Offshore wind power equipment inspection method and device, electronic equipment and storage medium

Abstract

The invention discloses an offshore wind power equipment inspection method and device, electronic equipment and a storage medium, and belongs to the technical field of offshore wind power inspection. Obtaining a blade vibration time domain signal of target wind power equipment in the wind power equipment cluster, an inflow wind speed of upstream wind power equipment, a wake wind speed of downstream wind power equipment, an instantaneous wind speed value of the target wind power equipment, operation data of the target wind power equipment and an ocean current speed of the target wind power equipment; determining a turbulence dominant frequency according to the instantaneous wind speed value, the inflow wind speed and the wake wind speed; determining wind speed loss according to the inflow wind speed, the equipment operation data and the ocean current speed; and determining a vibration incentive result according to the blade vibration time domain signal, the inflow wind speed, the turbulence intensity, the turbulence dominant frequency and the wind speed loss, and performing inspection. Through analysis in combination with turbulence intensity, wind speed loss and the like, accuracy of vibration inducement judgment is improved, polling work is more targeted, waste caused by blind polling is reduced, polling efficiency is improved, and cost is reduced.

Description

Technical Field

[0001] This application belongs to the field of offshore wind power inspection, specifically relating to an offshore wind power equipment inspection method, an offshore wind power equipment inspection device, electronic equipment, and a computer-readable storage medium. Background Technology

[0002] Offshore wind power refers to wind power generation projects built in marine environments (including nearshore, offshore, and intertidal zones). Wind energy is converted into electrical energy through wind turbine generators on the sea surface and transmitted to the land power grid via submarine cables. Offshore wind power typically adopts a cluster layout: dozens or even hundreds of wind turbines are densely arranged at intervals of about 800 meters to form a large-scale wind farm.

[0003] Compared to onshore wind power, offshore wind power has more abundant and stable wind energy resources. However, the special characteristics of the marine environment (such as high salt spray, strong typhoons, and wave impact) pose more severe challenges to its equipment. Therefore, the inspection of offshore wind power is very important. Abnormal vibration (such as high-frequency blade vibration) is a significant precursor to damage to offshore wind turbines. Prolonged vibration accelerates material fatigue, leading to problems like blade crack propagation and loose bolts. The causes of abnormal vibration fall into two categories: first, equipment malfunctions, such as blade manufacturing defects or bearing wear; and second, the wake effect, a phenomenon unique to clustered wind farms. When airflow passes through the blades of an upstream turbine, the blade rotation consumes some wind energy, creating a low-speed zone downstream. Simultaneously, vortices are generated at the blade trailing edges, causing strong turbulent pulsations (i.e., wakes). Due to the close proximity of the turbines, downstream turbines are often directly within the wake coverage area of ​​upstream turbines, continuously experiencing turbulent impacts. When the turbulent pulsation frequency of the wake approaches the natural frequency of the blades, resonance may occur, leading to a sudden increase in vibration amplitude and ultimately, turbine damage.

[0004] Current inspection methods typically involve using drones to capture images of the blades, manually inspecting nacelle components from the tower, and using sensors to monitor vibration and temperature. However, existing inspection technologies can only detect abnormal vibrations but cannot distinguish their specific causes. This can easily lead to misdiagnosis of the root cause of the fault. For example, vibrations caused by wake currents may be mistaken for defects in the equipment itself and repaired blindly (such as replacing faulty blades). This results in wasted costs and missed detections. If abnormal vibrations caused by wake currents are not identified for a long time, it may lead to mass damage to wind turbines. Summary of the Invention

[0005] The purpose of this application is to provide a method for inspecting offshore wind power equipment, an offshore wind power equipment inspection device, electronic equipment, and a corresponding computer-readable storage medium, which can solve the problem that existing inspection technologies can only detect vibration abnormalities but cannot distinguish their specific causes.

[0006] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a method for inspecting offshore wind power equipment, the method comprising: The following data are collected: blade vibration time-domain signal of the target wind turbine in the wind turbine cluster; inflow wind speed at the upstream wind turbine; wake wind speed at the downstream wind turbine; instantaneous wind speed at the target wind turbine; operating data of the target wind turbine; and ocean current speed at the target wind turbine. The turbulence intensity at the target wind turbine is determined based on the instantaneous wind speed and the inflow wind speed, and the dominant turbulence frequency at the target wind turbine is determined based on the turbulence intensity and the wake wind speed. The wind speed deficit is determined based on the inflow wind speed, the equipment operating data, and the ocean current speed. The vibration inducing factors are determined based on the blade vibration time-domain signal, the inflow wind speed, the turbulence intensity, the turbulence dominant frequency, and the wind speed deficit. Based on the vibration induction results, the target wind power equipment is inspected.

[0007] Optionally, determining the turbulence intensity at the target wind turbine based on the instantaneous wind speed value and the inflow wind speed includes: Based on the instantaneous wind speed value, determine the average wind speed at the target wind power equipment; Based on the instantaneous wind speed and the average wind speed, the pulsating wind speed at the target wind power equipment is determined; The turbulence intensity at the target wind turbine is determined based on the pulsating wind speed and the inflow wind speed.

[0008] Optionally, determining the wind speed loss based on the inflow wind speed, the equipment operating data, and the ocean current speed includes: The upstream fan thrust coefficient is determined based on the inflow velocity and the equipment operating data. Determine the ocean current correction factor based on the inflow wind speed and the ocean current velocity; The wind speed loss is determined based on the upstream wind turbine thrust coefficient and the ocean current correction coefficient.

[0009] Optionally, determining the vibration inducing factor result based on the blade vibration time-domain signal, the inflow wind speed, the turbulence intensity, the dominant turbulence frequency, and the wind speed deficit includes: The probability of wake influence is determined based on the inflow wind speed, the wind speed deficit, the turbulence intensity, and the turbulence dominant frequency. Based on the vibration time-domain signal, determine the blade vibration frequency-domain signal of the target wind turbine. Based on the vibration frequency domain signal, the probability of the target wind power equipment's own failure is obtained; The vibration induction result is determined based on the wake influence probability and the self-fault probability.

[0010] Optionally, determining the wake influence probability based on the inflow wind speed, the wind speed deficit, the turbulence intensity, and the dominant turbulence frequency includes: The inflow wind speed, wind speed deficit, turbulence intensity, and turbulence dominant frequency are input into the wake influence probability calculation model to obtain the wake influence probability. The step of obtaining the inherent failure probability of the target wind turbine based on the vibration frequency domain signal includes: The vibration frequency domain signal is input into the self-fault probability calculation model to obtain the self-fault probability.

[0011] Optionally, determining the vibration cause result based on the wake influence probability and the self-failure probability includes: If the probability of wake influence is greater than the probability of its own failure, then the vibration cause is determined to be wake vibration. If the probability of the wake effect is less than the probability of its own failure, then the vibration cause is determined to be vibration due to its own failure. If the probability of wake influence is equal to the probability of self-failure, or if the difference between the probability of wake influence and the probability of self-failure is less than a preset threshold, then the vibration cause is determined to be the combined effect of wake influence and self-failure.

[0012] Optionally, the step of inspecting the target wind turbine based on the vibration cause results includes: If the vibration is determined to be caused by wake vibration, then adjust the power of the upstream fan or the yaw angle of the downstream fan. If the vibration induced by the vibration is determined to be vibration caused by its own fault, then the type of fault source is determined. If the vibration is determined to be caused by a combination of wake effects and its own malfunction, then manual troubleshooting is recommended.

[0013] Optionally, determining the type of fault source includes: Obtain the theoretical characteristic frequencies corresponding to various fault types; In the vibration frequency domain signal, determine the amplitude corresponding to the theoretical characteristic frequency; The theoretical characteristic frequency whose amplitude is not within the preset amplitude range is identified as the fault source type.

[0014] Secondly, embodiments of this application provide an apparatus for offshore wind power equipment, the apparatus comprising: The acquisition module is used to acquire the blade vibration time-domain signal of the target wind turbine in the wind turbine cluster, the inflow wind speed at the upstream wind turbine, the wake wind speed at the downstream wind turbine, the instantaneous wind speed value at the target wind turbine, the operating data of the target wind turbine, and the ocean current speed at the target wind turbine. The turbulence dominant frequency determination module is used to determine the turbulence intensity at the target wind turbine based on the instantaneous wind speed value and the inflow wind speed, and to determine the turbulence dominant frequency at the target wind turbine based on the turbulence intensity and the wake wind speed. The wind speed loss determination module is used to determine the wind speed loss based on the inflow wind speed, the equipment operating data, and the ocean current speed. The vibration cause determination module is used to determine the vibration cause result based on the blade vibration time-domain signal, the inflow wind speed, the turbulence intensity, the turbulence dominant frequency, and the wind speed deficit. The inspection module is used to inspect the target wind power equipment based on the vibration cause results.

[0015] Optionally, the turbulence dominant frequency determination module includes: The average wind speed determination submodule is used to determine the average wind speed at the target wind power equipment based on the instantaneous wind speed value. The pulsating wind speed determination submodule is used to determine the pulsating wind speed at the target wind power equipment based on the instantaneous wind speed value and the average wind speed. The turbulence intensity determination submodule is used to determine the turbulence intensity at the target wind turbine based on the pulsating wind speed and the inflow wind speed.

[0016] Optionally, the wind speed loss determination module includes: The upstream fan thrust coefficient determination submodule is used to determine the upstream fan thrust coefficient based on the inflow velocity and the equipment operating data. The ocean current correction coefficient determination submodule is used to determine the ocean current correction coefficient based on the inflow wind speed and the ocean current velocity; The wind speed loss determination submodule is used to determine the wind speed loss based on the upstream wind turbine thrust coefficient and the ocean current correction coefficient.

[0017] Optionally, the vibration cause determination module includes: The wake influence probability determination submodule is used to determine the wake influence probability based on the inflow wind speed, the wind speed deficit, the turbulence intensity, and the turbulence dominant frequency. The vibration frequency domain signal determination submodule is used to determine the blade vibration frequency domain signal of the target wind turbine based on the vibration time domain signal. The self-fault probability determination submodule is used to obtain the self-fault probability of the target wind power equipment based on the vibration frequency domain signal. The vibration cause determination submodule is used to determine the vibration cause result based on the wake influence probability and the self-fault probability.

[0018] Optionally, the wake influence probability determination submodule includes: The wake influence probability calculation unit is used to input the inflow wind speed, the wind speed deficit, the turbulence intensity, and the turbulence dominant frequency into the wake influence probability calculation model to obtain the wake influence probability. The self-fault probability determination submodule includes: The self-fault probability calculation unit is used to input the vibration frequency domain signal into the self-fault probability calculation model to obtain the self-fault probability.

[0019] Optionally, the vibration cause determination submodule includes: A wake vibration determination unit is used to determine the vibration cause result as wake vibration if the wake influence probability is greater than the self-fault probability. The self-fault vibration determination unit is used to determine the vibration cause result as self-fault vibration if the wake influence probability is less than the self-fault probability. The combined effect determination unit is used to determine that the vibration cause is the combined effect of wake influence and its own fault if the wake influence probability is equal to the self-fault probability, or if the difference between the wake influence probability and the self-fault probability is less than a preset threshold.

[0020] Optionally, the inspection module includes: The adjustment submodule is used to adjust the power of the upstream fan or the yaw angle of the downstream fan if the vibration cause is determined to be wake vibration. The fault source type determination submodule is used to determine the fault source type if the vibration inducing result is determined to be its own fault vibration. The reminder submodule is used to remind users to investigate if the vibration is determined to be caused by a combination of wake effect and its own fault.

[0021] Optionally, the fault source type determination submodule includes: The theoretical characteristic frequency acquisition unit is used to acquire the theoretical characteristic frequencies corresponding to various fault types; An amplitude determination unit is used to determine the amplitude corresponding to the theoretical characteristic frequency in the vibration frequency domain signal. The fault source type determination unit is used to determine the fault type corresponding to the theoretical characteristic frequency whose amplitude is not within the preset amplitude range.

[0022] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0023] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0024] The embodiments of this application have the following advantages: This application provides a method for inspecting offshore wind power equipment, comprising: acquiring the blade vibration time-domain signal of a target wind turbine in a wind turbine cluster, the inflow wind speed at an upstream wind turbine, the wake wind speed at a downstream wind turbine, the instantaneous wind speed at the target wind turbine, the operating data of the target wind turbine, and the ocean current velocity at the target wind turbine; determining the turbulence intensity at the target wind turbine based on the instantaneous wind speed and the inflow wind speed, and then determining the dominant turbulence frequency at the target wind turbine in conjunction with the wake wind speed; determining the wind speed deficit based on the inflow wind speed, equipment operating data, and ocean current velocity; determining the vibration cause result based on the blade vibration time-domain signal, inflow wind speed, turbulence intensity, dominant turbulence frequency, and wind speed deficit; and inspecting the target wind turbine based on the vibration cause result. By integrating multi-dimensional data and combining it with data such as turbulence intensity and wind speed deficit for analysis, the reliability and accuracy of vibration cause judgment are improved, making the inspection work more targeted, reducing the waste of manpower and resources caused by blind inspections, improving inspection efficiency, and reducing inspection costs. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the steps of an offshore wind power equipment inspection method provided in an embodiment of this application; Figure 2 This is a flowchart of another method for inspecting offshore wind power equipment provided in an embodiment of this application; Figure 3 This is a structural block diagram of an offshore wind power equipment inspection device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0028] The following description, in conjunction with the accompanying drawings, details a method for inspecting offshore wind power equipment, an offshore wind power equipment inspection device, an electronic device, and a computer-readable storage medium provided in this application, through specific embodiments and application scenarios.

[0029] Reference Figure 1 The diagram illustrates a flowchart of a method for inspecting offshore wind power equipment according to an embodiment of this application. The method may specifically include the following steps: Step 101: Obtain the blade vibration time-domain signal of the target wind turbine in the wind turbine cluster, the inflow wind speed at the upstream wind turbine, the wake wind speed at the downstream wind turbine, the instantaneous wind speed at the target wind turbine, the operating data of the target wind turbine, and the ocean current speed at the target wind turbine. Wind power equipment refers to a complete system and device that converts wind energy into electrical energy. It is a complex mechatronic system, mainly composed of wind turbine generator sets (usually referred to as "wind turbines") and supporting electrical, control, and support systems. The core of wind power equipment is the wind turbine generator set, whose main components include the wind rotor (including blades and hub), nacelle, tower, and foundation.

[0030] Blade vibration time-domain signal refers to the raw data sequence recording the changes of physical quantities of wind turbine blade vibration (such as acceleration, velocity, or displacement) over time. The horizontal axis represents time, and the vertical axis represents the intensity of vibration, visually demonstrating the entire process of blade "shaking" during operation. It is typically acquired by accelerometers installed at the blade root, hub, or nacelle, which convert mechanical vibration into continuous voltage or digital signals. In fault diagnosis, the vibration time-domain signal is the starting point for data acquisition; all analysis begins with the acquisition of the time-domain signal. By observing the waveform or calculating the RMS value, it is determined whether the overall vibration exceeds the standard. High kurtosis values ​​may indicate early pitting corrosion of bearings or gears. Filtering, denoising, and detrending operations on the time-domain signal prepare it for subsequent frequency domain analysis.

[0031] Inflow velocity refers to the free-flow velocity of the incoming air at the hub height before the wind turbine blades begin to be acted upon by the wind. Simply put, it is the original speed of the wind blowing towards the turbine before it is disturbed by the turbine. It is the most fundamental input for assessing wind energy resources, predicting turbine power, and formulating aerodynamic design and control strategies, and it relates to power, efficiency, load, control, and lifespan. Wake speed refers to the wind speed behind the blades of a wind turbine, that is, the airflow speed after the wind has been slowed down and disturbed by the turbine. According to Betz's Law, a wind turbine can only capture a maximum of 59.3% of the kinetic energy in the wind. When wind passes through the turbine blades, some of the kinetic energy is converted into mechanical energy (and thus generates electricity), causing the airflow speed to decrease and forming a low-speed, highly turbulent, rotating airflow region behind the turbine, which is the "wake." In wind farms, turbines are usually arranged in rows. The wake generated by the upstream turbines directly affects the downstream turbines. Downstream turbines are in the low-speed wake, resulting in less captured wind energy and a significant decrease in power generation. Studies have shown that the power of turbines affected by the wake can be reduced by 10%-40%. The highly turbulent wake causes the downstream turbine blades to bear severe alternating loads, accelerating fatigue damage, shortening lifespan, and increasing vibration and wear on the tower and drivetrain. Therefore, understanding the characteristics of the wake is crucial for wind farm layout optimization, control strategies, and lifespan prediction.

[0032] Instantaneous wind speed refers to the wind speed measured at a specific moment, reflecting the wind's flow velocity over a very short period (theoretically instantaneous). Instantaneous wind speed values ​​can be used to study wind turbulence characteristics, analyze aerodynamic loads, assess structural fatigue, and serve as real-time control inputs. Since components such as blades and towers bear instantaneous wind loads, the design must consider the structural strength requirements of the maximum instantaneous wind speed (gusts), or it can be used to calculate fatigue loads, as material fatigue damage is directly related to the number and amplitude of stress cycles, both of which stem from fluctuations in instantaneous wind speed. It can also be used for wind turbine control and protection, turbulence intensity calculations, wind tunnel testing and simulation, etc.

[0033] Changes in generator power can reflect whether the wind turbine is operating normally and efficiently, providing a basis for determining whether vibration is caused by equipment malfunctions, such as abnormal component operation leading to power fluctuations or increased vibration. The operating data of the target wind power equipment in this application refers to the generator power of the wind turbine.

[0034] Ocean current velocity refers to the speed at which seawater flows horizontally in a large-scale, regular manner. The impact of ocean current velocity on offshore wind power equipment is mainly reflected in the supporting structure (foundation) and submarine cables. Although it does not act directly on the upper unit of the wind turbine like wind speed or waves, long-term or extreme ocean current action can indirectly cause or aggravate equipment failures, especially in terms of fatigue damage, erosion, vibration, and cable wear.

[0035] In this embodiment, the vibration time-domain signal is acquired by a high-frequency acceleration sensor installed at the blade root. This sensor records the vibration acceleration values ​​of the blade at different times in real time, forming a vibration time-domain signal. This signal is then converted into a blade vibration frequency-domain signal for further fault analysis. The inflow wind speed is collected by a three-dimensional ultrasonic anemometer installed on the top of the upstream wind turbine nacelle, and the wake wind speed is acquired by a three-dimensional ultrasonic anemometer installed on the windward side of the downstream wind turbine. Instantaneous wind speed values ​​are collected by the three-dimensional ultrasonic anemometer, recording the instantaneous wind speed at each moment within a preset time period (e.g., 10 minutes), i.e., the instantaneous wind speed value at the i-th moment. Generator power is acquired by the wind turbine's built-in SCADA system (Supervisory and Data Acquisition System). This system, through sensors connected to the generator and other equipment, can acquire operating parameters such as generator power in real time. Ocean current velocity is measured using an ADCP (Acoustic Doppler Current Profiler) deployed in the seawater near the wind turbine foundation, allowing for the acquisition of ocean current velocities at different water depths.

[0036] Step 102: Determine the turbulence intensity at the target wind turbine based on the instantaneous wind speed value and the inflow wind speed, and determine the dominant turbulence frequency at the target wind turbine based on the turbulence intensity and the wake wind speed. Wind is the driving force behind wind turbine operation, but it is also a significant factor causing vibration. The magnitude of the inflow wind speed directly affects the stress on the wind turbine; the higher the wind speed, the greater the load on the turbine blades, and the more severe the vibration may be. Simultaneously, it forms the basis for calculating turbulence intensity, which reflects the degree of wind speed instability. The stronger the turbulence, the greater the wind speed fluctuations, and the more easily blade vibration is exacerbated. Upstream wind turbines generate wakes, where wind speeds are unstable and flow rates decrease. When downstream turbines are located within these wakes, this unstable wind speed causes uneven stress on the blades, leading to vibration. The wake wind speed directly reflects the degree of wake influence. Combined with the calculated dominant turbulence frequency, the frequency characteristics of the vibrations induced by the wake can be determined, thus identifying whether the wake is the primary cause of the vibration.

[0037] In this embodiment of the application, based on turbulence intensity and wake wind speed Determine the dominant turbulence frequency Its expression is: , in, This is the turbulence frequency coefficient, and its value depends on factors such as wind turbine layout and sea topography. Preferably 0.15-0.25, generally speaking. Take 0.2, This refers to the diameter of the fan rotor.

[0038] Step 103: Determine the wind speed loss based on the inflow wind speed, the equipment operating data, and the ocean current speed; Wind speed loss refers to the phenomenon where the wind speed at a point downstream of an obstacle (such as a wind turbine) is lower than the undisturbed incoming wind speed upstream; in other words, the wind slows down after passing the wind turbine. Wind turbines extract kinetic energy from the wind through their blades, converting it into mechanical energy. According to the law of conservation of energy, the airflow speed inevitably decreases after losing kinetic energy. This deceleration effect creates a low-speed zone behind the wind turbine, known as the wake, and the amount of wind speed reduction is the wind speed loss. The wind speed loss is greatest directly behind the wind turbine. As the distance increases, the wake diffuses, the wind speed gradually recovers, and the loss decreases. The wind speed loss is greatest along the central axis of the wake and decreases towards the edges.

[0039] Wind speed loss is related to the wind turbine thrust. The more energy the wind turbine extracts, the greater the thrust, and the more severe the wind speed loss. It is also affected by atmospheric stability. In a stable atmosphere, the wake recovers slowly and the loss lasts for a long distance. In an unstable atmosphere, the turbulence is strong, the wake mixes quickly, and the loss recovers quickly.

[0040] The power output of a wind turbine directly reflects the energy it extracts from the wind. According to the law of conservation of energy, the more energy extracted, the greater the kinetic energy loss of the wind, which manifests as a greater wind speed loss. Ocean current speed does not directly change the wind speed loss in the air, but it indirectly modulates the manifestation of wind speed loss and wake development by affecting the wind turbine structure and the marine environment.

[0041] Step 104: Determine the vibration cause result based on the blade vibration time-domain signal, the inflow wind speed, the turbulence intensity, the turbulence dominant frequency, and the wind speed deficit; Environmental wind conditions, including inflow wind speed, turbulence intensity, and wake effect (i.e., wind speed deficit), act on the blades, generating specific aerodynamic loads. These loads directly excite blade vibration, which manifests as observable time-domain vibration signals. By analyzing these signals, the causes of the vibration can be deduced.

[0042] In this embodiment, by collecting the time-domain vibration signal of the blade, the frequency characteristics obtained after processing can directly reflect the difference between its own fault and the wake effect, thereby achieving accurate differentiation of the vibration source; collecting the inflow wind speed serves as the basis for calculating turbulence intensity and thrust coefficient. Turbulence intensity can quantify the degree of aggravation of wind speed fluctuations on blade vibration, and the thrust coefficient can reflect the upstream wind turbine's ability to block the wind. This makes the assessment of the correlation between wind load and wake and vibration more targeted, solving the problem of ambiguity in the assessment of wind factor influence; collecting the wake wind speed directly reflects the degree of wake influence, and combined with the calculated dominant turbulence frequency, the vibration caused by the wake can be pinpointed. Dynamic characteristics can clearly define the actual proportion of the wake's role in vibration, avoiding omissions due to neglecting the wake; the average wind speed, fluctuating wind speed, and turbulence intensity calculated from instantaneous wind speed values ​​can present the severity of wind speed fluctuations, accurately quantifying the aggravating effect of wind speed fluctuations on vibration; generator power data can provide direct evidence for fault diagnosis, effectively distinguishing between equipment problems and external factors, reducing the possibility of misjudging external influences as equipment malfunctions; and the collection of ocean current velocity can offset the interference of ocean currents on wake assessment, making the wake-induced vibration assessment more consistent with the actual marine environment and avoiding assessment deviations caused by ocean current factors.

[0043] Step 105: Based on the vibration induction results, conduct an inspection of the target wind power equipment.

[0044] Different causes of vibration require different inspection and maintenance methods. Based on the cause of the vibration, determine whether it is caused by the wake, a fault in the equipment itself, or a combination of both, and then perform targeted maintenance based on the specific fault.

[0045] In one embodiment provided in this application, by integrating multi-dimensional data and combining it with data such as turbulence intensity and wind speed loss for analysis, the operating environment and state of wind power equipment can be comprehensively reflected, improving the reliability and accuracy of vibration cause judgment, avoiding misjudgment caused by single data, and conducting inspections based on clear vibration causes can make the inspection work more targeted, reduce the waste of manpower and material resources caused by blind inspections, improve inspection efficiency, shorten fault diagnosis time, ensure the stable operation of wind power equipment, reduce the risk of equipment damage caused by abnormal vibration, and thus improve the power generation efficiency and economic benefits of offshore wind power.

[0046] This application provides a method for inspecting offshore wind power equipment, comprising: acquiring the blade vibration time-domain signal of a target wind turbine in a wind turbine cluster, the inflow wind speed at an upstream wind turbine, the wake wind speed at a downstream wind turbine, the instantaneous wind speed at the target wind turbine, the operating data of the target wind turbine, and the ocean current velocity at the target wind turbine; determining the turbulence intensity at the target wind turbine based on the instantaneous wind speed and the inflow wind speed, and then determining the dominant turbulence frequency at the target wind turbine based on the wake wind speed; determining the wind speed deficit based on the inflow wind speed, equipment operating data, and ocean current velocity; determining the vibration cause result based on the blade vibration time-domain signal, inflow wind speed, turbulence intensity, dominant turbulence frequency, and wind speed deficit; and inspecting the target wind turbine based on the vibration cause result. By combining analysis with turbulence intensity and wind speed deficit, the accuracy of vibration cause judgment is improved, making the inspection work more targeted, reducing waste caused by blind inspection, improving inspection efficiency, and reducing inspection costs.

[0047] Reference Figure 2 This document illustrates a flowchart of another method for inspecting offshore wind power equipment according to an embodiment of this application. The method may specifically include the following steps: Step 201: Obtain the blade vibration time-domain signal of the target wind turbine in the wind turbine cluster, the inflow wind speed at the upstream wind turbine, the wake wind speed at the downstream wind turbine, the instantaneous wind speed at the target wind turbine, the operating data of the target wind turbine, and the ocean current speed at the target wind turbine. Step 202: Determine the turbulence intensity at the target wind turbine based on the instantaneous wind speed value and the inflow wind speed, and determine the dominant turbulence frequency at the target wind turbine based on the turbulence intensity and the wake wind speed. In one embodiment, determining the turbulence intensity at the target wind turbine based on the instantaneous wind speed value and the inflow wind speed in step 202 may include the following sub-steps: Wind speed is not constant but fluctuates constantly. This fluctuation causes the forces on the blades to change continuously, thus inducing vibration. Instantaneous wind speed values ​​reflect real-time changes in wind speed, and the average wind speed calculated from it reflects the overall wind speed level over a period of time. Pulsating wind speed and its standard deviation reflect the severity of wind speed fluctuations. Turbulence intensity is derived from these data and is used to measure the impact of wind speed fluctuations on vibration. High turbulence intensity indicates severe wind speed fluctuations, and blade vibration is more likely to intensify.

[0048] Sub-step S11: Determine the average wind speed at the target wind power equipment based on the instantaneous wind speed value; First, based on the instantaneous wind speed value Calculate the average wind speed Its expression is:

[0049] in, The number of sampling points within a preset time period (e.g., the number of sampling points within 10 minutes). For the first The instantaneous wind speed value at a given moment.

[0050] Sub-step S12: Determine the pulsating wind speed at the target wind power equipment based on the instantaneous wind speed value and the average wind speed; Next, based on the instantaneous wind speed value and average wind speed Calculate the difference in fluctuating wind speed Its expression is:

[0051] Then, based on the pulsating wind speed Calculate the standard deviation of fluctuating wind speed Its expression is:

[0052] Sub-step S13: Determine the turbulence intensity at the target wind turbine based on the pulsating wind speed and the inflow wind speed.

[0053] Finally, based on the standard deviation of the pulsating wind speed and inflow wind speed Determine turbulence intensity Its expression is:

[0054] Step 203: Determine the wind speed loss based on the inflow wind speed, the equipment operating data, and the ocean current speed; In one embodiment, step 203 may include the following sub-steps: Sub-step S21: Determine the upstream fan thrust coefficient based on the inflow wind speed and the equipment operating data; Inflow velocity is also the basis for calculating thrust coefficient. Thrust coefficient reflects the ability of the upstream fan to block the wind. The stronger the blockage, the greater the impact of the wake on the downstream fan.

[0055] In this embodiment of the application, based on the inflow wind speed and generator power Determine the upstream wind turbine thrust coefficient Its expression is: , in, air density, For generator power, The diameter of the fan rotor. The inflow velocity.

[0056] Sub-step S22: Determine the ocean current correction coefficient based on the inflow wind speed and the ocean current speed; Offshore wind turbine foundations are located in seawater, and ocean currents exert forces on these foundations. These forces can be transmitted through the foundations to the upper structure of the turbine, causing vibrations. Simultaneously, ocean currents affect the flow patterns of the seawater, indirectly influencing the airflow environment around the turbine and thus impacting the wake. Calculating ocean current correction factors is precisely to correct for the influence of ocean currents on the wake model, allowing the wake model to more accurately reflect the actual situation and thus more precisely determine the vibrations caused by the wake.

[0057] In this embodiment of the application, based on the inflow wind speed and ocean current speed Determine the ocean current correction factor Its expression is: , in, The baseline ocean current correction factor is determined based on the wind turbine model and represents the inherent response coefficient of the wind turbine to the marine environment, typically ranging from 0.05 to 0.1. The current influence coefficient, typically ranging from 0.2 to 0.5, is obtained by fitting measured data from offshore wind farms. The more significant the impact of the current on the wake, the better. The larger the value; This is the actual ocean current correction factor that takes into account ocean current velocity.

[0058] Sub-step S23: Determine the wind speed loss based on the upstream wind turbine thrust coefficient and the ocean current correction coefficient.

[0059] In this embodiment of the application, based on the upstream wind turbine thrust coefficient Ocean current correction factor Determine wind speed loss Its expression is: , in, This refers to the spacing between the fans.

[0060] Step 204: Determine the blade vibration frequency domain signal of the target wind turbine based on the vibration time domain signal; The time domain refers to the representation of a signal with time as the independent variable. In contrast, the frequency domain uses frequency as the independent variable and is obtained through the FFT (Fourier Transform). Time-domain signals are raw data, rich in information but complex, making it difficult to directly discern frequency components. Frequency-domain signals, converted from time-domain signals using the Fourier Transform (FFT), clearly show the amplitude of each frequency component. Relationship: Both are two representations of the same signal.

[0061] In this embodiment of the application, the vibration time-domain signal Perform a Fourier transform to obtain the vibration frequency domain signal. Its expression is: , in, For time, The vibration time-domain signal represents the time at time t. The vibration acceleration value, For frequency, The imaginary unit, It is a complex exponential function. It is a vibration frequency domain signal.

[0062] Step 205: Determine the vibration cause result based on the blade vibration time-domain signal, the inflow wind speed, the turbulence intensity, the turbulence dominant frequency, and the wind speed deficit; In this embodiment of the application, the inflow velocity is... Wind speed loss turbulence intensity turbulence dominant frequency and vibration frequency domain signals The data is input into a pre-built classification model to obtain vibration cause results, and wind power equipment is inspected based on the vibration cause results; the network architecture of the classification model is a parallel LSTM network and a 1D-CNN network.

[0063] The training process of the classification model is as follows: First, a large amount of historical data is collected, including samples of data such as blade vibration frequency domain signals, inflow wind speed, wind speed deficit, turbulence intensity, and turbulence dominant frequency, and the corresponding vibration causes are labeled, namely wake influence or self-fault. Next, the sample data is preprocessed, such as outlier removal and normalization. Then, the preprocessed samples are divided into training and validation sets according to the proportion. The training set data is used to input the preset network architecture for model training. The network parameters are continuously adjusted through the backpropagation algorithm to minimize the error between the prediction results and the labeled results. At the same time, the model performance is evaluated in real time using the validation set. If the performance does not meet the preset standard, such as the accuracy rate being lower than 90%, the network hyperparameters, such as the learning rate and the number of iterations, are adjusted, and the model is retrained until the classification model performance meets the requirements.

[0064] In one embodiment, step 205 may include the following sub-steps: Sub-step S31: Determine the wake influence probability based on the inflow wind speed, the wind speed deficit, the turbulence intensity, and the turbulence dominant frequency; In one embodiment, sub-step S31 may include the following sub-steps: Sub-step S311: Input the inflow wind speed, the wind speed deficit, the turbulence intensity, and the turbulence dominant frequency into the wake influence probability calculation model to obtain the wake influence probability; In this embodiment, the inflow wind speed, wind speed deficit, turbulence intensity, and turbulence dominant frequency are used as inputs to the LSTM network, and the LSTM network outputs the wake influence probability. .

[0065] Sub-step S32: Based on the vibration frequency domain signal, obtain the inherent failure probability of the target wind power equipment; In one embodiment, sub-step S32 may include the following sub-steps: Sub-step S321: Input the vibration frequency domain signal into the self-fault probability calculation model to obtain the self-fault probability.

[0066] In this embodiment, the vibration frequency domain signal is used as the input to the 1D-CNN network, and the 1D-CNN network outputs its own fault probability. .

[0067] Sub-step S33: Determine the vibration cause result based on the wake influence probability and the self-fault probability.

[0068] In one embodiment, sub-step S33 may include the following sub-steps: Sub-step S331: If the probability of wake influence is greater than the probability of its own failure, then the vibration cause result is determined to be wake vibration. Sub-step S332: If the probability of the wake effect is less than the probability of its own failure, then the vibration cause result is determined to be vibration due to its own failure. In sub-step S333, if the probability of wake influence is equal to the probability of self-failure, or the difference between the probability of wake influence and the probability of self-failure is less than a preset threshold, then it is determined that the vibration cause is the combined effect of wake influence and self-failure.

[0069] Step 206: Based on the vibration cause results, conduct an inspection of the target wind power equipment.

[0070] In one embodiment, step 206 may include the following sub-steps: Sub-step S41: If the vibration cause is determined to be wake vibration, then adjust the power of the upstream fan or the yaw angle of the downstream fan. Sub-step S42: If the vibration induction result is determined to be self-fault vibration, then determine the self-fault source type; In one embodiment, sub-step S42 may include the following sub-steps: Sub-step S421: Obtain the theoretical characteristic frequencies corresponding to various fault types; If the probability of wake influence is less than the probability of its own fault, then the vibration induced by the fault is the vibration caused by the fault itself, and the type of fault source needs to be determined. The specific location is determined by obtaining the theoretical characteristic frequencies corresponding to various fault types. For example: rotor imbalance is mainly characterized by a frequency equal to 1 times the rotational speed (1×RPM). For example, if the fan rotates 10 revolutions per minute (10 RPM), then 1×RPM = 10 / 60 ≈ 0.167 Hz; bearing inner ring faults have specific formulas to calculate the fault characteristic frequency (such as BPFI), which is related to the number of bearing balls, pitch diameter, contact angle, and rotational speed; gear meshing problems manifest as the gear meshing frequency (number of teeth × rotational speed) and its harmonics; blade damage may lead to abnormal blade passing frequency (number of blades × rotational speed) or a shift in the blade's natural frequency.

[0071] In one embodiment provided in this application, the theoretical characteristic frequencies of various faults are calculated, wherein the blade 1P frequency is the vibration frequency generated per revolution of the blade, and its expression is: , in, The theoretical characteristic frequency (Hz) of the blade is 1P. This refers to the generator speed (revolutions per minute). For example, when the generator speed is 300 revolutions per minute, That is, theoretically, the blade will vibrate at 5Hz for every rotation.

[0072] The 3P frequency of the blades is determined by the fact that the fan has three blades. Each of the three blades vibrates once per revolution, resulting in a frequency three times that of 1P. The expression for this frequency is: , in, This refers to the 3P theoretical characteristic frequency of the blade, for example, when the generator speed is 300 rpm. .

[0073] Sub-step S422: In the vibration frequency domain signal, determine the amplitude corresponding to the theoretical characteristic frequency; Then, the amplitude is checked for abnormalities in the vibration frequency domain signal. It can reflect the vibration amplitude at different frequencies. Find the theoretical characteristic frequencies calculated above (such as...) , Extract the amplitude values ​​at the corresponding positions (e.g., etc.). .

[0074] Sub-step S423: The theoretical characteristic frequency whose amplitude is not within the preset amplitude range is identified as the fault source type.

[0075] Finally, compare it with the amplitude during normal operation. If the amplitude |A(fvib)| corresponding to a certain theoretical characteristic frequency exceeds 3 times the preset threshold amplitude, the component corresponding to that frequency is determined to be the source of the fault. For example, if the amplitude corresponding to f1p is abnormal, it indicates that the blade is unbalanced or misaligned during installation.

[0076] In the vibration frequency domain signal, the amplitude corresponding to the theoretical characteristic frequency is determined. The amplitude corresponding to the theoretical characteristic frequency is compared with a pre-set threshold amplitude to determine the type of fault. The threshold amplitude used for comparison is pre-set. Those skilled in the art can pre-determine the threshold amplitude corresponding to different fault types by analyzing the amplitude range corresponding to each theoretical characteristic frequency in the vibration frequency domain signal during normal operation of the equipment, based on the specific model of the equipment (such as blade material, bearing specifications, etc.) and long-term accumulated operating experience.

[0077] In sub-step S43, if it is determined that the vibration is caused by a combination of wake effect and its own fault, then prompt for manual troubleshooting.

[0078] In this embodiment of the application, if the probability of wake influence is equal to the probability of its own failure, or the absolute value of the difference between the two is less than a preset threshold, the vibration cause may be the result of the combined effect of wake influence and its own failure. In this case, further investigation and determination are required by human intervention, such as combining historical data of wind turbine operation and on-site inspections to comprehensively judge the actual degree of influence of the two factors, and then formulate a targeted treatment plan.

[0079] This application provides a method for inspecting offshore wind power equipment, comprising: acquiring the blade vibration time-domain signal of a target wind turbine in a wind turbine cluster, the inflow wind speed at an upstream wind turbine, the wake wind speed at a downstream wind turbine, the instantaneous wind speed at the target wind turbine, the operating data of the target wind turbine, and the ocean current velocity at the target wind turbine; determining the turbulence intensity at the target wind turbine based on the instantaneous wind speed and the inflow wind speed, and then determining the dominant turbulence frequency at the target wind turbine in conjunction with the wake wind speed; determining the wind speed deficit based on the inflow wind speed, equipment operating data, and ocean current velocity; determining the vibration cause result based on the blade vibration time-domain signal, inflow wind speed, turbulence intensity, dominant turbulence frequency, and wind speed deficit; and inspecting the target wind turbine based on the vibration cause result. By integrating multi-dimensional data and combining it with data such as turbulence intensity and wind speed deficit for analysis, the reliability and accuracy of vibration cause judgment are improved, making the inspection work more targeted, reducing the waste of manpower and resources caused by blind inspections, improving inspection efficiency, and reducing inspection costs.

[0080] It should be noted that the offshore wind power equipment inspection method provided in this application embodiment can be executed by an offshore wind power equipment device, or a control module in the offshore wind power equipment device for executing the method of loading the offshore wind power equipment. This application embodiment uses the offshore wind power equipment device executing the method of loading the offshore wind power equipment as an example to illustrate the offshore wind power equipment method provided in this application embodiment.

[0081] Reference Figure 3 The diagram shows a structural block diagram of an offshore wind power equipment inspection device according to an embodiment of this application, which may specifically include the following modules: The acquisition module 301 is used to acquire the blade vibration time-domain signal of the target wind turbine in the wind turbine cluster, the inflow wind speed at the upstream wind turbine, the wake wind speed at the downstream wind turbine, the instantaneous wind speed value at the target wind turbine, the operating data of the target wind turbine, and the ocean current speed at the target wind turbine. The turbulence dominant frequency determination module 302 is used to determine the turbulence intensity at the target wind turbine based on the instantaneous wind speed value and the inflow wind speed, and to determine the turbulence dominant frequency at the target wind turbine based on the turbulence intensity and the wake wind speed. The wind speed loss determination module 303 is used to determine the wind speed loss based on the inflow wind speed, the equipment operating data, and the ocean current speed. The vibration cause determination module 304 is used to determine the vibration cause result based on the blade vibration time domain signal, the inflow wind speed, the turbulence intensity, the turbulence dominant frequency, and the wind speed deficit. The inspection module 305 is used to inspect the target wind power equipment based on the vibration cause results.

[0082] In one embodiment, the turbulence dominant frequency determination module 302 includes: The average wind speed determination submodule is used to determine the average wind speed at the target wind power equipment based on the instantaneous wind speed value. The pulsating wind speed determination submodule is used to determine the pulsating wind speed at the target wind power equipment based on the instantaneous wind speed value and the average wind speed. The turbulence intensity determination submodule is used to determine the turbulence intensity at the target wind turbine based on the pulsating wind speed and the inflow wind speed.

[0083] In one embodiment, the wind speed loss determination module 303 includes: The upstream fan thrust coefficient determination submodule is used to determine the upstream fan thrust coefficient based on the inflow velocity and the equipment operating data. The ocean current correction coefficient determination submodule is used to determine the ocean current correction coefficient based on the inflow wind speed and the ocean current velocity; The wind speed loss determination submodule is used to determine the wind speed loss based on the upstream wind turbine thrust coefficient and the ocean current correction coefficient.

[0084] In one embodiment, the vibration cause determination module 304 includes: The wake influence probability determination submodule is used to determine the wake influence probability based on the inflow wind speed, the wind speed deficit, the turbulence intensity, and the turbulence dominant frequency. The vibration frequency domain signal determination submodule is used to determine the blade vibration frequency domain signal of the target wind turbine based on the vibration time domain signal. The self-fault probability determination submodule is used to obtain the self-fault probability of the target wind power equipment based on the vibration frequency domain signal. The vibration cause determination submodule is used to determine the vibration cause result based on the wake influence probability and the self-fault probability.

[0085] In one embodiment, the wake influence probability determination submodule includes: The wake influence probability calculation unit is used to input the inflow wind speed, the wind speed deficit, the turbulence intensity, and the turbulence dominant frequency into the wake influence probability calculation model to obtain the wake influence probability. The self-fault probability determination submodule includes: The self-fault probability calculation unit is used to input the vibration frequency domain signal into the self-fault probability calculation model to obtain the self-fault probability.

[0086] In one embodiment, the vibration cause determination submodule includes: A wake vibration determination unit is used to determine the vibration cause result as wake vibration if the wake influence probability is greater than the self-fault probability. The self-fault vibration determination unit is used to determine the vibration cause result as self-fault vibration if the wake influence probability is less than the self-fault probability. The combined effect determination unit is used to determine that the vibration cause is the combined effect of wake influence and its own fault if the wake influence probability is equal to the self-fault probability, or if the difference between the wake influence probability and the self-fault probability is less than a preset threshold.

[0087] In one embodiment, the inspection module 305 includes: The adjustment submodule is used to adjust the power of the upstream fan or the yaw angle of the downstream fan if the vibration cause is determined to be wake vibration. The fault source type determination submodule is used to determine the fault source type if the vibration inducing result is determined to be its own fault vibration. The reminder submodule is used to remind users to investigate if the vibration is determined to be caused by a combination of wake effect and its own fault.

[0088] In one embodiment, the fault source type determination submodule includes: The theoretical characteristic frequency acquisition unit is used to acquire the theoretical characteristic frequencies corresponding to various fault types; An amplitude determination unit is used to determine the amplitude corresponding to the theoretical characteristic frequency in the vibration frequency domain signal. The fault source type determination unit is used to determine the fault type corresponding to the theoretical characteristic frequency whose amplitude is not within the preset amplitude range.

[0089] The offshore wind power equipment inspection device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0090] The offshore wind power equipment inspection device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0091] The offshore wind power equipment inspection device provided in this application embodiment can achieve... Figures 1 to 2 The various processes implemented by the offshore wind power equipment in the method embodiments will not be described again here to avoid repetition.

[0092] This application describes an offshore wind turbine inspection device that acquires the blade vibration time-domain signal of a target wind turbine in a wind turbine cluster, the inflow wind speed at the upstream wind turbine, the wake wind speed at the downstream wind turbine, the instantaneous wind speed at the target wind turbine, the operating data of the target wind turbine, and the ocean current velocity at the target wind turbine. Based on the instantaneous wind speed and inflow wind speed, the turbulence intensity at the target wind turbine is determined, and then combined with the wake wind speed, the dominant turbulence frequency at the target wind turbine is determined. Based on the inflow wind speed, equipment operating data, and ocean current velocity, the wind speed deficit is determined. Based on the blade vibration time-domain signal, inflow wind speed, turbulence intensity, dominant turbulence frequency, and wind speed deficit, the vibration cause is determined. Based on the vibration cause results, the target wind turbine is inspected. By integrating multi-dimensional data and combining it with data such as turbulence intensity and wind speed deficit for analysis, the reliability and accuracy of vibration cause judgment are improved, making inspection work more targeted, reducing the waste of manpower and resources caused by blind inspections, improving inspection efficiency, and reducing inspection costs.

[0093] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described offshore wind power equipment inspection method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0094] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0095] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0096] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0097] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for inspecting offshore wind power equipment, characterized in that, include: The following data are collected: blade vibration time-domain signal of the target wind turbine in the wind turbine cluster; inflow wind speed at the upstream wind turbine; wake wind speed at the downstream wind turbine; instantaneous wind speed at the target wind turbine; operating data of the target wind turbine; and ocean current speed at the target wind turbine. The turbulence intensity at the target wind turbine is determined based on the instantaneous wind speed and the inflow wind speed, and the dominant turbulence frequency at the target wind turbine is determined based on the turbulence intensity and the wake wind speed. The wind speed deficit is determined based on the inflow wind speed, the equipment operating data, and the ocean current speed. The vibration inducing factors are determined based on the blade vibration time-domain signal, the inflow wind speed, the turbulence intensity, the turbulence dominant frequency, and the wind speed deficit. Based on the vibration induction results, the target wind power equipment is inspected.

2. The method for inspecting offshore wind power equipment according to claim 1, characterized in that, Determining the turbulence intensity at the target wind turbine based on the instantaneous wind speed and the inflow wind speed includes: Based on the instantaneous wind speed value, determine the average wind speed at the target wind power equipment; Based on the instantaneous wind speed and the average wind speed, the pulsating wind speed at the target wind power equipment is determined; The turbulence intensity at the target wind turbine is determined based on the pulsating wind speed and the inflow wind speed.

3. The method for inspecting offshore wind power equipment according to claim 1, characterized in that, The determination of wind speed loss based on the inflow wind speed, the equipment operating data, and the ocean current speed includes: The upstream fan thrust coefficient is determined based on the inflow velocity and the equipment operating data. Determine the ocean current correction factor based on the inflow wind speed and the ocean current velocity; The wind speed loss is determined based on the upstream wind turbine thrust coefficient and the ocean current correction coefficient.

4. The method for inspecting offshore wind power equipment according to claim 1, characterized in that, The determination of vibration inducing factors based on the blade vibration time-domain signal, the inflow wind speed, the turbulence intensity, the dominant turbulence frequency, and the wind speed deficit includes: The probability of wake influence is determined based on the inflow wind speed, the wind speed deficit, the turbulence intensity, and the turbulence dominant frequency. Based on the vibration time-domain signal, determine the blade vibration frequency-domain signal of the target wind turbine. Based on the vibration frequency domain signal, the probability of the target wind power equipment's own failure is obtained; The vibration induction result is determined based on the wake influence probability and the self-fault probability.

5. The method for inspecting offshore wind power equipment according to claim 4, characterized in that, The step of determining the wake influence probability based on the inflow wind speed, the wind speed deficit, the turbulence intensity, and the dominant turbulence frequency includes: The inflow wind speed, wind speed deficit, turbulence intensity, and turbulence dominant frequency are input into the wake influence probability calculation model to obtain the wake influence probability. The step of obtaining the inherent failure probability of the target wind turbine based on the vibration frequency domain signal includes: The vibration frequency domain signal is input into the self-fault probability calculation model to obtain the self-fault probability.

6. The method for inspecting offshore wind power equipment according to claim 4, characterized in that, The determination of vibration induced causes based on the wake influence probability and the self-fault probability includes: If the probability of wake influence is greater than the probability of its own failure, then the vibration cause is determined to be wake vibration. If the probability of the wake effect is less than the probability of its own failure, then the vibration cause is determined to be vibration due to its own failure. If the probability of wake influence is equal to the probability of self-failure, or if the difference between the probability of wake influence and the probability of self-failure is less than a preset threshold, then the vibration cause is determined to be the combined effect of wake influence and self-failure.

7. The method for inspecting offshore wind power equipment according to claim 6, characterized in that, The step of inspecting the target wind power equipment based on the vibration induced causes includes: If the vibration is determined to be caused by wake vibration, then adjust the power of the upstream fan or the yaw angle of the downstream fan. If the vibration induced by the vibration is determined to be vibration caused by its own fault, then the type of fault source is determined. If the vibration is determined to be caused by a combination of wake effects and its own malfunction, then manual troubleshooting is recommended.

8. The method for inspecting offshore wind power equipment according to claim 7, characterized in that, Determining the type of fault source includes: Obtain the theoretical characteristic frequencies corresponding to various fault types; In the vibration frequency domain signal, determine the amplitude corresponding to the theoretical characteristic frequency; The theoretical characteristic frequency whose amplitude is not within the preset amplitude range is identified as the fault source type.

9. A device for offshore wind power equipment, characterized in that, include: The acquisition module is used to acquire the blade vibration time-domain signal of the target wind turbine in the wind turbine cluster, the inflow wind speed at the upstream wind turbine, the wake wind speed at the downstream wind turbine, the instantaneous wind speed value at the target wind turbine, the operating data of the target wind turbine, and the ocean current speed at the target wind turbine. The turbulence dominant frequency determination module is used to determine the turbulence intensity at the target wind turbine based on the instantaneous wind speed value and the inflow wind speed, and to determine the turbulence dominant frequency at the target wind turbine based on the turbulence intensity and the wake wind speed. The wind speed loss determination module is used to determine the wind speed loss based on the inflow wind speed, the equipment operating data, and the ocean current speed. The vibration cause determination module is used to determine the vibration cause result based on the blade vibration time-domain signal, the inflow wind speed, the turbulence intensity, the turbulence dominant frequency, and the wind speed deficit. The inspection module is used to inspect the target wind power equipment based on the vibration cause results.

10. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the offshore wind power equipment inspection method as described in claims 1-9.

11. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions, which, when executed by a processor, implement the steps of the offshore wind power equipment inspection method as described in claims 1-9.

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