥1000 seconds and includes the complete operating cycle;

[0158] S602, Perform a Fast Fourier Transform (FFT) on the acceleration signal to identify all peak points in the spectrum whose amplitude exceeds three times the standard deviation of the baseline noise;

[0159] S603, when the signal-to-noise ratio (SNR) at any peak point satisfies the following formula, it is determined to be an effective oscillation frequency component:

[0160]

[0161] Where: P peak P represents the peak acceleration amplitude. mean Γ represents the average acceleration amplitude across the entire frequency band, and Γ is a preset threshold with a value range of [16dB, 20dB].

[0162] S604, the frequency corresponding to the peak point that satisfies step S603 is recorded as the effective oscillation frequency F;

[0163] S605, calculate the number of oscillations within the time period based on the time window length T and the effective oscillation frequency F: n = T×F; (T is seconds, F is Hz, n is the total number of times)

[0164] S606, based on the proportion η of the time window in the whole year, convert n into the number of annual fluctuations: = n (365×24×3600) / (T η).

[0165] In the above embodiments, the effective sway frequency component (F) is screened by the signal-to-noise ratio threshold (Γ∈[16dB,20dB]) to eliminate environmental noise interference. Based on the time window ratio η, the number of short-term sway times n is converted into the number of effective times ni per year, thus solving the data processing burden of continuous monitoring throughout the year.

[0166] In some embodiments, S4 includes:

[0167] Establish the remaining life index model R_life = 1 - (D × L), where L is the preset design life years;

[0168] Triggering logic for implementing a Level 3 early warning response:

[0169] When R_life is in the first warning interval, a health status report containing the spatial distribution of damage is generated;

[0170] When R_life drops to the second warning range, a power limiting command is sent to the wind turbine control system;

[0171] When R_life enters the third warning zone, the emergency shutdown protocol is triggered;

[0172] The warning intervals satisfy the following conditions: lower limit of the first warning interval > lower limit of the second warning interval > lower limit of the third warning interval;

[0173] The execution of the fan operation control commands satisfies:

[0174] P_output ≤ min(P_rated, κ(R) P_max)

[0175] Where P_output is the real-time output power of the wind turbine, P_rated is the rated power of the wind turbine (design maximum continuous output power), P_max is the theoretical maximum capture power at the current wind speed, κ(R) is the safety margin coefficient, and is a monotonically decreasing function of the remaining life index R_life.

[0176] The rule for determining the safety margin coefficient κ(R) is as follows:

[0177] κ(R) = κ_min + (κ_max - κ_min) · R_life

[0178] Where κ_max and κ_min are preset coefficients and satisfy 0 < κ_min ≤ κ(R)≤κ_max < 1.

[0179] In the above embodiments, the remaining lifetime model is: R_life = 1 - D × L, which quantifies lifetime loss and triggers a graded response: Level 1 warning generates a damage report, Level 2 warning limits power, and Level 3 warning triggers an emergency shutdown. The safety margin coefficient κ(R) dynamically decreases with R_life (κ(R) = κ_min + (κ_max - κ_min)). R_life), balancing power generation efficiency and structural safety.

[0180] In some embodiments, the execution strategy of the wind turbine operation control command and emergency shutdown protocol is dynamically adapted according to the wind turbine operating status, including:

[0181] When a power limiting command is triggered, a differentiated control path is selected based on the current wind speed conditions, including:

[0182] If the wind turbine is in extreme wind conditions, activate the active yaw system to deviate from the prevailing wind direction and reduce the load on the tower.

[0183] If the wind is strong, reduce the generator torque output to the sub-rated operating range so that the output power does not exceed the current wind energy capture limit;

[0184] When an emergency stop protocol is triggered, a tiered braking strategy is implemented, including:

[0185] Prioritize the use of pneumatic braking, and adjust the blades to a safe position via the pitch control system;

[0186] When the pneumatic brake fails or the tower sway exceeds the critical threshold, the mechanical braking system is triggered.

[0187] The control in the sub-rated operating range must meet power constraints:

[0188] P_lim = min(P_rated, k_safe · P_max(wind));

[0189] Where k_safe is the safety factor for strong wind conditions, and P_max(wind) is the theoretical maximum capture power at the current wind speed.

[0190] The angular offset Δθ of active yaw control is dynamically calculated based on the tower's natural frequency f_n and wind speed v:

[0191] Δθ ∝ (v / v_rated) · (f / f_n)²;

[0192] Where v_rated is the rated wind speed and f is the currently monitored sway frequency.

[0193] In the above embodiments, active yaw at extreme wind speeds (Δθ ∝ (v / v_rated) · (f / f_n)²) reduces the load, and power is limited to the sub-rated range under strong wind conditions (P_lim = min(P_rated, k_safe · P_max(wind))). Staged braking prioritizes pneumatic braking, with mechanical braking as a backup, to reduce the impact of emergency stops on the tower.

[0194] In some embodiments, the method further includes: introducing a full lifecycle damage correction model in step S3, and dynamically updating the annual cumulative damage value through the following logic:

[0195] Establish environment-structure degradation coupling factor:

[0196] ξ = β ·γ_env

[0197] Wherein: β is the weld degradation factor, obtained through regression analysis of historical nondestructive testing data, characterizing the fatigue crack propagation rate of the weld; γ_env is the environmental corrosion factor, calculated based on real-time monitoring data from salt spray concentration and humidity sensors on the tower surface.

[0198] The generation of the weld degradation factor β includes:

[0199] Extract the defect growth rate Δd / Δt from the three most recent nondestructive testing reports, and apply it through the crack propagation equation β ∝(Δd / Δt)·( / E)^m is calculated, where m is a material constant, β takes the default baseline value when there is no detection data, and d is the percentage of damage caused by a single shaking.

[0200] The quantification of the environmental corrosion factor γ_env includes:

[0201] γ_env = 1 + k_corr·(C / )^n;

[0202] Where: C is the real-time salt spray concentration (mg / m³). The baseline corrosion concentration is given by k_corr, where k is the environmental sensitivity coefficient and n is the corrosion nonlinearity exponent.

[0203] Final revised annual damage accumulation:

[0204] D* = D ξ;

[0205] The process of feeding D* back into the remaining lifespan index R_life is then used in the calculation.

[0206] In the above embodiment, through the factor ξ=β γ_env dynamically corrects annual damage values ​​(D∗=D ξ), where β (weld degradation factor) is based on nondestructive testing data to regress crack propagation rate; γ_env (environmental corrosion factor) is calculated from salt spray concentration (C) (γ_env = 1 + k_corr·(C / By fusing structural degradation and environmental data in real time, the reliability of long-term predictions is improved.

[0207] This invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the wind turbine tower life monitoring methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0208] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described wind turbine tower life monitoring method.

[0209] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0210] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0211] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0212] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0213] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0214] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0215] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0216] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0217] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0218] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

[0219] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the lifespan of a wind turbine tower, characterized in that, include: S1: Real-time acquisition of acceleration signals at the top of the tower, and secondary integration and filtering of the acceleration signals to generate sway displacement time history data S(t); S2: Calculate the sway amplitude A based on the maximum displacement value of the sway displacement time history data S(t), and combine it with the material elastic modulus E of the wind turbine tower and the moment of inertia I of the structural section, according to the stress transformation model. = (E · A) / I Calculate the stress amplitude at the bottom flange of the tower. ; S3: Based on the stress amplitude Match the corresponding SN curve to determine the proportion of damage caused by a single shaking, and statistically analyze the annual cumulative damage value D based on the working condition classification; S4: When the product of the annual cumulative damage value D and the design life L reaches the preset life loss threshold, a wind turbine operation control command is generated to avoid the risks of extreme operating conditions.

2. The wind turbine tower life monitoring method according to claim 1, characterized in that, Step S1 includes: Accelerometers were deployed at the point of maximum bending response at the top of the tower, which was defined as the plane of maximum displacement response in the tower's structural dynamics model. Multi-axial acceleration time history signals were acquired at a sampling rate exceeding the highest frequency of the fundamental harmonic components of the tower. A reference coordinate system is established based on the main direction of the tower bending vibration, and the multi-axial acceleration time history signals are synthesized into a radially dominant vibration component. The radial vibration component is used as the input source for kinematic transformation to generate displacement time history data of S1.

3. The wind turbine tower life monitoring method according to claim 1, characterized in that, S2 include: The sway displacement time history data S(t) obtained in step S1 is subjected to drift suppression processing to generate an optimized displacement signal S'(t) and eliminate the integral accumulation error. The absolute maximum value of the optimized displacement signal S'(t) is determined as the sway amplitude A, which characterizes the maximum dynamic response of the tower. Based on the theory of mechanics of materials, the stress amplitude at the bottom flange of the tower is calculated using the bending stress formula: = (E · A) / I; Introducing a deformation compensation factor δ to dynamically correct the moment of inertia of the cross section: I* = I · (1 + δ); Using the corrected stress amplitude = (E · A) / I* ; The deformation compensation factor δ is determined by the following functional relationship: δ= α_T · ΔT + β_L · (F / F_y) Where: α_T is the material thermal expansion correction coefficient, ΔT is the maximum temperature gradient difference between the inner and outer walls of the tower monitored in real time; β_L is the load state correction coefficient; F is the equivalent bending moment load currently borne by the tower section; F_y is the ultimate bending moment load allowed by the tower design.

4. The method for monitoring the lifespan of a wind turbine tower according to claim 1, characterized in that, According to the stress amplitude Matching the corresponding SN curve, the percentage of damage caused by a single sway is determined, including: S401, using a predetermined high survival rate standard SN curve as a reference curve, based on the stress amplitude. Query the SN curve database, including: when When ≤ σ_D, take the corresponding fatigue limit cycle number N_D; when When σ_D > , the slope k is calculated according to the power law relationship: N = N_D · (σ_D / )^k; S402, the number of cycles is corrected by the weld quality coefficient α: N' = N · α, and the proportion of damage per single shaking is calculated as d = 1 / N'. Where σ_D is the fatigue limit stress amplitude; N_D is the fatigue limit cycle number; and k is the slope of the high stress zone.

5. The method for monitoring the lifespan of a wind turbine tower according to claim 1, characterized in that, The annual cumulative damage value D, which is classified and statistically analyzed based on working conditions, includes: S501 classifies wind turbine operating conditions into multiple operating types based on wind speed level thresholds defined by international standards. S502, for each working condition i: Get the number of valid shaking times per year and the average stress amplitude under working condition i ,according to Obtain the corresponding single-instance damage ratio from the SN curve model. Calculate the annual cumulative damage under a single operating condition: ; S503, the total annual cumulative damage value D is calculated using the composite formula: ； Where Σ represents summation over all operating conditions, is the weighting coefficient for operating condition i.

6. The wind turbine tower life monitoring method according to claim 5, characterized in that, Annual effective shaking count The statistics are achieved through a joint time-frequency domain detection algorithm, including: S601, acquire the acceleration signal with a time window of length T, where T≥1000 seconds and includes the complete operating cycle; S602, Perform a fast Fourier transform on the acceleration signal to identify all peak points in the spectrum whose amplitude exceeds three times the standard deviation of the baseline noise; S603, when the signal-to-noise ratio at any peak point satisfies the following formula, it is determined to be an effective oscillation frequency component: SNR = (P peak / P mean ) ≥ Γ Where: P peak P represents the peak acceleration amplitude. mean Γ represents the average acceleration amplitude across the entire frequency band, and Γ is the preset threshold. S604, the frequency corresponding to the peak point that satisfies step S603 is recorded as the effective oscillation frequency F; S605, calculate the number of swaying events within the time period based on the time window length T and the effective swaying frequency F: n = T×F; S606, based on the proportion η of the time window in the whole year, convert n into the number of annual fluctuations: = n·(365×24×3600) / (T·η).

7. The wind turbine tower life monitoring method according to claim 1, characterized in that, S4 include: Establish the remaining life index model R_life = 1 - (D × L), where L is the preset design life years; Triggering logic for implementing a Level 3 early warning response: When R_life is in the first warning interval, a health status report containing the spatial distribution of damage is generated; When R_life drops to the second warning range, a power limiting command is sent to the wind turbine control system; When R_life enters the third warning zone, the emergency shutdown protocol is triggered; The warning intervals satisfy the following conditions: lower limit of the first warning interval > lower limit of the second warning interval > lower limit of the third warning interval; The execution of the fan operation control commands satisfies: P_output ≤ min(P_rated, κ(R) · P_max); Where P_output is the real-time output power of the wind turbine, P_rated is the rated power of the wind turbine, P_max is the theoretical maximum capture power at the current wind speed, κ(R) is the safety margin coefficient, and is a monotonically decreasing function of the remaining life index R_life. The rule for determining the safety margin coefficient κ(R) is as follows: κ(R) = κ_min + (κ_max - κ_min) · R_life; Where κ_max and κ_min are preset coefficients and satisfy 0 < κ_min ≤ κ(R)≤κ_max < 1.

8. The method for monitoring the lifespan of a wind turbine tower according to claim 7, characterized in that, The execution strategy of the wind turbine operation control commands and emergency shutdown protocol is dynamically adapted according to the wind turbine operating status, including: When a power limiting command is triggered, a differentiated control path is selected based on the current wind speed conditions, including: If the wind turbine is in extreme wind conditions, activate the active yaw system to deviate from the prevailing wind direction and reduce the load on the tower. If the wind is strong, reduce the generator torque output to the sub-rated operating range so that the output power does not exceed the current wind energy capture limit; When an emergency stop protocol is triggered, a tiered braking strategy is implemented, including: Prioritize the use of pneumatic braking, and adjust the blades to a safe position via the pitch control system; When the pneumatic brake fails or the tower sway exceeds the critical threshold, the mechanical braking system is triggered. The control in the sub-rated operating range must meet power constraints: P_lim = min(P_rated, k_safe · P_max(wind)); Where k_safe is the safety factor for strong wind conditions, and P_max(wind) is the theoretical maximum capture power at the current wind speed. The angular offset Δθ of active yaw control is dynamically calculated based on the tower's natural frequency f_n and wind speed v: Δθ ∝ (v / v_rated) · (f / f_n)²; Where v_rated is the rated wind speed and f is the currently monitored sway frequency.

9. The method as described in claim 7, characterized in that, The method further includes: introducing a full life-cycle damage correction model in step S3, and dynamically updating the annual cumulative damage value through the following logic: Establish environment-structure degradation coupling factor: ξ = β c; Wherein: β is the weld degradation factor, obtained through regression analysis of historical nondestructive testing data, characterizing the fatigue crack propagation rate of the weld; γ is the environmental corrosion factor, calculated based on real-time monitoring data from salt spray concentration and humidity sensors on the tower surface. The generation of the weld degradation factor β includes: Extract the defect growth rate Δd / Δt from the three most recent nondestructive testing reports, and apply it using the crack propagation equation β ∝ (Δd / Δt). ( The calculation is performed using / E)^m, where m is a material constant and d is the percentage of damage caused by a single shaking event. The quantification of environmental corrosion factor γ includes: γ = 1 + k_corr·(C / )^n; Where: C is the real-time salt spray concentration (mg / m³). The baseline corrosion concentration is given by k_corr, where k is the environmental sensitivity coefficient and n is the corrosion nonlinearity exponent. Final revised annual damage accumulation: D* = D ξ; The process of feeding D* back into the remaining lifespan index R_life is then used in the calculation.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a computer, causes the computer to perform the wind turbine tower life monitoring method as described in any one of claims 1 to 9.

Images