Offshore wind power project risk assessment method and system

Abstract

The present application relates to the technical field of risk analysis, in particular to a kind of offshore wind power project risk assessment method and system, comprising the following steps: according to the wheel hub height of offshore wind farm multi-unit, wind speed value is collected, and unit power, yaw angle change, vibration amplitude are obtained, and wind speed variation rate is calculated.In the present application, by combining blade vibration, yaw drive feedback and tower stress monitoring data, multi-source information cross analysis improves the calculation accuracy of unit load fluctuation rate, quantifies the influence of wind shear intensity, improves the risk prediction ability under the condition of wind speed rapid change, dynamically evaluates and optimizes the control of unit yawing accuracy, reduces the influence of long-term operation deviation on equipment stability, multi-dimensional data fusion calculates the unstable factors of unit operation state, comprehensively quantifies the influence of wind speed variation, load fluctuation and yawing error, perfects the risk assessment system, optimizes the risk assessment model of wind turbine generator, and improves the safety management level of offshore wind power project.

Description

Technical Field

[0001] This invention relates to the field of risk analysis technology, and in particular to a method and system for risk assessment of offshore wind power projects. Background Technology

[0002] Risk analysis technology encompasses the identification, assessment, and management of various risk factors to reduce potential losses and optimize decision-making processes. This technology primarily involves key aspects such as data acquisition, modeling and analysis, risk quantification, and risk prediction, with applications spanning multiple industries including finance, insurance, safety production, and environmental protection. In the offshore wind power sector, risk analysis technology comprehensively assesses data from the marine environment, equipment operating status, and construction management to establish risk assessment models. By combining historical data with real-time monitoring information, it provides risk warnings and management solutions to improve project safety and controllability.

[0003] The risk assessment method for offshore wind power projects refers to the technical solution for identifying, analyzing, and evaluating various risk factors in offshore wind power projects. This method covers multiple aspects, including wind resource fluctuations, changes in the marine environment, equipment operational reliability, construction safety, and maintenance management. It acquires environmental and equipment status information through data acquisition technology, quantifies risk indicators using probabilistic statistical analysis methods, predicts risk trends using machine learning modeling, and assesses risk levels through multi-dimensional weighted calculation methods. Finally, based on the assessment results, it formulates risk classification and response strategy recommendations, providing data support for the full lifecycle safety management of offshore wind power projects.

[0004] Current technologies rely on a single data source to assess turbine load changes, lacking multi-source information collaborative analysis. This leads to biases in load volatility calculations, making it difficult to accurately reflect the actual stress on equipment. Wind shear analysis lacks key indicator extraction, and the impact of sudden wind speed changes is difficult to quantify effectively, affecting turbine operational stability assessments. Yaw system risk assessments lack dynamic analysis of error accumulation, relying solely on static yaw error calculations, making long-term deviation impacts difficult to predict. Data processing methods are primarily based on historical statistical analysis, unable to adapt to rapid changes in the wind farm environment, affecting the accuracy of real-time risk assessments. The interactive effects of multiple factors are not fully modeled, and the coupling effects between wind speed, load, and yaw are not deeply analyzed, reducing overall risk prediction capabilities. The lack of systematic assessments for complex environmental factors affects long-term safety management and decision optimization for wind power projects. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a risk assessment method and system for offshore wind power projects.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A risk assessment method for offshore wind power projects includes the following steps:

[0008] S1: Based on the hub height of multiple units in the offshore wind farm, collect wind speed values, and obtain unit power, yaw angle changes, vibration amplitude, calculate the wind speed change rate, extract the wind speed fluctuation frequency and amplitude, and establish the wind speed fluctuation amplitude value.

[0009] S2: Based on the wind speed fluctuation amplitude value, call the blade vibration monitoring data, yaw drive feedback data, and tower stress monitoring device data to calculate the vibration frequency difference of the blade vibration monitoring device, analyze the tower stress of the tower, and combine the unit hub height wind speed measurement point data to obtain the unit load fluctuation rate.

[0010] S3: Based on the unit load fluctuation rate, calculate the wind speed change at multiple measuring points of the blade vibration monitoring device, extract key wind shear indicators, and establish the wind shear intensity coefficient.

[0011] S4: Based on the wind shear intensity coefficient, calculate the response delay of the yaw drive, compare the yaw record with the real-time yaw, determine the yaw error, and obtain the yaw drive prediction deviation.

[0012] S5: Call the yaw drive to predict the deviation, combine the unit load fluctuation rate and wind shear intensity coefficient, calculate the operational instability coefficient of the offshore wind turbine structure monitoring, analyze the cumulative impact of wind speed variation, load fluctuation and yaw error, and obtain the unit operation risk coefficient.

[0013] As a further aspect of the present invention, the wind speed fluctuation amplitude value specifically includes the wind speed change rate, wind speed fluctuation frequency, and wind speed fluctuation amplitude; the unit load fluctuation rate includes the vibration frequency difference of the blade vibration monitoring device, the tower stress of the tower monitoring device, and the wind speed measurement data at the unit hub height; the wind shear intensity coefficient specifically includes the wind speed change at multiple measurement points and key wind shear indicators; the yaw drive prediction deviation includes the response delay of the yaw drive, the comparison result between the yaw record and the real-time yaw, and the yaw error; and the unit operation risk coefficient specifically refers to the impact of wind speed variation, the impact of load fluctuation, and the cumulative impact of yaw error.

[0014] As a further aspect of the present invention, the step of obtaining the wind speed fluctuation amplitude value specifically includes:

[0015] S101: Obtain wind speed values ​​at the hub height of multiple units in an offshore wind farm. Use multi-sensor data fusion to normalize the wind speed at multiple height measurement points, calculate the mean and standard deviation of wind speed at the hub height of multiple units, filter out abnormal wind speed data, and obtain wind speed characteristic values.

[0016] S102: Based on the wind speed characteristic value, obtain the power output data, yaw angle change data and vibration amplitude data of multiple units, calculate the rate of change of wind speed with time, use the discrete differential method to solve the wind speed change trend, and calculate the wind speed change rate sequence.

[0017] S103: Using the aforementioned wind speed change rate sequence, perform spectral analysis on the wind speed change rate using Fourier transform, extract key fluctuation frequency components, and combine this with the time-domain data of the wind speed change rate to calculate the amplitude using the formula:

[0018] ;

[0019] The main frequency and amplitude of wind speed fluctuations are obtained through calculation, and the amplitude value of wind speed fluctuations is established.

[0020] in, This represents the amplitude of wind speed fluctuations. This represents the i-th data point representing the rate of change of wind speed. The mean of the wind speed change rate series. This represents the total number of wind speed data points. The absolute deviation representing the rate of change of wind speed. The sum of variances representing the rate of change of wind speed.

[0021] As a further aspect of the present invention, the step of obtaining the unit load fluctuation rate specifically includes:

[0022] S201: Based on the wind speed fluctuation amplitude value, call the blade vibration monitoring data, yaw drive feedback data, and tower stress monitoring device data, calculate the vibration frequency difference of the blade vibration monitoring device, extract the maximum and minimum difference of vibration frequency, filter out frequency difference values ​​that exceed the set threshold range, and obtain the blade vibration abnormal frequency difference value.

[0023] S202: Based on the abnormal frequency difference of the blade vibration, the tower stress monitoring device data is called to calculate the tower stress change rate using the following formula:

[0024] ;

[0025] The tower stress change value is calculated and compared with the set tower safety stress range to obtain the tower stress anomaly coefficient.

[0026] in, Represents the change in tower stress. This represents the abnormal frequency value of the blade vibration. Represents the reference vibration frequency value. Represents the number of data points within the monitoring period. This represents the current stress value on the tower.

[0027] This represents the tower stress value at all times within the monitoring period. Represents the total number of data points within the monitoring period;

[0028] S203: Based on the tower stress anomaly coefficient and combined with the wind speed measurement data at the unit hub height, calculate the ratio of the influence of wind speed change on tower stress and obtain the unit load fluctuation rate.

[0029] As a further aspect of the present invention, the step of obtaining the wind shear intensity coefficient specifically includes:

[0030] S301: Based on the unit load fluctuation rate, calculate the wind speed change rate at multiple measuring points of the blade vibration monitoring device, call the wind speed measurement values ​​and time series data of the differentiated measuring points, calculate the wind speed change gradient at multiple measuring points, and obtain the wind speed change gradient value at the measuring points.

[0031] S302: Call the wind speed change gradient value of the measuring point, calculate the degree of wind shear at multiple measuring points, compare the differences in the wind speed change rate at the measuring points, filter the measuring points with significant wind speed changes, and obtain the key measuring points of wind shear.

[0032] S303: Based on the data from the key wind shear measurement points, calculate the wind shear intensity coefficient using the formula:

[0033] ;

[0034] The wind shear intensity at the measuring point is calculated, and the wind shear intensity coefficient is established.

[0035] Where S represents the wind shear intensity coefficient. The height of the (i+1)th key wind shear measurement point is Wind speed data at the location, The height of the i-th key wind shear measurement point is Wind speed data at the location, This represents the total number of key wind shear measurement points used to calculate the wind shear intensity coefficient. The height measured at the i-th key wind shear measurement point and height The time difference corresponding to the wind speed change is calculated in the denominator, which is the normalized result of the square root of the sum of the squares of the time differences.

[0036] As a further aspect of the present invention, the step of obtaining the yaw drive prediction deviation is specifically as follows:

[0037] S401: Based on the wind shear intensity coefficient, historical yaw records and real-time yaw data are retrieved to calculate the time deviation between the two, and then the response delay of the yaw drive is calculated using the formula:

[0038] ;

[0039] The yaw drive response delay was calculated.

[0040] in, This represents the yaw drive response delay. Represents the real-time yaw angle. Represents the historical yaw angle. Represents the wind shear intensity coefficient. Represents the wind speed influence coefficient. This represents the wind speed at time point j. This represents the number of time points used in the wind speed weighted calculation. This represents the weighted contribution of wind speed to the yaw response;

[0041] S402: Call the yaw drive response delay amount, compare the historical yaw record with the real-time yaw data, calculate the angle deviation between the two, compare the calculated deviation value with the yaw error threshold, and determine whether it exceeds the set range; if it exceeds the set range, mark the yaw error as abnormal and obtain the yaw error angle.

[0042] S403: Based on the yaw error angle, calculate the correction amount of the yaw drive system, adjust the yaw drive control parameters according to the correction amount, recalculate the yaw angle adjustment value, evaluate the yaw correction magnitude in combination with the wind speed change trend, and obtain the yaw drive prediction deviation.

[0043] As a further aspect of the present invention, the step of obtaining the unit operation risk coefficient specifically includes:

[0044] S501: Call the yaw drive to predict the deviation, obtain the unit load fluctuation rate and wind shear intensity coefficient based on the unit operation data, calculate the yaw error change trend in multiple time periods, select the wind speed change range, determine the error distribution characteristics of multiple wind speed intervals, extract the error fluctuation value, classify the error change according to the wind speed interval, analyze the relationship between the increase and decrease of error and wind speed, screen the error interval under differentiated wind speed conditions, and generate the wind speed-yaw error correlation value.

[0045] S502: Based on the aforementioned wind speed-yaw error correlation value, and combined with the unit load fluctuation rate, calculate the operational instability under the influence of wind speed variations, using the following formula:

[0046] ;

[0047] The system calculates the operational instability coefficient under the combined effect of wind speed and load changes, combines it with the wind shear intensity coefficient, calculates the stability difference within the wind speed range, identifies the operational instability characteristics of multiple wind speed ranges, and generates wind speed-operational instability mapping values.

[0048] in, Represents the instability coefficient of the operating state. This represents a single data point in the wind speed-yaw error correlation value. Represents the unit load fluctuation rate. Represents the wind shear intensity coefficient. This represents the offset of load fluctuations over multiple time periods. The time integral value representing the magnitude of wind speed change. Represents the total number of observation periods;

[0049] S503: Based on the wind speed-operational instability mapping value, analyze the cumulative impact of yaw error, the operational risk of the computer group under differentiated wind speed and load fluctuation conditions, select the combination of wind speed and load fluctuation, assess the changing trend of the operating status, identify high-frequency fluctuation areas, screen key wind speed and load combinations with abnormal operating status, analyze the long-term cumulative impact in combination with long-term data, screen risk intervals, and obtain the unit operation risk coefficient.

[0050] The present invention also provides a risk assessment system for offshore wind power projects, which is used to perform the above-mentioned risk assessment method for offshore wind power projects. The offshore wind power project risk assessment system includes a wind speed fluctuation monitoring module, a load fluctuation calculation module, a wind shear assessment module, a yaw error calculation module, and an operation risk assessment module.

[0051] The wind speed fluctuation monitoring module acquires wind speed measurement data at the hub height of multiple units in the offshore wind farm, collects wind speed values ​​at the corresponding heights, and extracts unit power, yaw angle changes, and vibration amplitudes. It calculates the wind speed change rate, compares the wind speed change rate with the changes in unit vibration amplitude, extracts the wind speed fluctuation frequency and amplitude, calculates the wind speed fluctuation amplitude value, and establishes the wind speed fluctuation amplitude value.

[0052] The load fluctuation calculation module, based on the wind speed fluctuation amplitude value, calls the blade vibration monitoring data, yaw drive feedback data, and tower stress monitoring device data to calculate the vibration frequency difference of the blade vibration monitoring device, compares the stress state of the tower stress monitoring device, and combines the unit hub height wind speed measurement point data with the unit load fluctuation rate to obtain the unit load fluctuation rate.

[0053] The wind shear assessment module calls the unit load fluctuation rate, calculates the wind speed change at multiple measuring points of the blade vibration monitoring device, extracts the gradient of the wind speed change rate, compares the gradient change amplitude, screens key wind shear indicators, calculates the wind speed vertical gradient change rate based on the key wind shear indicators, compares the wind speed vertical gradient change rate with the vibration frequency of the blade vibration monitoring device, and establishes the wind shear intensity coefficient.

[0054] The yaw error calculation module calculates the response delay of the yaw drive based on the wind shear intensity coefficient, compares the yaw record with the real-time yaw, calculates the offset angle of the yaw error, filters the key influencing factors of the yaw offset, calculates the influence of multiple factors on the yaw drive, and obtains the yaw drive prediction deviation.

[0055] The operational risk assessment module calls the yaw drive prediction deviation, combines the unit load fluctuation rate and wind shear intensity coefficient, calculates the operational instability coefficient of the offshore wind turbine structure monitoring, analyzes the cumulative impact of wind speed variation, load fluctuation and yaw error, and obtains the unit operation risk coefficient.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0057] In this invention, the dynamic characteristics of wind speed changes are accurately extracted through data acquisition and calculation, enhancing the ability to identify wind resource fluctuations. Combining blade vibration, yaw drive feedback, and tower stress monitoring data, multi-source information cross-analysis improves the calculation accuracy of turbine load fluctuation rate. The establishment of a wind shear intensity coefficient allows for the quantitative analysis of wind shear effects, improving risk prediction capabilities under drastic wind speed changes. Dynamic assessment of the cumulative effect of yaw error optimizes the control of turbine yaw accuracy, reducing the impact of long-term operational deviations on equipment stability. Multi-dimensional data fusion calculates unstable factors in turbine operation, comprehensively quantifying the impact of wind speed variations, load fluctuations, and yaw errors, thus improving the risk assessment system. Through dynamic data analysis, real-time monitoring feedback, and multivariate calculation, the risk assessment model for wind turbines is optimized, improving the safety management level of offshore wind power projects. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0059] Figure 2 This is a flowchart of the steps for obtaining the wind speed fluctuation amplitude value according to the present invention;

[0060] Figure 3 This is a flowchart illustrating the steps for obtaining the unit load fluctuation rate according to the present invention.

[0061] Figure 4 This is a flowchart illustrating the steps for obtaining the wind shear intensity coefficient according to the present invention.

[0062] Figure 5 This is a flowchart of the steps for obtaining the yaw drive prediction deviation in this invention.

[0063] Figure 6 This is a flowchart illustrating the steps for obtaining the unit operation risk coefficient according to the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0065] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0066] Example 1

[0067] Please see Figure 1 This invention provides a technical solution: a risk assessment method for offshore wind power projects, comprising the following steps:

[0068] S1: Acquire real-time operating data of multiple wind turbines in the offshore wind farm, including wind speed values ​​at the hub height wind speed measurement point, power output monitoring data of the turbines, yaw angle changes of the yaw angle sensor and vibration amplitude of the blade vibration monitoring device, wind speed change rate between the hub height wind speed measurement points of the computer group, extract wind speed fluctuation frequency and amplitude, and establish wind speed fluctuation amplitude value.

[0069] S2: Based on the wind speed fluctuation amplitude, the vibration data of the blade vibration monitoring device, the feedback data of the yaw drive, and the stress measurement data of the tower stress monitoring device are called to calculate the vibration frequency change of the blade vibration monitoring device at different measurement points, analyze the stress difference of the tower at multiple heights monitored by the tower stress monitoring device, calculate the dynamic stress change rate of the blade vibration monitoring device and the tower stress monitoring device, and combine the instantaneous load change rate of the computer group at the wind speed change of the unit hub height wind speed measurement point to obtain the unit load fluctuation rate.

[0070] S3: Based on the unit load fluctuation rate, calculate the wind speed change rate of the blade vibration monitoring device at the differentiated measurement points, extract the wind shear influence factor, call the data of the offshore wind farm wind resource measurement points to analyze the wind speed gradient of the wind turbine hub height wind speed measurement points, and obtain the wind shear intensity coefficient.

[0071] S4: Based on the wind shear intensity coefficient, calculate the response delay time of the yaw drive, collect the yaw angle change data of the unit, analyze the impact of the wind direction adjustment recorded by the yaw angle sensor on the error of wind speed prediction, calculate the response hysteresis error of the yaw drive, and obtain the yaw drive prediction deviation.

[0072] S5: Based on the yaw drive prediction deviation, combined with the unit load fluctuation rate and wind shear intensity coefficient, calculate the operational instability coefficient of the offshore wind turbine structure monitoring, analyze the cumulative impact of wind speed variation, load fluctuation and yaw error, and establish the unit operation risk coefficient.

[0073] The wind speed fluctuation amplitude specifically includes the wind speed change rate, wind speed fluctuation frequency, and wind speed fluctuation amplitude. The unit load fluctuation rate includes the vibration frequency difference of the blade vibration monitoring device, the tower stress of the tower monitoring device, and the wind speed measurement data at the unit hub height. The wind shear intensity coefficient specifically includes the wind speed change at multiple measurement points and key wind shear indicators. The yaw drive prediction deviation includes the response delay of the yaw drive, the comparison result between the yaw record and the real-time yaw, and the yaw error. The unit operation risk coefficient specifically refers to the impact of wind speed variation, the impact of load fluctuation, and the cumulative impact of yaw error.

[0074] Please see Figure 2 The specific steps for obtaining the wind speed fluctuation amplitude value are as follows:

[0075] S101: Obtain wind speed values ​​at the hub height of multiple units in an offshore wind farm. Use multi-sensor data fusion to normalize the wind speed at multiple height measurement points, calculate the mean and standard deviation of wind speed at the hub height of multiple units, filter out abnormal wind speed data, and obtain wind speed characteristic values.

[0076] Specifically, obtaining the wind speed values ​​at the hub height of multiple units in an offshore wind farm requires accessing the wind speed sensor data of each wind turbine. The data is then matched according to sampling time to form a simultaneous wind speed data set for multiple units. The wind speed sensors are installed at the hub height, collecting wind speed data once per second and storing it in a database. A multi-sensor data fusion method is used, which involves normalizing the wind speed data from multiple height measurement points. The normalization formula is as follows:

[0077] ;

[0078] in, Representing the Wind speed data from each wind speed measuring point and These represent the maximum and minimum values ​​in the collected wind speed data, respectively. The mean of the normalized data is calculated to obtain the wind speed characteristic value. The calculation method is as follows:

[0079] ;

[0080] in, After calculating the wind speed characteristic values ​​based on the number of sensor measurement points, abnormal wind speed data in the dataset are removed. The method for determining abnormal wind speed data is to calculate the standard deviation of the data and set a threshold. If a certain wind speed data point satisfy:

[0081] ;

[0082] If the data is deemed outlier, it is removed. Typically, the standard deviation of wind speed is between 0.5 m / s and 1.5 m / s to ensure data quality. After removing outlier data, the mean is recalculated to obtain the characteristic value of wind speed.

[0083] S102: Based on wind speed characteristic values, obtain power output data, yaw angle change data and vibration amplitude data of multiple units, calculate the rate of change of wind speed over time, use the discrete differential method to solve the wind speed change trend, and calculate the wind speed change rate sequence.

[0084] Specifically, the power data of the wind turbine is recorded by the SCADA system in kW and stored once per minute; the yaw angle change data is collected by the yaw system sensor in degrees and stored once every 5 seconds; the vibration amplitude data is collected by the vibration sensor in mm / s² and stored once per second; the rate of change of wind speed over time is calculated using the discrete differential method to solve for the wind speed change trend. The calculation method is as follows:

[0085] ;

[0086] in, This represents the i-th data point representing the rate of change of wind speed. and Wind speed data representing adjacent time points. and Representing the corresponding time points, after calculating the wind speed change rate sequence, a sliding window smoothing process is applied to it to reduce the impact of data fluctuations. The sliding window size is between 3 and 5 data points, and finally the smoothed wind speed change rate sequence is obtained.

[0087] S103: The wind speed change rate sequence is retrieved, and Fourier transform is used to perform spectral analysis on the wind speed change rate. Key fluctuation frequency components are extracted, and the amplitude is calculated using the time-domain data of the wind speed change rate, employing the following formula:

[0088] ;

[0089] The main frequency and amplitude of wind speed fluctuations are obtained through calculation, and the amplitude value of wind speed fluctuations is established.

[0090] in, This represents the amplitude of wind speed fluctuations. This represents the i-th data point representing the rate of change of wind speed. The mean of the wind speed change rate series. This represents the total number of wind speed data points. The absolute deviation representing the rate of change of wind speed. The sum of variances representing the rate of change of wind speed.

[0091] Specifically, before performing the Fourier transform, the rate of change of wind speed is first normalized to zero:

[0092] ;

[0093] Then, a Fast Fourier Transform (FFT) is performed to extract the key fluctuation frequency components and calculate the amplitude using the following formula:

[0094] ;

[0095] in, This represents the amplitude of wind speed fluctuations. This represents the i-th data point representing the rate of change of wind speed. The mean of the wind speed change rate series. This represents the total number of wind speed data points. The sum of variances representing the rate of change of wind speed is shown in Table 1.

[0096] Table 1 Calculation Parameters for Wind Speed ​​Change Rate

[0097]

[0098] Calculation example: Assume , A set of data points for the rate of change of wind speed:

[0099] ;

[0100] Its amplitude is calculated as follows:

[0101] ;

[0102] The results indicate the magnitude of wind speed fluctuations; the larger the value, the more severe the wind speed fluctuations and the more significant their impact on the wind turbine's operating status. These results are derived from two parts of calculation: the first part... It reflects the average deviation of the wind speed change rate data points from the mean, and measures the overall trend of wind speed change; Part Two The total variance of the secondary wind speed change rate was then calculated to measure the severity of wind speed changes. Finally, the combination of these two quantities is used to characterize the overall magnitude of wind speed fluctuations. This result can be further used to optimize wind turbine control strategies to address the operational impacts of wind speed variations.

[0103] Please see Figure 3 The specific steps for obtaining the unit load fluctuation rate are as follows:

[0104] S201: Based on the wind speed fluctuation amplitude value, call the blade vibration monitoring data, yaw drive feedback data, and tower stress monitoring device data to calculate the vibration frequency difference of the blade vibration monitoring device, extract the maximum and minimum difference of vibration frequency, filter out the frequency difference values ​​that exceed the set threshold range, and obtain the abnormal frequency difference value of blade vibration.

[0105] Specifically, wind speed fluctuation data is collected by the wind speed sensor of the wind turbine, recording once per second; blade vibration monitoring data is recorded by the acceleration sensor installed on the turbine blades, with millisecond-level sampling and storage; yaw drive feedback data is provided by the yaw system controller of the turbine, with a data sampling period of 1 second; tower stress monitoring data is obtained through strain sensors installed on key parts of the tower, with a sampling frequency of 100Hz. Then, the vibration frequency difference of the blade vibration monitoring device is calculated. First, the vibration frequency at different time points is extracted from the blade vibration monitoring data, and a sliding window method with a time window of 1 minute is used to calculate the maximum vibration frequency within the time window. and minimum vibration frequency The formula for calculating the vibration frequency difference is as follows:

[0106] ;

[0107] Assuming the normal vibration frequency of wind turbine blades is within the range of 2.5Hz to 3.2Hz, if the vibration frequency exceeds this range within a certain time window, the vibration frequency difference within that window is calculated. Assuming the maximum vibration frequency is 3.5Hz and the minimum vibration frequency is 2.4Hz within a certain time window, the calculation is as follows:

[0108] ;

[0109] Filter frequency differences that exceed a set threshold range. The threshold is set at 0.3Hz. If the blade vibration is abnormal within that time window, then extract the corresponding values ​​for all abnormal time windows. As the difference in abnormal blade vibration frequency, this result indicates that the blade vibration frequency fluctuated significantly within this time window, which may be related to changes in wind speed, yaw system adjustment, or unit load changes, and its impact needs further analysis.

[0110] S202: Based on the abnormal frequency difference of blade vibration, data from the tower stress monitoring device is retrieved to calculate the tower stress change rate using the following formula:

[0111] ;

[0112] The tower stress change value is calculated and compared with the set tower safety stress range to obtain the tower stress anomaly coefficient.

[0113] in, Represents the change in tower stress. Representing the The abnormal frequency value of each blade vibration. Represents the reference vibration frequency value. Represents the number of data points within the monitoring period. This represents the current stress value on the tower. This represents the tower stress value at all times within the monitoring period. Represents the total number of data points within the monitoring period;

[0114] Specifically, tower stress data is acquired using strain gauge sensors, measured in MPa, and recorded once per second. First, the corresponding tower stress data is extracted from the abnormal time window. Then calculate the average tower stress during all normal time periods within the same time window. Set the reference vibration frequency value Assuming the number of data points within the monitoring period The tower stress data are shown in Table 2:

[0115] Table 2. Tower stress data during the monitoring period

[0116]

[0117] Then, calculate the tower stress variation, assuming a certain time window:

[0118] ;

[0119] ;

[0120] ;

[0121] Then calculate:

[0122] ;

[0123] The tower stress variation value was compared with the set safe stress range for the tower, which was set at 30MPa to 40MPa. The calculated values ​​were... If the value is much smaller than this range, it indicates that the stress on the tower of the unit changes little within this time window and will not lead to structural safety issues.

[0124] S203: Based on the tower stress anomaly coefficient and combined with the wind speed measurement data at the unit hub height, calculate the ratio of the impact of wind speed changes on tower stress and obtain the unit load fluctuation rate.

[0125] Specifically, wind speed data is collected via a wind speed sensor at the hub height of the turbine, sampling once per second to obtain the rate of change of wind speed. Calculate the ratio of the effect of wind speed change on tower stress:

[0126] ;

[0127] Set wind speed change rate Substitute into the calculation:

[0128] ;

[0129] Computer group load volatility:

[0130] ;

[0131] Assumption ,but:

[0132] ;

[0133] The results show that wind speed variation accounts for 4.4% of the total influencing factors on tower stress within this time window, indicating that wind speed fluctuations have a relatively small impact on unit load fluctuations. Load fluctuations are mainly affected by other factors (such as yaw adjustment or blade adjustment). The calculation results can be used to further adjust the unit operation strategy.

[0134] Please see Figure 4 The specific steps for obtaining the wind shear intensity coefficient are as follows:

[0135] S301: Based on the unit load fluctuation rate, calculate the wind speed change rate at multiple measuring points of the blade vibration monitoring device, call the wind speed measurement values ​​and time series data of the differentiated measuring points, calculate the wind speed change gradient at multiple measuring points, and obtain the wind speed change gradient value at the measuring points.

[0136] Specifically, the wind speed data of the blade vibration monitoring device is acquired by wind speed sensors at multiple measuring points, with sampling occurring once per second. A sliding window method with a time window of 1 minute is used to extract the data sequence of wind speed changes over time at the measuring points. The system calls upon wind speed measurements from differentiated measuring points and time-series data to calculate the gradient of wind speed changes at multiple measuring points. The calculation method for the wind speed change gradient is as follows:

[0137] ;

[0138] in, Representing the Wind speed at each measuring point This represents the time point of measurement. Assume the wind speed data of a certain unit is shown in Table 3:

[0139] Table 3 Multi-point wind speed data table

[0140]

[0141] Calculate the gradient of wind speed change at the measuring point, assuming measuring point 1 is at... wind speed ,exist wind speed Then calculate:

[0142] ;

[0143] Obtain the gradient value of wind speed change at the measuring point.

[0144] S302: Call the gradient value of wind speed change at the measuring point, calculate the degree of wind shear at multiple measuring points, compare the differences in the rate of change of wind speed at the measuring points, filter the measuring points with significant wind speed changes, and obtain the key measuring points of wind shear.

[0145] Specifically, the wind shear degree is calculated as follows:

[0146] ;

[0147] in, and This represents the wind speed at two adjacent measuring points. Assume measuring points 1 and 2 are at... The wind speeds at the times are 10.2 m / s and 10.4 m / s respectively, then:

[0148] ;

[0149] The threshold for the difference in wind speed change rate is set at 0.3 m / s. If the difference between the wind speed change rate of a certain measuring point and the wind speed change rate of its adjacent measuring points exceeds this threshold, it is determined that there is significant wind shear at the measuring point. Measuring points with significant wind speed changes are selected to obtain key measuring points for wind shear.

[0150] S303: Based on data from key wind shear measurement points, the wind shear intensity coefficient is calculated using the following formula:

[0151] ;

[0152] The wind shear intensity at the measuring point is calculated, and the wind shear intensity coefficient is established.

[0153] in, Represents the wind shear intensity coefficient. The height of the (i+1)th key wind shear measurement point is Wind speed data at the location, The height of the i-th key wind shear measurement point is Wind speed data at the location, This represents the total number of key wind shear measurement points used to calculate the wind shear intensity coefficient. The height measured at the i-th key wind shear measurement point and height The formula calculates the sum of squares of the time differences corresponding to changes in wind speed. .

[0154] set up And assume that the wind speed data at the key measuring points are as shown in Table 4:

[0155] Table 4 Wind speed data at key measuring points

[0156]

[0157] Calculate the wind shear intensity based on the data in Table 4:

[0158] ;

[0159] Calculate the sum of squares of the time intervals:

[0160] ;

[0161] Take the square root:

[0162] ;

[0163] Substitute into the formula:

[0164] ;

[0165] The results show that the wind shear intensity coefficient is 0.106 within this time window, indicating that the wind shear in this area is relatively mild and the wind speed varies little between different measuring points, which will not cause drastic load fluctuations to the wind turbine.

[0166] Please see Figure 5 The specific steps for obtaining the yaw-driven prediction error are as follows:

[0167] S401: Based on the wind shear intensity coefficient, historical yaw records and real-time yaw data are retrieved to calculate the time deviation between the two, and then the response delay of the yaw drive is calculated using the formula:

[0168] ;

[0169] The yaw drive response delay was calculated.

[0170] in, This represents the yaw drive response delay. Represents the real-time yaw angle. Represents the historical yaw angle. Represents the wind shear intensity coefficient. Represents the wind speed influence coefficient. This represents the wind speed at time point j. This represents the number of time points used in the wind speed weighted calculation. This represents the weighted contribution of wind speed to the yaw response;

[0171] Specifically, real-time yaw data is collected by angle sensors in the wind turbine control system, recorded once per second, and historical yaw records are stored in a database. Real-time yaw data can then be retrieved. and historical yaw data Calculate the angular deviation between the two:

[0172] ;

[0173] Then calculate the response delay of the yaw drive. Set the wind shear intensity coefficient. Historical yaw angle Real-time yaw angle The wind speed influence coefficient and wind speed data are shown in Table 5:

[0174] Table 5. Data on the Influence of Yaw Response Wind Speed

[0175]

[0176] Calculate the weighted contribution of wind speed to the yaw response:

[0177] ;

[0178] Calculate the yaw drive response delay:

[0179] ;

[0180] The result indicates that the yaw system has a response time of 0.12 seconds under the current wind shear conditions, suggesting that wind speed changes cause a certain delay in the yaw drive response.

[0181] S402: Call the yaw drive response delay, compare the historical yaw record with the real-time yaw data, calculate the angle deviation between the two, compare the calculated deviation value with the yaw error threshold, determine whether it exceeds the set range, if it exceeds, mark the yaw error as abnormal, and obtain the yaw error angle.

[0182] The yaw drive response latency is called, and the historical yaw records are compared with the real-time yaw data to calculate the angle deviation between the two:

[0183] ;

[0184] Substitute the known data:

[0185] ;

[0186] Set the yaw error threshold to ,like If the yaw error is abnormal, the yaw error angle is recorded. This result indicates that the current yaw adjustment deviation exceeds the preset range, which may affect the wind turbine's wind-fighting capability. The yaw error angle is then obtained.

[0187] S403: Based on the yaw error angle, calculate the correction amount of the yaw drive system, adjust the yaw drive control parameters according to the correction amount, recalculate the yaw angle adjustment value, evaluate the yaw correction magnitude in combination with the wind speed change trend, and obtain the yaw drive prediction deviation.

[0188] Based on the yaw error angle, the correction amount of the yaw drive system is calculated. The calculation method for the correction amount is as follows:

[0189] ;

[0190] in, This represents the yaw angle correction. Represents the yaw error angle. Representing the yaw error threshold, let's substitute the known data:

[0191] ;

[0192] Adjust the yaw drive control parameters based on the correction amount, and recalculate the yaw angle adjustment value:

[0193] ;

[0194] Input data:

[0195] ;

[0196] By assessing the yaw correction magnitude in conjunction with wind speed variation trends, the yaw drive prediction bias is obtained. This result indicates that the current yaw system should adjust the yaw angle to... This is to reduce the impact of yaw error on wind turbine operation.

[0197] Please see Figure 6 The specific steps for obtaining the unit operation risk coefficient are as follows:

[0198] S501: Call the yaw drive to predict the deviation, obtain the unit load fluctuation rate and wind shear intensity coefficient based on the unit operation data, calculate the yaw error change trend in multiple time periods, select the wind speed change range, determine the error distribution characteristics of multiple wind speed intervals, extract the error fluctuation value, classify the error change according to the wind speed interval, analyze the relationship between the increase and decrease of error and wind speed, screen the error interval under differentiated wind speed conditions, and generate the wind speed-yaw error correlation value.

[0199] Specifically, the unit's operating data is collected via wind speed sensors, load monitoring devices, and wind direction sensors, recorded once per second. Yaw error data from multiple time periods is retrieved to calculate the yaw error variation trend. First, the wind speed at each time point is acquired. and the corresponding yaw error The error distribution characteristics of multiple wind speed ranges were calculated, and the wind speed ranges were set as: low wind speed range (4-8m / s), medium wind speed range (8-12m / s), and high wind speed range (12-16m / s).

[0200] Within each wind speed range, extract the yaw error data for the corresponding time period and calculate the mean error. And error fluctuation value:

[0201] ;

[0202] Based on the changes in wind speed interval classification error, the relationship between error and wind speed increase / decrease was analyzed, error intervals under differentiated wind speed conditions were selected, and wind speed data for a certain time period are shown in Table 6:

[0203] Table 6 Wind speed and yaw error data

[0204]

[0205] Calculate the error fluctuation value in the low wind speed range:

[0206] ;

[0207] Calculate the error fluctuation value in the high wind speed range:

[0208] ;

[0209] The results indicate that the yaw error fluctuates more in the high wind speed range, generating a wind speed-yaw error correlation value.

[0210] S502: Based on the wind speed-yaw error correlation value and combined with the unit load fluctuation rate, the operational instability under the influence of wind speed variation is calculated using the following formula:

[0211] ;

[0212] The system calculates the operational instability coefficient under the combined effects of wind speed and load changes. Combined with the wind shear intensity coefficient, it calculates the stability differences within the wind speed range, identifies the operational instability characteristics across multiple wind speed ranges, and generates a wind speed-operational instability mapping value.

[0213] in, Represents the instability coefficient of the operating state. This represents a single data point in the wind speed-yaw error correlation value. Represents the unit load fluctuation rate. Represents the wind shear intensity coefficient. This represents the offset of load fluctuations over multiple time periods. The time integral value representing the magnitude of wind speed change. This represents the total number of observation periods.

[0214] Set observation time period Take the load volatility Wind shear intensity coefficient ,calculate:

[0215] ;

[0216] Set wind speed-yaw error correlation value data:

[0217] ;

[0218] Calculate the error term:

[0219] ;

[0220] Calculate the sum of squares of load fluctuation offsets:

[0221] ;

[0222] Take the square root:

[0223] ;

[0224] Set the time integral value of the wind speed change amplitude:

[0225] ;

[0226] Calculate the sum:

[0227] ;

[0228] Substitute into the calculation:

[0229] ;

[0230] The results indicate that current wind speed variations have a significant impact on the unit's operating status, and the wind speed-operational instability mapping value is high.

[0231] S503: Based on the wind speed-operational instability mapping value, analyze the cumulative impact of yaw error, the operational risk of the computer unit under differentiated wind speed and load fluctuation conditions, select the combination of wind speed and load fluctuation, assess the changing trend of the operating status, identify high-frequency fluctuation areas, screen key wind speed and load combinations with abnormal operating status, combine long-term data to analyze the long-term cumulative impact, screen risk intervals, and obtain the unit's operating risk coefficient.

[0232] Wind speed groups are set: wind speed 5-10 m / s, corresponding to Wind speed 10-15 m / s, corresponding to ;

[0233] Identify high-frequency fluctuation areas and set thresholds for judging abnormal operating conditions. Judgment: If Then mark this wind speed range as unstable. Then it is determined to be operating normally;

[0234] In the wind speed range of 10-15 m / s:

[0235] ;

[0236] In the wind speed range of 5-10 m / s:

[0237] ;

[0238] The results indicate that the unit's operating status is unstable and the risk is high in the wind speed range of 10-15 m / s. By combining long-term data analysis to analyze the long-term cumulative impact, the risk range is screened and the unit's operating risk coefficient is obtained.

[0239] In summary, the present invention also provides a risk assessment system for offshore wind power projects, which can execute the above-mentioned risk assessment method for offshore wind power projects, specifically including:

[0240] The wind speed fluctuation monitoring module acquires wind speed measurement data at the hub height of multiple units in the offshore wind farm, collects wind speed values ​​at the corresponding height, extracts unit power, yaw angle changes, vibration amplitude, calculates the wind speed change rate, compares the wind speed change rate with the unit vibration amplitude change, extracts the wind speed fluctuation frequency and amplitude, calculates the wind speed fluctuation amplitude value, and establishes the wind speed fluctuation amplitude value.

[0241] The load fluctuation calculation module, based on the wind speed fluctuation amplitude value, calls the blade vibration monitoring data, yaw drive feedback data, and tower stress monitoring device data to calculate the vibration frequency difference of the blade vibration monitoring device, compares the stress state of the tower stress monitoring device, and combines the unit hub height wind speed measurement point data to calculate the unit load fluctuation rate.

[0242] The wind shear assessment module calls the unit load fluctuation rate, calculates the wind speed change at multiple measuring points of the blade vibration monitoring device, extracts the gradient of the wind speed change rate, compares the gradient change amplitude, screens key wind shear indicators, calculates the wind speed vertical gradient change rate based on the key wind shear indicators, compares the wind speed vertical gradient change rate with the vibration frequency of the blade vibration monitoring device, and establishes the wind shear intensity coefficient.

[0243] The yaw error calculation module calculates the response delay of yaw drive based on the wind shear intensity coefficient, compares the yaw record with the real-time yaw, calculates the offset angle of yaw error, screens the key influencing factors of yaw offset, calculates the influence of multiple factors on yaw drive, and obtains the yaw drive prediction deviation.

[0244] The risk assessment module calls the yaw drive to predict the deviation, and combines the unit load fluctuation rate and wind shear intensity coefficient to calculate the operational instability coefficient of the offshore wind turbine structure monitoring, analyze the cumulative impact of wind speed variation, load fluctuation and yaw error, and obtain the unit operation risk coefficient.

[0245] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method of risk assessment for an offshore wind project, characterized in that, The method comprises the following steps: S1: According to the hub height of the offshore wind farm multi-unit, the wind speed value, unit power, yaw angle change, vibration amplitude, wind speed change rate, wind speed fluctuation frequency and amplitude are collected, and the wind speed fluctuation amplitude value is calculated; S2: Based on the wind speed fluctuation amplitude value, the blade vibration monitoring data, yaw drive feedback data and tower stress monitoring device data are called, the blade vibration monitoring device vibration frequency difference is calculated, the tower stress of the tower stress monitoring device is analyzed, and the unit load fluctuation rate is obtained combined with the unit hub height wind speed measurement point data; S3: According to the unit load fluctuation rate, the multi-measurement-point wind speed change of the blade vibration monitoring device is calculated, the key indicators of wind shear are extracted, and the wind shear intensity coefficient is established; S4: Based on the wind shear intensity coefficient, the response delay of the yaw drive is calculated, the yaw record and real-time yaw are compared, the yaw error is judged, and the yaw drive prediction deviation is obtained; S5: The yaw drive prediction deviation is called, the unit load fluctuation rate and the wind shear intensity coefficient are combined, the operating state instability coefficient of offshore wind turbine structure monitoring is calculated, the influence of wind speed variation, load fluctuation and yaw error accumulation is analyzed, and the unit operation risk coefficient is obtained; The wind speed fluctuation amplitude value includes wind speed change rate, wind speed fluctuation frequency and wind speed fluctuation amplitude, the unit load fluctuation rate includes blade vibration monitoring device vibration frequency difference, tower stress of tower stress monitoring device, unit hub height wind speed measurement point data, wind shear intensity coefficient includes multi-measurement-point wind speed change and wind shear key indicators, yaw drive prediction deviation includes response delay of yaw drive, comparison result of yaw record and real-time yaw, yaw error, and unit operation risk coefficient specifically refers to wind speed variation influence, load fluctuation influence and yaw error accumulation influence; The unit operation risk coefficient acquisition step is specifically: S501: The yaw drive prediction deviation is called, the unit load fluctuation rate and the wind shear intensity coefficient are obtained based on the unit operation data, the yaw error change trend in multiple time periods is calculated, the wind speed change range is selected, the error distribution characteristics of multiple wind speed intervals are determined, the error fluctuation value is extracted, the error change is classified according to the wind speed interval, the relationship between error and wind speed is analyzed, the error interval under different wind speed conditions is screened, and the wind speed-yaw error correlation value is generated; S502: Based on the wind speed-yaw error correlation value, the operating state instability under the influence of wind speed variation is calculated combined with the unit load fluctuation rate, and the formula is used: ； The operating state instability coefficient under the joint action of wind speed and load change is obtained by operation, the stability difference in the wind speed interval is calculated combined with the wind shear intensity coefficient, the operating instability characteristics of multiple wind speed intervals are identified, and the wind speed-operating instability mapping value is generated; wherein, represents a running state instability coefficient, represents a single data point in a wind speed-yaw error correlation value, represents a unit of time, represents a wind shear intensity coefficient, represents a shift amplitude of load fluctuation over multiple time periods, represents a time integral value of wind speed variation amplitude, represents a total number of observation time periods; S503: Based on the wind speed-operation instability mapping value, analyze the influence of yaw error accumulation, calculate the operation risk of the unit under the condition of differentiated wind speed and load fluctuation, select the combination of wind speed and load fluctuation, evaluate the trend of the operation state, identify the high-frequency fluctuation area, screen the key wind speed and load combination of the abnormal operation state, analyze the long-term cumulative effect based on long-term data, screen the risk interval, and obtain the unit operation risk coefficient.

2. The offshore wind farm project risk assessment method according to claim 1, characterized in that, The wind speed fluctuation amplitude value obtaining step is specifically: S101: Obtain the hub height wind speed value of the offshore wind farm multi-unit, adopt a multi-sensor data fusion method, normalize the wind speed of multiple height measuring points, calculate the wind speed mean value and standard deviation of the multi-unit hub height, screen and eliminate abnormal wind speed data, and obtain the wind speed characteristic value; S102: Based on the wind speed characteristic value, obtain the power output data, yaw angle change data and vibration amplitude data of the multi-unit, calculate the wind speed change rate with time, adopt a discrete differential method to solve the wind speed change trend, and calculate the wind speed change rate sequence; S103: Call the wind speed change rate sequence, adopt Fourier transform to perform frequency spectrum analysis on the wind speed change rate, extract the key fluctuation frequency component, calculate the amplitude size in combination with the time domain data of the wind speed change rate, and adopt the formula: ； Operate to obtain the main frequency and amplitude value of the wind speed fluctuation, and establish the wind speed fluctuation amplitude value; wherein, represents a wind speed fluctuation amplitude value, represents a first wind speed change rate data point, represents a mean value of a wind speed change rate sequence, represents a total number of wind speed data points, represents an absolute deviation of a wind speed change rate, represents a sum of variances of a wind speed change rate.

3. The offshore wind farm project risk assessment method according to claim 2, characterized in that, The unit load fluctuation rate obtaining step is specifically: S201: Based on the wind speed fluctuation amplitude value, call the blade vibration monitoring data, yaw drive feedback data and tower stress monitoring device data, calculate the blade vibration monitoring device vibration frequency difference, extract the maximum and minimum difference value of the vibration frequency, screen the frequency difference value exceeding the set threshold range, and obtain the blade vibration abnormal frequency difference value; S202: Based on the blade vibration abnormal frequency difference value, call the tower stress monitoring device data, calculate the tower stress change rate, and adopt the formula: ； Operate to obtain the tower stress change value, compare the tower stress change value with the set tower safety stress range, and obtain the tower stress abnormal coefficient; wherein, represents a tower stress change value, represents a blade vibration abnormal frequency value, represents a reference vibration frequency value, represents a number of data points in a monitoring period, represents a current time tower stress value, represents a tower stress value at all times in a monitoring period, represents a total number of data points in a monitoring period; S203: Based on the tower stress abnormal coefficient, combine the unit hub height wind speed measuring point data, calculate the influence ratio of wind speed change on the tower stress, and obtain the unit load fluctuation rate.

4. The offshore wind farm project risk assessment method according to claim 3, characterized in that, The wind shear intensity coefficient obtaining step is specifically: S301: Based on the unit load fluctuation rate, calculate the wind speed change rate of the multiple measuring points of the blade vibration monitoring device, call the wind speed measurement value and time sequence data of the differentiated measuring points, calculate the wind speed change gradient of the multiple measuring points, and obtain the measuring point wind speed change gradient value; S302: Call the measuring point wind speed change gradient value, calculate the wind shear degree of the multiple measuring points, compare the differences of the measuring point wind speed change rates, screen the measuring points with significant wind speed change, and obtain the wind shear key measuring point; S303: Based on the data of the wind shear key measuring point, calculate the wind shear intensity coefficient, and adopt the formula: ； Operate to obtain the measuring point wind shear intensity, and establish the wind shear intensity coefficient; wherein, represents the wind shear intensity coefficient, the height of the wind shear key point in the wind speed data at the position, the height of the wind shear key point in the wind speed data at the position, represents the total number of wind shear key points used to calculate the wind shear intensity coefficient, the height of the wind shear key point in the and the height of the wind speed change, the denominator part is the square sum of the time difference value after normalization.

5. A method of risk assessment of an offshore wind farm project according to claim 4, characterized in that, The yaw drive prediction deviation amount obtaining step is specifically: S401: Based on the wind shear intensity coefficient, the historical yaw record and the real-time yaw data are called to calculate the time deviation amount of the two, and then the yaw driving response delay is calculated, using the formula: ； The yaw driving response delay amount is calculated; wherein, represents a yaw drive response delay amount, represents a real-time yaw angle, represents a historical yaw angle, represents a wind shear strength coefficient, represents a wind speed influence coefficient, represents a wind speed at a time point, represents a number of time points for wind speed weighted calculation, represents a weighted contribution of wind speed to yaw response; S402: The yaw driving response delay amount is called to compare the historical yaw record and the real-time yaw data, and the angle deviation of the two is calculated. According to the comparison of the calculated deviation value and the yaw error threshold, it is judged whether it exceeds the set range. If it exceeds the set range, the yaw error abnormality is marked, and the yaw error angle is obtained; S403: Based on the yaw error angle, the correction amount of the yaw driving system is calculated, the yaw driving control parameter is adjusted according to the correction amount, the yaw angle adjustment value is recalculated, the yaw correction amplitude is evaluated in combination with the wind speed change trend, and the yaw driving prediction deviation amount is obtained.

6. A system for risk assessment of an offshore wind project, characterized in that The system is used to run the offshore wind power project risk assessment method of any one of claims 1-5, and the system comprises a wind speed fluctuation monitoring module, a load fluctuation calculation module, a wind shear evaluation module, a yaw error calculation module and an operation risk assessment module; The wind speed fluctuation monitoring module obtains the multi-machine hub height wind speed measurement point data of the offshore wind farm, collects the wind speed value at the corresponding height, extracts the unit power, yaw angle change and vibration amplitude, calculates the wind speed change rate, compares the wind speed change rate with the unit vibration amplitude change, extracts the wind speed fluctuation frequency and amplitude, calculates the wind speed fluctuation amplitude value, and establishes the wind speed fluctuation amplitude value; The load fluctuation calculation module calculates the blade vibration monitoring device vibration frequency difference based on the wind speed fluctuation amplitude value, compares the stress state of the tower stress monitoring device, combines the unit hub height wind speed measurement point data, calculates the unit load fluctuation rate, and obtains the unit load fluctuation rate; The wind shear evaluation module calculates the wind speed change of the blade vibration monitoring device multi-measurement point based on the unit load fluctuation rate, extracts the gradient of the wind speed change rate, compares the gradient change amplitude, screens the wind shear key indicators, calculates the wind speed vertical gradient change rate according to the wind shear key indicators, compares the wind speed vertical gradient change rate with the blade vibration monitoring device vibration frequency, and establishes the wind shear intensity coefficient; The yaw error calculation module calculates the response delay of the yaw driving based on the wind shear intensity coefficient, compares the yaw record with the real-time yaw, calculates the offset angle of the yaw error, screens the key influencing factors of the yaw offset, calculates the influence amount of multiple factors on the yaw driving, and obtains the yaw driving prediction deviation amount; The operation risk assessment module calls the yaw driving prediction deviation amount, combines the unit load fluctuation rate and the wind shear intensity coefficient, calculates the operation state instability coefficient of the offshore wind turbine structure monitoring, analyzes the cumulative influence of wind speed variation, load fluctuation and yaw error, and obtains the unit operation risk coefficient.

Images