Anti-typhoon self-adaptive control method and system for offshore wind turbine generator

Abstract

The invention relates to the technical field of offshore wind turbine generators, in particular to an anti-typhoon self-adaptive control method and system for an offshore wind turbine generator, and the method comprises the steps: collecting environment data based on a multi-sensor network, dynamically adjusting the blade angle, rotating speed and shutdown operation through a self-adaptive algorithm, and optimizing load distribution in combination with wind regime fluctuation characteristics. According to the invention, extreme meteorological conditions can be monitored in real time, unit load is reduced, mechanical damage is avoided, and disaster resistance and operation stability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power, and in particular relates to a typhoon-resistant adaptive control method and system for offshore wind turbines. Background Art

[0002] In specific environments, such as typhoon-prone sea areas, areas with extreme weather conditions or complex sea conditions, offshore wind turbines may face challenges that are significantly different from conventional operating environments. These special environments place higher demands on the disaster resistance and operational reliability of wind turbines, which may cause the units to be overloaded when dealing with severe weather such as typhoons, thereby affecting their stability and safety. Typhoons are characterized by high wind speeds, changeable wind directions, and drastic fluctuations in air pressure, which can easily pose a serious threat to the structure and operating status of wind turbines, and even cause mechanical damage or unit shutdown, thereby affecting power generation efficiency and equipment life.

[0003] At present, traditional wind turbine control methods are mainly based on fixed parameter control strategies, relying on preset operating conditions and static models. However, under severe weather conditions such as typhoons, these methods are difficult to adapt to rapidly changing wind conditions and complex meteorological environments, and cannot adjust key parameters such as blade angle and speed in real time to reduce unit load. This results in low disaster resistance and operational reliability of wind turbines in extreme weather, increasing maintenance costs and operational risks. Summary of the invention

[0004] The present invention aims to solve the problem that offshore wind turbines may suffer mechanical damage or operational failure due to excessive load under extreme weather conditions such as typhoons, and provides a typhoon-resistant adaptive control method and system for offshore wind turbines. The method monitors environmental meteorological data and wind turbine operating status in real time, and dynamically adjusts key parameters of the unit in combination with an adaptive algorithm, thereby reducing the stress on the unit and improving disaster resistance.

[0005] The present invention adopts the following technical means to solve the technical problem:

[0006] The present invention provides a typhoon-resistant adaptive control method for an offshore wind turbine generator set, comprising:

[0007] Based on the preset multi-sensor network of the wind turbine, environmental data such as wind speed, wind direction, air pressure and temperature are collected, and the analog signals are converted into digital signals through the preset data processing unit;

[0008] Determining whether the digital signal matches a preset extreme weather condition;

[0009] If not, activate the preset adaptive adjustment module of the wind turbine, test the key parameters of blade angle, rotational speed, and generator output power, and optimize and adjust the operating state of the wind turbine according to the preset safe operating threshold of the wind turbine using the preset dynamic load compensation strategy; wherein, the key parameters specifically include blade inclination angle, tip speed ratio, and generator torque coefficient, and the dynamic load compensation strategy specifically includes blade angle fine-tuning, rotational speed limitation, and shutdown operation;

[0010] Judge whether the wind turbine detects a preset wind condition fluctuation;

[0011] If detected, collect the characteristic parameters of the wind condition fluctuation, detect the input wind speed waveform of the wind turbine based on the characteristic parameters, and calculate the phase difference between the input wind speed and the blade angle; based on the phase difference, dynamically adjust the blade angle of the wind turbine, and synchronize the wind speed waveform and load distribution of the wind turbine according to the blade angle; wherein, the characteristic parameters specifically include wind speed change rate, wind direction offset, and turbulence intensity.

[0012] Further, before the step of activating the preset adaptive adjustment module of the wind turbine and testing the key parameters of blade angle, rotational speed, and generator output power, it further includes:

[0013] Based on the preset protection mechanism type of the wind turbine, identify the abnormal operating state of the wind turbine, wherein the protection mechanism type specifically includes overload protection, overspeed protection, vibration protection, and temperature protection, and the abnormal operating state specifically includes transient shock and continuous overload;

[0014] Judge whether the abnormal operating state matches the protection mechanism type;

[0015] If so, obtain the meteorological characteristics of the current sea area, dynamically adjust the trigger sensitivity of the protection mechanism type according to the meteorological characteristics, and adjust other operating parameters of the wind turbine through a preset feedback loop, wherein the meteorological characteristics specifically include wind speed distribution, wind direction change frequency, and wave interference, and the other operating parameters specifically include blade angle step value, rotational speed adjustment rate, and shutdown delay time.

[0016] Further, in the step of optimizing and adjusting the operating state of the wind turbine using the preset dynamic load compensation strategy, it further includes:

[0017] Collect the load change coefficient of the wind turbine, and identify the actual load demand of the wind turbine based on the load change coefficient;

[0018] Judge whether the actual load demand matches the load change trend;

[0019] If not, then according to the load change coefficient, detect the deviation value between the generator output power and the expected value, and dynamically adjust the load compensation factor of the wind turbine according to the deviation value. Through the real-time speed of the wind turbine, adjust the matching relationship between the generator output power and the load demand.

[0020] Further, in the step of collecting the characteristic parameters of the wind condition fluctuation and detecting the input wind speed waveform of the wind turbine according to the characteristic parameters, it further includes:

[0021] Use the data processing unit to perform high-speed sampling on the input wind speed waveform of the wind turbine, and scan the short-time high-amplitude pulses of the input wind speed waveform. Among them, the high-amplitude pulses specifically include pulse amplitude, pulse duration, and pulse occurrence time point;

[0022] Judge whether the short-time high-amplitude pulse matches the preset average amplitude;

[0023] If not, then identify the waveform position of the short-time high-amplitude pulse, compare the blade angle waveform when the short-time high-amplitude pulse appears, and based on the blade angle waveform, detect the wind speed fluctuation and wind speed peak of the wind turbine. Among them, the waveform position specifically includes the positive half-cycle, negative half-cycle, and zero crossing point.

[0024] Further, in the step of judging whether the digital signal matches the preset extreme weather conditions, it further includes:

[0025] Convert the digital signal into a corresponding frequency-domain signal, and extract spectrum information from the frequency-domain signal. Among them, the spectrum information specifically includes frequency components and relative amplitudes;

[0026] Judge whether the spectrum information is within the preset spectrum threshold;

[0027] If so, then based on the spectrum region corresponding to the spectrum threshold, calculate the total power of the frequency components in the spectrum region, and detect the preset noise data from the total power of the frequency components. Among them, the spectrum region specifically includes the high-frequency region and the low-frequency region, and the noise data specifically includes abnormal high-frequency interference and abnormal low-frequency drift.

[0028] Further, in the step of judging whether the wind turbine detects the preset wind condition fluctuation, it further includes:

[0029] Based on the preset sampling record points, collect the fluctuation slope of the wind condition fluctuation, and calculate the wind speed change of the wind turbine according to the adjacent sampling points and the fluctuation slope;

[0030] Judge whether the wind speed change matches the preset fluctuation type. Among them, the fluctuation type specifically includes normal change, abnormal jump, and slow drift;

[0031] If so, perform a frequency-domain conversion on the slope sequence corresponding to the fluctuation slope, extract the fluctuation range parameters from the slope sequence, and obtain the periodic characteristics of the wind condition fluctuation based on the fluctuation range parameters, where the fluctuation range parameters specifically include the maximum and minimum values, the mean value, and the standard deviation.

[0032] Further, in the step of applying a preset data processing unit to sample the analog signals of the multi-sensor network and converting the analog signals into digital signals, the following steps are further included:

[0033] Based on the resistance matching preset by the wind turbine for the multi-sensor network, adjust the high wind speed signal of the wind turbine to the signal range preset by the data processing unit, and sample the preset standard signal through the data processing unit;

[0034] Determine whether the standard signal is within the preset error range;

[0035] If not, draw the corresponding sampling data waveform according to the standard signal, and perform error correction on the standard signal based on the sampling data waveform, where the error correction is specifically to adjust the zero-point offset and gain compensation.

[0036] The solution of the present invention realizes the all-round monitoring of environmental parameters through a multi-sensor network, combines digital signal processing technology to identify extreme meteorological conditions, adopts a dynamic load compensation strategy to optimize real-time parameters in a conventional scenario, and at the same time introduces a wind condition fluctuation detection mechanism to synchronize the wind speed waveform and the load distribution through phase difference adjustment. Through the above technical means, the problem of the operation reliability of offshore wind turbines under extreme meteorological conditions such as typhoons is solved. Through the real-time monitoring of environmental data such as wind speed, wind direction, and air pressure, combined with the operating state of the wind turbine, the blade angle, rotation speed, and shutdown operation are dynamically adjusted, effectively reducing the load of the unit and avoiding mechanical damage. At the same time, through the application of an adaptive control algorithm, a rapid response to wind condition changes is realized, and the disaster resistance and operation stability of the wind turbine are improved. Description of the Drawings

[0037] Figure 1 It is a schematic diagram of the overall flow of an anti-typhoon adaptive control method for an offshore wind turbine according to an embodiment of the present invention.

[0038] Figure 2 It is a schematic diagram of the flow of a dynamic load compensation strategy of an anti-typhoon adaptive control method for an offshore wind turbine according to an embodiment of the present invention.

[0039] Figure 3 It is a schematic diagram of the flow of adjustment based on wind condition fluctuations of an anti-typhoon adaptive control method for an offshore wind turbine according to an embodiment of the present invention.

[0040] Figure 4 This is a schematic flowchart of the execution protection mechanism type for an anti-typhoon adaptive control method for an offshore wind turbine in an embodiment of the present invention.

[0041] Figure 5 This is a schematic structural diagram of an anti-typhoon adaptive control system for an offshore wind turbine in an embodiment of the present invention. Detailed implementation manners

[0042] Embodiment 1

[0043] Combined with Figure 1 As shown, this embodiment provides an anti-typhoon adaptive control method for an offshore wind turbine, including the following steps:

[0044] Collect wind speed, wind direction, air pressure and temperature environment data based on a preset multi-sensor network of the wind turbine, and convert the analog signal into a digital signal through a preset data processing unit;

[0045] Judge whether the digital signal matches a preset extreme meteorological condition;

[0046] If not, activate a preset adaptive adjustment module of the wind turbine, test key parameters such as blade angle, rotational speed and generator output power, and adopt a preset dynamic load compensation strategy to optimize and adjust the operating state according to the preset safe operation threshold of the wind turbine;

[0047] Judge whether the wind turbine detects a preset wind condition fluctuation;

[0048] If detected, collect characteristic parameters of the wind condition fluctuation, detect the input wind speed waveform based on the characteristic parameters, calculate the phase difference between the input wind speed and the blade angle, and dynamically adjust the blade angle based on the phase difference to synchronize the wind speed waveform and the load distribution.

[0049] In this embodiment, the multi-sensor network is arranged at key positions of the wind turbine. The multi-sensor network refers to a distributed monitoring system composed of an anemometer, a wind vane, an air pressure sensor and a temperature sensor. Specifically, a multi-node redundant arrangement method can be adopted to achieve omnidirectional data collection, which is used to capture the dynamic changes of environmental parameters in real time. The multi-sensor network is used to collect environmental data, including information such as wind speed, wind direction, air pressure and temperature. The multi-sensor network transmits the collected analog signal to the data processing unit through a cable or wireless communication method.

[0050] The data processing unit refers to an embedded controller with analog-to-digital conversion function. Specifically, signal filtering and noise suppression can be realized through programmable logic devices, providing high-precision digital signals for subsequent judgment logic. The data processing unit is embedded with an analog-to-digital converter, which can convert the received analog signal into a digital signal and perform preliminary filtering on the signal to remove noise interference. During this process, the error correction unit will perform zero-point offset adjustment and gain compensation on the signal to ensure the accuracy of the collected data. If it is found that the signal exceeds the preset range, the high wind speed signal will be adjusted to the standard sampling range of the data processing unit by adjusting the resistance value matching, and the sampling data waveform will be drawn based on the standard signal to complete the error correction.

[0051] The dynamic load compensation strategy refers to an adjustment algorithm based on real-time operating parameters. Specifically, fuzzy control or model predictive control methods can be adopted to balance the power generation efficiency and mechanical load by adjusting the blade angle and speed limit value. The phase difference calculation refers to processing the wind speed waveform and blade angle waveform through Fourier transform or time-domain correlation analysis method. Specifically, a digital signal processor can be used to achieve millisecond-level real-time calculation, which is used to guide the dynamic adjustment of the blade angle.

[0052] After the data processing unit completes the signal conversion, the digital signal is transmitted to the spectrum analysis unit for further processing. The spectrum analysis unit converts the digital signal into a frequency-domain signal and extracts spectrum information such as frequency components and relative amplitudes. By judging whether the spectrum information is within the preset spectrum threshold, it is possible to identify whether there is abnormal high-frequency interference or low-frequency drift. For example, when the total power of the spectrum region exceeds the set threshold, it is considered that the current environment meets the definition of extreme meteorological conditions. At this time, the system can activate the preset emergency module to achieve shutdown, or reduce the output power and other ways to cope with possible risks. Otherwise, the adaptive adjustment module can be activated to dynamically adjust the operation of the unit.

[0053] The core function of the adaptive adjustment module is to dynamically adjust key parameters such as blade angle, speed, and generator output power according to environmental data and the operating state of the unit. Specifically, the system first identifies the abnormal operating state of the wind turbine through the protection mechanism type, such as overload protection, overspeed protection, vibration protection, or temperature protection. If a transient shock or continuous overload phenomenon is detected, the system will dynamically adjust the trigger sensitivity of the protection mechanism type in combination with the meteorological characteristics of the current sea area. For example, in the case of uneven wind speed distribution or large wave interference, parameters such as the blade angle step value, speed adjustment rate, and shutdown delay time can be adjusted through the feedback loop to optimize the operating performance of the unit.

[0054] During the execution of the dynamic load compensation strategy, the system first collects the load change coefficient of the wind turbine and identifies the actual load demand based on this coefficient. If the actual load demand does not match the load change trend, the deviation value between the generator output power and the expected value is further detected. According to the deviation value, the system dynamically adjusts the load compensation factor and regulates the generator output power through real-time speed control to achieve the best match between the load demand and the output power. In addition, when the wind condition fluctuation detection module detects wind condition fluctuations, the system collects the fluctuation slope and calculates the wind speed change. If the wind speed change is an abnormal jump or slow drift, the periodic characteristics of the wind condition fluctuations are obtained by performing a frequency domain conversion on the slope sequence and extracting fluctuation range parameters such as the maximum and minimum values, mean, and standard deviation.

[0055] In the specific operation of the wind condition fluctuation detection module, the system uses a data processing unit to perform high-speed sampling on the input wind speed waveform and scans for the occurrence of short-term high-amplitude pulses. If the amplitude, duration, and occurrence time point of the short-term high-amplitude pulse do not meet the preset average amplitude, the waveform position of the pulse is further identified, including the positive half-cycle, negative half-cycle, and zero crossing point. By comparing the blade angle waveform, the system can detect wind speed fluctuations and wind speed spikes and dynamically adjust the blade angle based on the phase difference to synchronize the wind speed waveform and the load distribution.

[0056] During operation, when the multi-sensor network collects environmental parameters, the data processing unit converts the analog signal into a digital signal and performs preliminary filtering. Through a preset extreme weather condition judgment model, such as a logical judgment based on the wind speed threshold and the air pressure change rate, the typhoon environment and the normal operating condition are distinguished. Under normal operating conditions, the adaptive adjustment module is activated to perform real-time tests on key parameters such as the blade tilt angle and the tip speed ratio and compare them with the safe operation threshold. When it is detected that the key parameters approach the threshold, the dynamic load compensation strategy generates an adjustment instruction according to the current parameter deviation amount, for example, by fine-tuning the blade angle to disperse the stress distribution in the wind pressure concentration area. At the same time, the wind condition fluctuation detection module continuously monitors characteristic parameters such as the wind speed change rate and the turbulence intensity. When it is detected that there is a phase deviation between the wind speed waveform and the blade angle, a blade angle correction signal is generated based on the phase difference calculation result, so that the operating state of the unit follows the wind speed fluctuation in real time, thereby avoiding local overload caused by waveform mismatch.

[0057] Compared with traditional methods that rely on a single wind speed parameter for control decisions, this solution constructs a comprehensive judgment model by integrating multi-dimensional environmental parameters, significantly improving the recognition accuracy of extreme meteorological conditions. This solution realizes real-time optimization of operating parameters through a dynamic load compensation strategy, effectively suppressing mechanical overload while ensuring power generation efficiency. In addition, compared with existing technologies that do not consider the synchronization problem between wind speed waveforms and the response of the unit, this solution introduces a phase difference calculation mechanism and optimizes the load distribution by dynamically adjusting the blade angle, solving the stress concentration problem caused by response lag in traditional methods.

[0058] Through the above technical solutions, this application can real-time sense the change trend of multi-dimensional environmental parameters in a typhoon environment, actively adjust key operating parameters under normal conditions to prevent overload risks, and offset the impact of sudden wind condition fluctuations on the unit through the wind speed waveform synchronization mechanism. This closed-loop control system not only ensures the structural safety under typhoon conditions but also maintains the power generation efficiency under non-extreme meteorological conditions, effectively solving the safety hazards caused by control lag and parameter rigidity in traditional methods.

[0059] Combined with Figure 4 As shown, in this embodiment, before the step of activating the preset adaptive adjustment module of the wind turbine and testing the key parameters of blade angle, rotational speed, and generator output power, it further includes identifying the abnormal operating state of the wind turbine based on the preset protection mechanism type of the wind turbine. The protection mechanism type specifically includes overload protection, overspeed protection, vibration protection, and temperature protection, and the abnormal operating state specifically includes transient shock and continuous overload; determining whether the abnormal operating state matches the protection mechanism type; if so, obtaining the meteorological characteristics of the current sea area, and dynamically adjusting the trigger sensitivity of the protection mechanism type according to the meteorological characteristics, and adjusting other operating parameters of the wind turbine through a preset feedback loop. The meteorological characteristics specifically include wind speed distribution, wind direction change frequency, and wave interference, and the other operating parameters specifically include blade angle step value, rotational speed adjustment rate, and shutdown delay time.

[0060] The type of protection mechanism refers to the set of protection strategies pre-set by the wind turbine for different fault modes, which can be specifically implemented by using a multi-level threshold trigger logic. For example, overload protection is associated with the signal of the current sensor, overspeed protection is linked to the speed monitoring module, vibration protection is bound to the data of the acceleration sensor, and temperature protection is coordinated with the thermosensitive element. The type of abnormality is judged by the combination of different sensors. The abnormal operating state refers to the non-steady-state working conditions of the unit under extreme weather, which can be specifically identified by monitoring the fluctuation amplitude and duration of real-time parameters. For example, a transient shock is manifested as an instantaneous mutation of the speed or vibration amplitude, and continuous overload is manifested as the generator output power continuously exceeding the threshold. The meteorological characteristics refer to the dynamic environmental parameters related to the operation of the wind turbine in the current sea area, which can be jointly collected by using meteorological radar data and marine monitoring equipment. For example, the wind speed distribution is obtained by a multi-point anemometer, the wind direction change frequency is statistically analyzed from the historical data of the wind vane, and the wave interference is measured by a wave height sensor. The trigger sensitivity refers to the response threshold or delay time for the protection mechanism to start, which can be dynamically adjusted by using an adaptive algorithm according to environmental changes. For example, the delay time of overspeed protection is reduced in areas with frequent wind direction changes, and the trigger threshold of vibration protection is increased in high-wave interference scenarios. The feedback loop refers to forming a closed-loop control between the adjustment process after the protection mechanism is triggered and the operating parameters of the unit, which can be specifically realized by a PID controller. For example, according to the correlation model between the shutdown delay time parameter and the wave interference intensity, the buffer time before emergency shutdown is dynamically optimized.

[0061] When an abnormal operating state is detected, first, the type of protection mechanism is matched with the fault mode. For example, a transient shock corresponds to vibration protection, and continuous overload corresponds to overload protection. Subsequently, the meteorological characteristics of the current sea area are combined. For example, in areas with a relatively high wind direction change frequency, by reducing the trigger delay time of overspeed protection, the unit can quickly respond to the abnormal speed caused by sudden wind direction changes. At the same time, the shutdown delay time parameter is adjusted according to the wave interference intensity. For example, the shutdown delay is extended under strong wave interference to avoid the unit performing shutdown operations at the peak of wave impact. During this process, the feedback loop adjusts the blade angle step value in real time. For example, the single adjustment angle is adjusted from the default 0.5 degrees to 0.3 degrees to reduce the mechanical impact caused by large-angle adjustments. By dynamically matching the protection mechanism with the type of abnormality and optimizing the trigger parameters based on environmental data, a closed-loop control adapted to the sea area characteristics is formed.

[0062] Compared with the protection mechanism using a fixed threshold, such as the overspeed protection only setting a single rotational speed threshold, it is impossible to distinguish normal wind speed fluctuations from abnormal acceleration caused by typhoons. In this embodiment, by introducing meteorological characteristic parameters, such as correlating the wind speed distribution characteristics with the triggering sensitivity of overspeed protection, the rotational speed protection threshold is automatically increased when the typhoon eye passes, and the threshold is decreased in the typhoon edge area to respond in advance. In addition, compared with the existing shutdown delay time usually being a fixed value, such as uniformly set to 10 seconds, while this solution combines wave interference data, such as extending the delay time to 15 seconds when the wave height is 3 meters, to avoid the unit from bearing excessive torque at the wave peak.

[0063] Through the above solution, the problem of lag in abnormal state recognition caused by the fixed sensitivity of the traditional protection mechanism is solved, and the matching accuracy between the protection mechanism and the changes in the sea area environment under extreme meteorological conditions such as typhoons is improved. By dynamically adjusting the triggering sensitivity, such as optimizing the overspeed protection response speed in real time according to the wind direction change frequency, the number of false triggers can be reduced and the abnormal state capture rate can be increased. Combining the optimized control of the shutdown delay time through the feedback loop, such as performing the shutdown operation in stages based on the wave intensity, can reduce the risk of mechanical damage caused by emergency shutdown and maintain the continuous operation ability of the unit in the abnormal state.

[0064] Combined with Figure 2 As shown, in this embodiment, when using the dynamic load compensation strategy to optimize and adjust the operating state of the wind turbine, the load change coefficient is collected and the actual load demand is identified based on this coefficient, and the matching degree between the actual demand and the load change trend is judged. When they do not match, the deviation value between the generator output power and the expected value is detected according to the load change coefficient, and the load compensation factor is dynamically adjusted based on the deviation value, and the matching relationship between the generator output power and the load demand is adjusted by real-time rotational speed regulation.

[0065] Among them, the load change coefficient is a quantitative index reflecting the current load fluctuation degree of the wind turbine, which is used to identify the actual load demand of the unit in real time and provide data basis for dynamic compensation. The actual load demand is a comprehensive index of the electric energy output and mechanical bearing capacity required for the unit to maintain stable operation under the current working conditions, which can be specifically obtained through the real-time output value collected by the power sensor and is used to accurately identify the real load state in the dynamic environment. The load change trend is the change curve output by the load fluctuation prediction model established based on historical operation data. Specifically, the load change rate within the sliding time window can be used for trend fitting to judge whether the current load is within the normal fluctuation range. The deviation value is the difference between the actual output power of the generator and the theoretical expected power, which can be specifically obtained by comparing the measured power value with the predicted value based on the wind speed-power curve. The load compensation factor is a proportional coefficient used to correct the adjustment intensity of the dynamic load compensation strategy. Specifically, the fuzzy control algorithm can be used to dynamically adjust the compensation amplitude according to the magnitude of the deviation value to achieve adaptive adjustment under different working conditions. The real-time speed is the number of revolutions per second of the generator rotor during operation, which can be sampled at the millisecond level by using an optical encoder or a Hall sensor and is used to establish a dynamic correlation model between the power output and the mechanical speed.

[0066] In the scenario of sudden wind speed change caused by a typhoon, the load change coefficient is obtained by real-time collecting the data of the force on the mechanical transmission components or the change of the generator current. This coefficient can reflect the impact degree of the instantaneous strong wind on the unit. When the system detects a deviation between the actual load demand and the trend model established based on historical data, the power deviation detection mechanism is triggered to compare the current output power of the generator with the theoretical value preset based on the wind speed-power curve. If the deviation exceeds the preset threshold, a compensation factor adjustment instruction is generated through the fuzzy control algorithm, and this instruction adjusts the compensation intensity in real time according to the positive and negative directions and the magnitude of the deviation value. At the same time, combined with the real-time data collected by the speed sensor, a speed-power response model is established, and the generator output power is gradually converged to the actual load demand by controlling the speed change rate. For example, when encountering a sudden increase in gusts, the compensation factor will automatically increase the compensation intensity according to the sudden change in current, and at the same time limit the speed increase rate to avoid mechanical overload, and achieve the dynamic balance of power and load through the dual adjustment mechanism.

[0067] Compared with the existing power control curve that relies on fixed compensation coefficients and offline calibration, in the scenario of severe load fluctuations caused by typhoons, it is easy to cause power mismatch due to insufficient compensation intensity or speed oscillation due to over-compensation. In this embodiment, by introducing a load change coefficient as a dynamic adjustment reference and combining a real-time deviation detection and compensation factor adjustment mechanism, the control parameters can be automatically corrected according to the actual working conditions. Further, by establishing a dynamic correlation model between the rotational speed and power output, a closed-loop matching control of the mechanical motion state and the electrical energy output characteristics is achieved, solving the problem of response lag caused by the independent adjustment of the mechanical system and the electrical system in the traditional method. The problem of power mismatch caused by sudden load changes in the typhoon environment is effectively solved. By dynamically detecting the load change coefficient and the power deviation value, a real-time adaptive adjustment of the compensation strategy is realized. Combining the linkage adjustment mechanism of the rotational speed parameter and the compensation factor can maintain the dynamic balance between the generator output power and the mechanical load under strong wind impact, avoiding the instability of the unit operation caused by compensation delay or adjustment inaccuracy.

[0068] Combined with Figure 3 As shown, in this embodiment, during the process of collecting the characteristic parameters of wind condition fluctuations and detecting the input wind speed waveform of the wind turbine according to the characteristic parameters, the data processing unit is used to perform high-speed sampling on the input wind speed waveform and scan the short-time high-amplitude pulses of the input wind speed waveform; determine whether the short-time high-amplitude pulses match the preset average amplitude; if not, identify the waveform position of the short-time high-amplitude pulses, compare the blade angle waveform when the short-time high-amplitude pulses appear, and detect the wind speed fluctuation and wind speed peak based on the blade angle waveform.

[0069] Among them, high-speed sampling refers to collecting wind speed signals at a frequency higher than the conventional sampling rate. For example, high-speed sampling is performed at a sampling rate of ≥1kHz, and specifically, a high-frequency analog-to-digital converter can be used to capture the instantaneous mutation characteristics of the wind speed in the typhoon environment. Short-time high-amplitude pulses refer to fluctuations in the wind speed signal with a short duration (for example, the duration ≤200ms) and an amplitude significantly higher than the average value, and can be specifically identified by an amplitude threshold comparison algorithm, which is used to characterize the sudden wind speed peaks in typhoons. Waveform position refers to the relative position of the pulse in the wind speed waveform period, which can be specifically determined by time-domain waveform analysis and is used to associate the pulse event with the blade motion state. The blade angle waveform refers to a dynamic change curve reflecting the real-time angle of the blade, which can be specifically collected by an encoder or an inclination sensor and is used to analyze the impact of wind speed mutation on the blade load.

[0070] In the complex wind conditions caused by typhoons, the data processing unit acquires the wind speed waveform with high-frequency sampling, and detects the amplitude, duration and occurrence time of short-term high-amplitude pulses in the wind speed signal through real-time scanning. When it is detected that the pulse amplitude exceeds the preset average amplitude, the waveform position of the pulse is further analyzed, such as the positive half-cycle, negative half-cycle or zero crossing point, and the blade angle waveform at the same time is compared. By analyzing the corresponding relationship between the spatiotemporal position of the wind speed pulse and the blade motion state, the impact timing and intensity of the wind speed peak on the blade load can be determined, thereby providing an accurate timing reference for the subsequent adjustment of the blade angle.

[0071] Through high-frequency sampling and waveform position correlation analysis, the spatiotemporal characteristics of wind speed spikes can be identified at the initial stage of their occurrence, shortening the detection delay time by about 50% compared with existing technologies. By combining the joint analysis of waveform position and blade status, real dangerous pulses can be effectively distinguished from conventional fluctuations, and the misjudgment rate can be reduced by about 30%. The rapid capture and precise positioning of short-term high-amplitude wind speed pulses in typhoons are achieved, solving the problem of load fluctuation control delay caused by detection lag in traditional methods. Through the spatiotemporal correlation analysis of pulse characteristics and blade status, the risk of misoperation caused by single amplitude judgment is avoided, enabling wind turbines to adjust the blade angle in a targeted manner before the wind speed spike impact arrives, effectively reducing the probability of instantaneous overload of the mechanical structure by about 40%.

[0072] In this embodiment, in the step of determining whether a digital signal matches a preset extreme meteorological condition, the digital signal is converted into a corresponding frequency domain signal, spectrum information is extracted from the frequency domain signal, and it is determined whether the spectrum information is within a preset spectrum threshold; if so, based on the spectrum area corresponding to the spectrum threshold, the total power of the frequency components of the spectrum area is calculated, and the preset noise data is detected from the total power of the frequency components.

[0073] Among them, the frequency domain signal refers to the signal containing frequency components converted from the original time domain signal by Fourier transform, which can be implemented by fast Fourier transform algorithm, and is used to decompose different frequency components in the composite signal. Spectral information refers to the frequency distribution characteristics in the frequency domain signal, including the amplitude and occurrence position of each frequency component, which can be extracted by spectrum analyzer or digital signal processor, such as ADSP-21489 digital signal processor, to identify abnormal frequency components in the signal. Spectral threshold refers to the pre-set boundary value of high-frequency and low-frequency regions, which can be calibrated by empirical data or historical meteorological data, and is used to divide the frequency band range of normal meteorological signals and noise interference. The total power of frequency components refers to the sum of the energy of all frequency components in a specific frequency band, which can be obtained by integral operation or power spectrum density calculation, and is used to quantify the energy characteristics of noise data to distinguish valid signals.

[0074] Specifically, after the digital signal of the collected environmental data is processed by Fourier transform, a spectral distribution including frequency components and their relative amplitudes is obtained. The preset spectral threshold divides the frequency domain into a high-frequency region and a low-frequency region. The high-frequency region is used to capture abnormal high-frequency interference caused by typhoon gusts, and the low-frequency region is used to detect low-frequency drift caused by abnormal air pressure. When the spectral information is within the threshold range, the total power of the frequency components in the target frequency band is calculated, and whether there is noise data exceeding normal meteorological fluctuations is judged by the power level. For example, in a typhoon scenario, a sudden increase in power in the high-frequency region (such as 2 - 5 kHz) can reflect instantaneous gust impacts, while a continuously high power in the low-frequency region (such as 0 - 0.5 Hz) corresponds to abnormal air pressure. The collaborative detection of the two can effectively eliminate the possibility of misjudgment in a single frequency band.

[0075] The solution of this embodiment decomposes the composite signal into independent frequency components through frequency-domain conversion and combines a dual-band power detection mechanism, which can accurately identify the high-frequency gust characteristics and low-frequency air pressure anomalies unique to typhoons, and significantly improve the detection reliability of extreme meteorological conditions. It solves the problem of misjudgment caused by noise interference in the traditional method in a complex meteorological environment. Through the synergistic effect of frequency-domain analysis and dual-band power detection, it effectively distinguishes normal meteorological fluctuations from abnormal noise signals and improves the accuracy of identifying extreme meteorological conditions. Especially in a typhoon scenario, this method can synchronously capture the characteristics of high-frequency gust impacts and low-frequency air pressure drift, avoid missed detection or false triggering caused by single-signal detection, and enhance the response reliability of wind turbines to bad weather.

[0076] Continue to combine Figure 3 As shown, in this embodiment, in the step of judging whether the wind turbine detects the preset wind condition fluctuations, the fluctuation slope of the wind condition fluctuations is collected based on the preset sampling record points, and the wind speed change is calculated according to adjacent sampling points and the fluctuation slope; it is judged whether the wind speed change matches the preset fluctuation type; the slope sequence corresponding to the fluctuation slope is subjected to frequency-domain conversion, the fluctuation range parameter is extracted, and the periodic characteristics of the wind condition fluctuations are obtained based on it.

[0077] Among them, the sampling record point refers to the measurement time node set periodically during the data acquisition process, which can be specifically implemented by using equally spaced time stamps or non-uniform sampling methods based on event triggers, and is used to construct the time series data of wind speed changes. The fluctuation slope refers to the rate of wind speed change between adjacent sampling points, which can be specifically implemented by calculating the difference in wind speed between two consecutive sampling points divided by the time interval, and is used to quantify the severity of wind speed changes. The fluctuation type refers to the predefined typical wind speed change pattern, which can be specifically implemented by using a classification model to match real-time data with normal changes, abnormal jumps, and slow drift samples in the historical database, and is used to distinguish different types of wind speed events. The frequency domain conversion refers to the method of converting time series data into frequency domain expressions, which can be specifically implemented by using fast Fourier transform or wavelet transform algorithms, and is used to reveal the periodic law of wind speed fluctuations. The fluctuation range parameter refers to the statistic that describes the data distribution characteristics, which can be specifically implemented by calculating the maximum value, minimum value, mean, and standard deviation of the sequence, and is used to quantify the amplitude distribution characteristics of wind speed fluctuations.

[0078] Specifically, when detecting wind condition fluctuations, first, wind speed data is collected at fixed time intervals and the slope values between adjacent points are calculated to form a slope sequence describing the wind speed change trend. When it is detected that the wind speed change exceeds the preset range, the current fluctuation pattern is compared with the preset type library through a classification model. For example, a sudden steep increase in slope is classified as an abnormal jump, and continuous low-amplitude fluctuations are classified as slow drift. For the fluctuation events that match the preset types, further frequency domain analysis is performed on the slope sequence to extract the energy distribution characteristics of each frequency band. The upper and lower limits of the fluctuation amplitude are determined by statistics of the maximum and minimum values, and the fluctuation stability is evaluated in combination with the standard deviation. Finally, a parameter set containing periodic intensity and change rules is generated to provide data support for subsequent control strategies. Through dynamic slope calculation combined with pattern classification, sudden jumps and long-term drift events can be accurately identified; through frequency domain conversion and statistical parameter extraction, the periodic laws hidden in the fluctuations can be revealed, thus supporting the generation of targeted control strategies. Through this solution, different wind speed fluctuation patterns under typhoon influence can be effectively distinguished, abnormal jump events can be accurately identified, and the operation status of the unit can be adjusted in a timely manner. By extracting periodic characteristic parameters, the trend of wind speed changes can be predicted and the timing of blade angle adjustment can be optimized to avoid load imbalance problems caused by control delays and improve the operation reliability of the unit under complex meteorological conditions.

[0079] In this embodiment, a method for sampling analog signals of a multi-sensor network by using a preset data processing unit and converting the analog signals into digital signals is further proposed. The method further includes performing resistance matching on the multi-sensor network preset by the wind turbine generator set to adjust the high wind speed signal of the wind turbine generator set to the signal range preset by the data processing unit, and sampling a preset standard signal through the data processing unit; determining whether the standard signal is within a preset error range; if not, drawing a corresponding sampling data waveform according to the standard signal, and performing error correction on the standard signal based on the sampling data waveform. The error correction specifically includes adjusting the zero-point offset and gain compensation.

[0080] Among them, resistance matching refers to adjusting the matching relationship between the output impedance of the sensor network and the input impedance of the data processing unit, which can be specifically implemented by using a resistor network or a programmable impedance circuit. Its function is to prevent signal reflection or attenuation and ensure the integrity of the high-speed wind signal during transmission. Among them, the signal range refers to the voltage or current range that the data processing unit can accurately sample, which can be specifically implemented by setting the reference voltage or current threshold of the analog-to-digital converter. Its function is to limit the high-dynamic-range wind speed signal within the processable range to avoid hardware overload. Among them, the error range refers to the deviation range allowed between the standard signal and the actual sampled signal, which can be specifically implemented by preset upper and lower threshold values or percentage differences. Its function is to quickly identify signal distortion caused by environmental interference. Among them, the zero-point offset refers to the deviation amount of the signal baseline relative to the reference potential, which can be specifically implemented by measuring the DC component of the signal in the no-input state and performing subtraction operations. Its function is to eliminate the static error caused by sensor reference drift. Among them, gain compensation refers to adjusting the signal amplification factor to correct the amplitude deviation, which can be specifically implemented by dynamically adjusting the feedback resistor of the operational amplifier or the proportional coefficient in the digital domain. Its function is to restore the signal amplitude attenuation or distortion caused by environmental interference.

[0081] Specifically, when the multi-sensor network collects analog signals, first, the amplitude of the high-speed wind signal is adjusted to the processable range of the data processing unit through a resistance matching circuit. Subsequently, the standardized signal is sampled periodically and compared with a preset standard signal. If the detected signal exceeds the error range, the correction process is triggered. At this time, by plotting the actual sampling waveform and analyzing the characteristics of its baseline offset and amplitude change, zero-offset correction and gain compensation are respectively used to dynamically adjust the signal. For example, in the case of baseline drift, the zero-offset amount is calculated and subtracted in the digital domain; for abnormal amplitude, the gain coefficient is adjusted according to the ratio relationship between the actual sampling waveform and the standard waveform, so that the finally output digital signal can accurately reflect the actual working condition. Through the dynamic resistance matching and the real-time correction mechanism based on waveform analysis, this solution can actively suppress the interference effect at the front end of the signal chain and achieve precise compensation at the back end. The double adjustment mechanism effectively solves the problem that the signal integrity decreases due to the unconsidered resistance mismatch, and the static correction parameters are difficult to cope with transient interference. It enables the system to significantly reduce the distortion rate of multi-sensor signals under extreme meteorological conditions such as typhoons, and ensures the acquisition accuracy of key parameters such as wind speed and wind direction. Through dynamic resistance matching and real-time error correction, the anti-interference ability of the signal conversion process is enhanced, thereby providing a reliable data basis for the adaptive control strategy of the wind turbine. At the same time, this solution avoids the problems of control instruction delay or misoperation caused by signal distortion in traditional methods, and improves the operation stability and safety of the unit in harsh environments.

[0082] Embodiment 2

[0083] Combined with Figure 5 As shown, this embodiment proposes an anti-typhoon adaptive control system for offshore wind turbines, including a multi-sensor network for collecting environmental data such as wind speed, wind direction, air pressure, and temperature; a data processing unit for converting the analog signals collected by the multi-sensor network into digital signals; an adaptive adjustment module for testing the key parameters of blade angle, rotation speed, and generator output power when the extreme meteorological conditions are not matched, and optimizing and adjusting the operating state of the wind turbine by adopting a dynamic load compensation strategy according to the preset safe operating threshold; a wind condition fluctuation detection module for detecting the characteristic parameters of wind condition fluctuations, detecting the input wind speed waveform of the wind turbine based on the characteristic parameters, calculating the phase difference between the input wind speed and the blade angle, and dynamically adjusting the blade angle based on the phase difference to synchronize the wind speed waveform and the load distribution.

[0084] Among them, the multi-sensor network refers to an integrated data acquisition device composed of an anemometer, a wind vane, a barometric pressure sensor, and a temperature sensor. Specifically, it can be implemented by sensor nodes distributed on the blades, nacelle, and tower of a wind turbine, and is used to capture multi-dimensional environmental parameters in real time to comprehensively evaluate the impact of typhoons. Among them, the data processing unit refers to a signal processing module with analog-to-digital conversion functions, which can be specifically implemented by using a high-precision ADC chip in combination with a digital filtering algorithm, and is used to eliminate environmental noise and improve signal reliability. Among them, the adaptive adjustment module refers to a controller that includes a parameter testing unit and a dynamic compensation strategy, which can be specifically implemented by an embedded system based on the PID control algorithm, and is used to dynamically adjust the blade angle and rotation speed according to safety thresholds to balance the load. Among them, the wind condition fluctuation detection module refers to a detection device with waveform analysis and phase difference calculation functions, which can be specifically implemented by using the fast Fourier transform algorithm in combination with a phase-locked loop circuit, and is used to identify the synchronization deviation between the wind speed waveform and the blade movement.

[0085] The original environmental data collected by the multi-sensor network is transmitted to the data processing unit, and after analog-to-digital conversion, a digital signal is generated, which is used to determine whether to trigger extreme weather conditions. When no extreme conditions are detected, the adaptive adjustment module starts the parameter testing process, and by real-time monitoring of the blade angle, rotation speed, and generator output power, compares them with the preset safe operation thresholds. If the parameters exceed the threshold range, the dynamic load compensation strategy is activated, such as by fine-tuning the blade inclination angle through a stepper motor, restricting the upper limit of the generator rotation speed, or starting the shutdown procedure, thereby reducing the mechanical load. At the same time, the wind condition fluctuation detection module continuously analyzes the input wind speed waveform, and by calculating the phase difference between the wind speed and the blade angle, drives the pitch system to adjust the blade angle, so that the load distribution is dynamically synchronized with the wind speed fluctuation, reducing the impact of sudden wind conditions on the unit. Through the cooperation of the multi-sensor network and the data processing unit, accurate acquisition and processing of environmental data are achieved. Combining the dynamic compensation strategy of the adaptive adjustment module, the operating parameters can be actively adjusted according to the real-time working conditions. In addition, the wind condition fluctuation detection module realizes the synchronization optimization of the wind speed and the blade movement through phase difference analysis, breaking through the technical bottleneck of uneven load distribution caused by waveform mismatch in traditional methods. Effectively reduce the mechanical load of the wind turbine under typhoon conditions, and avoid structural damage caused by sudden changes in wind speed. By dynamically adjusting the blade angle and rotation speed, ensure the matching of the generator output power and the load demand, reduce the shutdown frequency and improve the power generation efficiency. At the same time, the wind speed waveform synchronization mechanism based on the phase difference significantly alleviates the impact of turbulence and wind direction deviation on the stability of the unit, enhancing its disaster resistance ability in complex meteorological environments.

[0086] Furthermore, the system also includes a protection mechanism type and a feedback loop. The protection mechanism type is used to identify the abnormal operating state of the wind turbine and dynamically adjust the triggering sensitivity according to the meteorological characteristics of the current sea area; the feedback loop is used to adjust other operating parameters of the wind turbine, including the blade angle step value, the rotational speed adjustment rate, and the shutdown delay time.

[0087] Among them, the protection mechanism type refers to identifying abnormal states such as transient shocks or continuous overloads through preset overload protection, overspeed protection, vibration protection, and temperature protection modules. Specifically, it can be achieved by using a multi-sensor fusion algorithm combined with an abnormal feature threshold judgment to capture sudden abnormalities during the operation of the unit in real time. The meteorological characteristics refer to the wind speed distribution, wind direction change frequency, and wave interference data in the current sea area. Specifically, it can be achieved through the linkage collection of a meteorological radar and an ocean monitoring buoy, providing an environmental basis for adjusting the protection mechanism sensitivity. The triggering sensitivity refers to the minimum signal strength or duration required for the protection mechanism to initiate an abnormal response. Specifically, it can be achieved by using a fuzzy logic controller to dynamically adjust the threshold interval, matching different protection requirements in different stages of a typhoon. The feedback loop refers to the control link that converts the change in the protection mechanism sensitivity into an adjustment of the operating parameters. Specifically, it can be achieved by using a proportional-integral-derivative algorithm combined with an online parameter optimization model to achieve a progressive adjustment of the unit's operating state.

[0088] For example, when a typhoon causes a sudden change in wind speed, triggering a transient shock, the protection mechanism type identifies that it is currently in a high-turbulence area through the wind speed distribution data, and the triggering sensitivity is dynamically reduced to 70% of the preset threshold, causing the overload protection module to be activated in advance. At this time, the feedback loop adjusts the blade angle step value to 30% of the normal operating condition and gradually adjusts the blade angle with a small step size to avoid sudden changes in mechanical stress. When continuous overload is detected, the sensitivity threshold of the overspeed protection is increased to 150% of the preset value according to the wave interference intensity, and the feedback loop synchronously increases the rotational speed adjustment rate to twice that of the normal operating condition to quickly reduce the generator load. The shutdown delay time is dynamically set according to the wind direction change frequency. For example, when the wind direction deviates more than 5 times per minute, the delay time is extended to 10 seconds, and the system is allowed to self-recover before deciding whether to shut down.

[0089] This solution dynamically adjusts the sensitivity threshold by combining real-time meteorological data, enabling the overload protection to actively lower the threshold at the initial stage of a typhoon for early warning and raise the threshold during the continuous high-load stage to avoid false alarms. Meanwhile, the feedback loop converts the sensitivity change into a coordinated control of the blade angle step value and the rotational speed adjustment rate. Compared with traditional single-parameter adjustment, it can balance the dual requirements of instantaneous impact absorption and long-term load stability. It realizes the dynamic matching of the sensitivity of the protection mechanism trigger in typhoon-prone waters, effectively alleviates the damage of transient impact to the unit structure, and avoids generator overheating caused by continuous overload. By adjusting the shutdown delay time, it reduces the unnecessary shutdown times caused by short-term wind direction deviation, enabling the unit to maintain higher power generation continuity under extreme meteorological conditions.

[0090] The adaptive adjustment module further includes a load change coefficient acquisition unit for acquiring the load change coefficient of the wind turbine generator set and identifying the actual load demand; a deviation value detection unit for detecting the deviation value between the generator output power and the expected value; and a load compensation factor adjustment unit for dynamically adjusting the load compensation factor according to the deviation value and matching the relationship between the generator output power and the load demand through real-time rotational speed adjustment.

[0091] Among them, the load change coefficient is a quantitative index reflecting the current load fluctuation degree of the wind turbine generator set. Specifically, it can be achieved by using a pressure sensor or a current sensor to collect the force on the mechanical transmission components or the generator current change data, and is used to identify the actual load demand of the unit in real time, providing a data basis for dynamic compensation. Among them, the deviation value is the difference between the generator output power and the preset expected power. Specifically, it can be achieved by using a power meter to monitor the generator output power in real time and calculating the difference by comparing it with the preset curve, and is used to quantify the mismatch degree between the power and the load demand, positioning the benchmark for compensation adjustment. Among them, the load compensation factor is a dynamic parameter used to correct the relationship between the generator output power and the load demand. Specifically, it can be achieved by adjusting the generator excitation current or the converter control signal, and is used to dynamically balance the matching relationship between the load and the power according to the deviation value. Specifically, the load compensation factor can be adjusted according to the following formula: K = ΔP / (ω × J), where ΔP is the power deviation, ω is the real-time rotational speed, and J is the moment of inertia. Among them, the real-time rotational speed is the current rotational speed of the generator rotor or the blade of the wind turbine generator set. Specifically, it can be achieved by using a Hall sensor or an optical encoder to collect the rotational speed pulse signal, and is used to synchronously optimize the coupling relationship between the power output and the rotational speed when adjusting the load compensation factor.

[0092] Specifically, when the typhoon causes violent fluctuations in wind speed, the load change coefficient acquisition unit continuously collects the force changes of the unit's drive train through sensors to identify the current actual load demand. The deviation value detection unit compares the actual output power of the generator with the expected power generated based on the wind speed prediction model and calculates the deviation value between the two. The load compensation factor adjustment unit dynamically adjusts the compensation factor according to this deviation value. For example, it compensates for the power gap by increasing the excitation current and, at the same time, combines the real-time speed data to adjust the generator output within the allowable speed range to make the power output match the load demand in real time. For example, when it is detected that the load suddenly increases and the power output lags, the compensation factor is quickly increased to increase the generator torque, and at the same time, the overspeed risk is avoided through speed feedback. Through the collaborative action of the above units, it is possible to respond in real time to the load mutation caused by the typhoon and avoid overload shutdown caused by the disconnection between power and load. By dynamically collecting the load change coefficient and real-time speed and combining the deviation value-driven compensation factor adjustment, the dynamic coupling of the compensation strategy and the current operating state is achieved. For example, the prior art only sets the compensation value according to historical data, while this solution optimizes the compensation action through real-time speed synchronization, solves the problem of response delay of fixed parameters during wind speed mutation, and improves the real-time performance and accuracy of power regulation.

[0093] Through the above technical solution, the system can, under extreme meteorological conditions such as typhoons, effectively solve the mismatch problem between the generator output power and the load demand by real-time monitoring of load fluctuations and power deviation, dynamically adjusting the compensation factor and coupling the speed parameters. For example, when the wind speed suddenly increases and the load demand instantaneously exceeds the generator output capacity, the system quickly increases the compensation factor and adjusts based on the speed limit, avoiding unit overload or emergency shutdown caused by the power gap, thus ensuring the continuous and stable operation of the unit under complex sea conditions and improving the disaster resistance and operation reliability.

[0094] The operation process of the system is as follows: First, the multi-sensor network collects environmental data and performs signal conversion and error correction through the data processing unit. Subsequently, the spectrum analysis unit performs frequency-domain analysis on the digital signal to determine whether the extreme meteorological conditions are met. If so, the adaptive adjustment module is activated to optimize and adjust the wind turbine unit in combination with the protection mechanism type and the dynamic load compensation strategy. At the same time, the wind condition fluctuation detection module continuously monitors the wind condition fluctuation characteristics and reduces the force on the unit by dynamically adjusting the blade angle. Finally, the system effectively improves the disaster resistance and operation stability of the wind turbine unit under extreme meteorological conditions such as typhoons through the above steps.

[0095] In this embodiment, the connection relationships and cooperation modes among various components ensure the efficient operation of the system. The multi-sensor network is connected to the data processing unit through signal transmission lines, and the data processing unit conducts data interaction with the spectrum analysis unit and the error correction unit respectively. The adaptive adjustment module receives input signals from the data processing unit and the protection mechanism type, and transmits adjustment instructions to the dynamic load compensation strategy. The wind condition fluctuation detection module works in cooperation with the data processing unit and the adaptive adjustment module to jointly achieve comprehensive monitoring and control of the wind turbine generator set.

[0096] By means of real-time monitoring of environmental data, dynamic adjustment of key parameters, and rapid response to wind condition changes, the present invention not only effectively reduces the load of the wind turbine generator set under extreme meteorological conditions, but also significantly improves its disaster resistance and operation reliability.

[0097] It should be understood that the above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention.

[0098] The above introduction to the drawings used in the embodiments only shows some embodiments of the present invention and should not be regarded as a limitation of the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings without creative efforts.

Claims

1. A typhoon-resistant adaptive control method for offshore wind turbines, characterized in that: The following steps are involved: Based on the preset multi-sensor network of the wind turbine, the wind speed, wind direction, air pressure and temperature environmental data are collected, and the analog signals are converted into digital signals through the preset data processing unit; Determining whether the digital signal matches a preset extreme weather condition; If not, the preset adaptive adjustment module of the wind turbine is activated to test the key parameters of blade angle, rotation speed and generator output power. According to the preset safe operation threshold of the wind turbine, the preset dynamic load compensation strategy is adopted to optimize the operation state of the wind turbine. The key parameters specifically include blade inclination angle, blade tip speed ratio and generator torque coefficient, and the dynamic load compensation strategy specifically includes blade angle fine-tuning, rotation speed limit and shutdown operation. Determine whether the wind turbine generator set detects a preset wind condition fluctuation; If detected, characteristic parameters of the wind condition fluctuations are collected, the input wind speed waveform of the wind turbine is detected based on the characteristic parameters, the phase difference between the input wind speed and the blade angle is calculated, and the blade angle of the wind turbine is dynamically adjusted based on the phase difference. According to the blade angle, the wind speed waveform and load distribution of the wind turbine are synchronized, wherein the characteristic parameters specifically include the wind speed change rate, wind direction offset and turbulence intensity.

2. The typhoon-resistant adaptive control method for offshore wind turbines according to claim 1, characterized in that: Before activating the preset adaptive adjustment module of the wind turbine generator set and testing the key parameters of blade angle, rotation speed and generator output power, the method further includes: Based on the preset protection mechanism type of the wind turbine generator set, identifying the abnormal operation state of the wind turbine generator set, wherein the protection mechanism type specifically includes overload protection, overspeed protection, vibration protection and temperature protection, and the abnormal operation state specifically includes transient impact and continuous overload; Determining whether the abnormal operation state matches the protection mechanism type; If so, the meteorological characteristics of the current sea area are obtained, and the trigger sensitivity of the protection mechanism type is dynamically adjusted according to the meteorological characteristics, and other operating parameters of the wind turbine are adjusted through a preset feedback loop, wherein the meteorological characteristics specifically include wind speed distribution, wind direction change frequency and wave interference, and the other operating parameters specifically include blade angle step value, speed adjustment rate and shutdown delay time.

3. The typhoon-resistant adaptive control method for offshore wind turbines according to claim 1, characterized in that: The step of optimizing and adjusting the operating state of the wind turbine generator set by using a preset dynamic load compensation strategy also includes: Collecting a load variation coefficient of the wind turbine generator set, and identifying an actual load demand of the wind turbine generator set based on the load variation coefficient; Determining whether the actual load demand matches the load change trend; If not, the deviation between the generator output power and the expected value is detected according to the load variation coefficient, and the load compensation factor of the wind turbine is dynamically adjusted according to the deviation value. The matching relationship between the generator output power and the load demand is adjusted through the real-time speed of the wind turbine.

4. The typhoon-resistant adaptive control method for offshore wind turbines according to claim 1, characterized in that: The step of collecting characteristic parameters of the wind condition fluctuations and detecting the input wind speed waveform of the wind turbine generator set according to the characteristic parameters further includes: The data processing unit is used to perform high-speed sampling on the input wind speed waveform of the wind turbine generator set, and scan the short-time high-amplitude pulse of the input wind speed waveform, wherein the short-time high-amplitude pulse specifically includes the pulse amplitude, the pulse duration and the pulse occurrence time point; Determining whether the short-duration high-amplitude pulse matches a preset average amplitude; If not, identify the waveform position of the short-time high-amplitude pulse, compare the blade angle waveform when the short-time high-amplitude pulse occurs, and detect the wind speed fluctuation and wind speed peak of the wind turbine based on the blade angle waveform, wherein the waveform position specifically includes the positive half-cycle, the negative half-cycle and the zero crossing point.

5. The typhoon-resistant adaptive control method for offshore wind turbines according to claim 1, characterized in that: The step of determining whether the digital signal matches the preset extreme weather conditions also includes: Convert the digital signal into a corresponding frequency domain signal, and extract spectrum information from the frequency domain signal, wherein the spectrum information specifically includes frequency components and relative amplitudes; Determining whether the spectrum information is within a preset spectrum threshold; If so, based on the spectrum area corresponding to the spectrum threshold, the total power of the frequency components of the spectrum area is calculated, and the preset noise data is detected from the total power of the frequency components, wherein the spectrum area specifically includes a high-frequency area and a low-frequency area, and the noise data specifically includes abnormal high-frequency interference and abnormal low-frequency drift.

6. The typhoon-resistant adaptive control method for offshore wind turbines according to claim 1, characterized in that: The step of determining whether the wind turbine generator set detects a preset wind condition fluctuation also includes: Based on preset sampling and recording points, the fluctuation slope of the wind condition fluctuation is collected, and the wind speed change of the wind turbine is calculated according to adjacent sampling points and the fluctuation slope; Determine whether the wind speed change matches a preset fluctuation type, wherein the fluctuation type specifically includes normal change, abnormal jump and slow drift; If so, the slope sequence corresponding to the fluctuation slope is converted into the frequency domain, the fluctuation range parameter is extracted from the slope sequence, and the periodic characteristics of the wind condition fluctuation are obtained based on the fluctuation range parameter, wherein the fluctuation range parameter specifically includes the maximum and minimum values, the mean and the standard deviation.

7. The typhoon-resistant adaptive control method for offshore wind turbines according to claim 1, characterized in that: The step of using a preset data processing unit to sample the analog signal of the multi-sensor network and converting the analog signal into a digital signal also includes: Based on the preset resistance value matching of the wind turbine to the multi-sensor network, the high wind speed signal of the wind turbine is adjusted to the signal range preset by the data processing unit, and the preset standard signal is sampled by the data processing unit; Determining whether the standard signal is within a preset error range; If not, a corresponding sampling data waveform is drawn according to the standard signal, and error correction is performed on the standard signal according to the sampling data waveform, wherein the error correction specifically includes adjusting zero offset and gain compensation.

8. A typhoon-resistant adaptive control system for offshore wind turbines, characterized in that: include: A multi-sensor network for collecting environmental data on wind speed, wind direction, air pressure, and temperature; A data processing unit, used to convert analog signals collected by the multi-sensor network into digital signals; The adaptive adjustment module is used to test the key parameters of blade angle, rotation speed and generator output power when the extreme weather conditions are not matched, and optimize the operation status of the wind turbine using a dynamic load compensation strategy based on the preset safe operation threshold; The wind condition fluctuation detection module is used to detect the characteristic parameters of wind condition fluctuations, and detect the input wind speed waveform of the wind turbine according to the characteristic parameters, calculate the phase difference between the input wind speed and the blade angle, and dynamically adjust the blade angle based on the phase difference to synchronize the wind speed waveform and load distribution.

9. The typhoon-resistant adaptive control system for offshore wind turbines according to claim 8, characterized in that: Also includes: The type of protection mechanism is used to identify abnormal operating conditions of wind turbines and dynamically adjust the trigger sensitivity according to the meteorological characteristics of the current sea area; The feedback loop is used to adjust other operating parameters of the wind turbine generator set, wherein the other operating parameters specifically include blade angle step value, speed adjustment rate and shutdown delay time.

10. The typhoon-resistant adaptive control system for offshore wind turbines according to claim 8, characterized in that: The adaptive adjustment module also includes: A load variation coefficient acquisition unit, used to acquire the load variation coefficient of the wind turbine and identify the actual load demand; A deviation value detection unit, used for detecting a deviation value between the output power of the generator and an expected value; The load compensation factor adjustment unit is used to dynamically adjust the load compensation factor according to the deviation value, and adjust the matching relationship between the generator output power and the load demand through the real-time rotation speed.

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