Safety checking method, system and equipment for dynamic characteristic simulation device, medium and product
By determining the deviation between simulation data and measured data in the joint simulation system and ground test platform, and correcting the safety verification threshold, the problem of inaccurate verification results of offshore wind turbine simulation system was solved, and the accuracy of safety verification of key component status was improved.
Patent Information
- Application Number
- CN202511851162.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-09
- Publication Date
- 2026-03-06
AI Technical Summary
In the existing technology, the simulation system verification results of the ground test platform for offshore wind turbine generators have low accuracy and lack safety verification methods that reflect the status of key components such as five-degree-of-freedom loads, resulting in inaccurate verification results.
By loading wind turbines under different operating conditions in a joint simulation system and a ground test platform, the simulation data and measured data of the dynamic characteristic simulation device are determined, the simulation accuracy and deviation values are calculated, the safety verification threshold is corrected, and a safety analysis report is generated.
This improves the accuracy of the safety verification results of the dynamic characteristic simulation device, ensuring the safe and stable operation of key components under extreme conditions.
Smart Images
Figure CN121615365A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of safety verification, and in particular to a method, system, device, medium and product for safety verification of dynamic characteristic simulation devices. Background Technology
[0002] Offshore wind turbines share a similar structure with onshore wind turbines, being complex mechanical devices that convert kinetic energy from air into electrical energy. They consist of mechanical, hydraulic, electrical, and control subsystems. Offshore wind power offers advantages such as stable wind resources and strong wind energy, but it also needs to overcome the impact of special operating environments such as strong wind loads and wave impacts. Therefore, simulations of offshore wind turbines need to consider the influence of the operating environment on turbine operation to improve the realism of the simulation and ensure the safety and stability of offshore wind turbines.
[0003] The offshore wind turbine ground test platform (hereinafter referred to as the "ground test platform") is a complex mechanical device for testing wind turbines. It consists of mechanical, hydraulic, electrical, and control subsystems, and is difficult and expensive to build. Under extreme testing conditions, the components of the ground test platform are subjected to high loads, making it imperative to ensure its safe and stable operation.
[0004] The ground test platform co-simulation system consists of a dual-drive motor model, a five-degree-of-freedom loading device model, a wind turbine under test model, a mechanical interface model, a generator grid connection model, and a working condition simulation module model. Its main function is to realize the co-simulation system and safety verification of the ground test platform and the wind turbine under test, and to provide a reference for the safe and stable operation of the ground test platform.
[0005] While safety verification methods for wind turbines already exist, methods that reflect the state of key components such as the five-degree-of-freedom load during ground-based test platform testing are still lacking. Furthermore, the accuracy of verification results is low due to errors in the results of co-simulation systems. Summary of the Invention
[0006] The purpose of this application is to provide a method, system, device, medium, and product for safety verification of dynamic characteristic simulation devices, so as to solve the problem of low accuracy of verification results.
[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a safety verification method for a dynamic characteristic simulation device, including: The wind turbines were operated under different operating conditions in both the joint simulation system and the ground test platform. The simulation data and measured data of the dynamic characteristic simulation device under any operating condition were determined. The dynamic characteristic simulation device includes a drive motor, a five-degree-of-freedom loading device, and a wind turbine. The different operating conditions include classic operating conditions and general operating conditions. The classic operating conditions are the test conditions with measured data, and the general operating conditions are the test conditions without measured data. Based on the operating condition type, calculate the simulation accuracy of the simulation data relative to the measured data; Based on the simulation accuracy, calculate the deviation between the safety verification test value of the measured data and the safety verification test value of the simulation data; Based on the deviation value, the safety verification threshold of the co-simulation system under the general operating conditions is corrected to determine the dynamic safety verification threshold. The dynamic characteristic simulation device is subjected to a safety verification based on the dynamic safety verification threshold, and a safety analysis report is generated.
[0008] Secondly, this application provides a safety verification system for a dynamic characteristic simulation device, comprising: The operation module is used to run the wind turbine in the joint simulation system and the ground test platform under different operating conditions, and to determine the simulation data and measured data of the dynamic characteristic simulation device under any operating condition. The dynamic characteristic simulation device includes a drive motor, a five-degree-of-freedom loading device, and a wind turbine. The different operating conditions include classic operating conditions and general operating conditions. The classic operating conditions are test conditions with measured data, and the general operating conditions are test conditions without measured data. The simulation accuracy determination module is used to calculate the simulation accuracy of the simulation data relative to the measured data based on the working condition type. The deviation value calculation module is used to calculate the deviation value between the safety verification test value of the measured data and the safety verification test value of the simulated data based on the simulation accuracy. The dynamic safety verification threshold determination module is used to correct the safety verification threshold of the co-simulation system under the general operating conditions based on the deviation value, and determine the dynamic safety verification threshold. The safety analysis report generation module is used to perform safety verification on the dynamic characteristic simulation device based on the dynamic safety verification threshold and generate a safety analysis report.
[0009] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described security verification method for a dynamic characteristic simulation device.
[0010] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned security verification method for a dynamic characteristic simulation device.
[0011] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned security verification method for a dynamic characteristic simulation device.
[0012] According to the specific embodiments provided in this application, this application has the following technical effects: This application operates wind turbines in both a co-simulation system and a ground test platform under different operating conditions. Simulation and measured data of the dynamic characteristic simulation device are determined for each operating condition. By calculating the simulation accuracy of the simulation and measured data, the deviation between the safety verification test value of the measured data and the safety verification test value of the simulation data is obtained. This deviation is used to correct the safety verification threshold of the co-simulation system under the general operating condition. Then, a safety verification calculation is performed based on the dynamic safety verification threshold. This application, by combining the co-simulation system and the ground test platform, improves the accuracy of the verification results for the safety verification of the key components of the dynamic characteristic simulation device. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0014] Figure 1 A flowchart of a safety verification method for a dynamic characteristic simulation device provided in one embodiment of this application; Figure 2 A schematic diagram showing the results of torque safety verification of a drive motor under extreme operating gust wind conditions of 25 m / s using a dynamic characteristic simulation device provided in an embodiment of this application. Figure 3 A schematic diagram showing the safety verification results of the drive motor speed under extreme operating gust wind conditions of 25 m / s using a dynamic characteristic simulation device provided in an embodiment of this application; Figure 4 A flowchart illustrating the safety verification process for a classic operating condition, as provided in an embodiment of this application. Figure 5 A flowchart illustrating the safety verification process under general operating conditions provided in an embodiment of this application. Detailed Implementation
[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0016] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0017] like Figure 1 As shown in the figure, this application provides a safety verification method for a dynamic characteristic simulation device, including: S1: The wind turbine generators loaded in the joint simulation system and the ground test platform are operated under different operating conditions to determine the simulation data and measured data of the dynamic characteristic simulation device under any operating condition. The dynamic characteristic simulation device includes a drive motor, a five-degree-of-freedom loading device and a wind turbine generator. Different operating conditions include classic operating conditions and general operating conditions. The classic operating conditions are test conditions with measured data, and the general operating conditions are test conditions without measured data.
[0018] S2: Based on the operating condition type, calculate the simulation accuracy of the simulation data relative to the measured data.
[0019] S3: Based on the simulation accuracy, calculate the deviation between the safety verification test value of the measured data and the safety verification test value of the simulation data.
[0020] S4: Based on the deviation value, correct the safety verification threshold of the co-simulation system under the general operating conditions to determine the dynamic safety verification threshold.
[0021] S5: Perform a safety check on the dynamic characteristic simulation device based on the dynamic safety check threshold, and generate a safety analysis report.
[0022] In an exemplary embodiment, S2 specifically includes: S21: If the operating condition type is a classic operating condition, calculate the simulation accuracy of the simulation data relative to the measured data based on the deviation value between the measured data and the simulation data in the classic operating condition. The safety verification of the classic operating condition is as follows: Figure 4 As shown.
[0023] S22: If the operating condition type is a general operating condition, extract the feature parameters from the simulation data; the safety verification of the general operating condition is as follows: Figure 5As shown, the characteristic parameters include peak factor, coefficient of variation, skewness, kurtosis, maximum gradient, gradient standard deviation, average gradient magnitude, proportion of high gradient region, maximum curvature, curvature standard deviation, average curvature magnitude, high energy index, frequency spectrum, extreme point density, and average distance between extremes.
[0024] Wherein, peak factor: in The maximum absolute value of the simulation data. This represents the average of the absolute values of the simulation data.
[0025] Coefficient of variation: in, This represents the standard deviation of the simulation data.
[0026] Skewness: in, The total length of the simulation data distribution. This represents the average value of the simulation data.
[0027] Kuroshi: gradient: in, For simulating step size, For data points The corresponding simulation data values, For data points The corresponding simulation data values.
[0028] Maximum gradient: in, This represents the maximum absolute value of the gradient in the simulation data.
[0029] Gradient standard deviation: in, gradient The average value.
[0030] Average gradient magnitude: Proportion of high gradient regions: in The threshold (usually taken as) ), The total length of the interval that satisfies the conditions.
[0031] Second derivative: in, For data points The corresponding simulation data values.
[0032] Maximum curvature: in, This represents the maximum absolute value of the curvature in the simulation data.
[0033] Standard deviation of curvature: in, .
[0034] Mean curvature amplitude: Fourier transform: High-frequency energy index: in, The index corresponding to the cutoff frequency (usually taken as...) ), This represents the number of sampling points.
[0035] Frequency spectrum: in, .
[0036] Local extremum detection: Extreme point density: Average distance between extreme values: ② Feature standardization: in, for The average value, for The standard deviation.
[0037] ③ Use a random forest model to identify the most relevant features and calculate the root mean square error (RMSE) and coefficient of determination (R²).2 Cross-validation was performed to verify the reliability of the deviation prediction module.
[0038] Root Mean Square Error (RMSE): S23: Standardize the feature parameters to determine the standardized features.
[0039] S24: Train the error prediction model based on the standardized features, and perform cross-validation on the trained error prediction model based on the root mean square error and coefficient of determination of the prediction error to determine the trained error prediction model.
[0040] S25: Based on the trained error prediction model, predict the deviation between the simulation data and the measured data under normal working conditions.
[0041] S26: Calculate the simulation accuracy of the simulation data relative to the measured data based on the deviation between the simulation data and the measured data under normal operating conditions.
[0042] In practical applications, simulation data normalization is performed as follows: Normalization of measured data: Numerical similarity between simulation data and measured data (i.e., simulation accuracy): In the formula, To extract the number of data points of the matching data sequence from the simulation data, a matching data sequence is extracted from the measured data within the same time period. The two data sequences correspond to each other, and the number of data points is n for each sequence. For simulation data points The collected values; for The smallest collected value among the simulated data points; for The largest collected value among all simulation data points; For measured data points The collected values at the location; for The smallest collected value among the measured data points; for The largest collected value among all measured data points; For simulation data points Normalized values; For measured data points The extracted value; For measured data points Normalized values.
[0043] For a certain type of large-capacity offshore wind turbine, after conducting classic operating condition tests, an accuracy reference table is generated as shown in Table 1.
[0044] Table 1. Accuracy Test Results under Classic Operating Conditions
[0045] For other types of offshore wind turbines, if the data similarity (accuracy) is below a certain threshold (default 85%) after classic operating condition testing, the co-simulation system is considered to have failed to achieve the expected accuracy and is not suitable for subsequent safety verification. If the data similarity (accuracy) is above this threshold, subsequent steps can be performed.
[0046] In an exemplary embodiment, S3 specifically includes: S31: Determine whether the simulation accuracy meets the simulation accuracy requirements. If yes, execute S32; otherwise, execute S35.
[0047] S32: Based on the simulation accuracy, derive the numerical similarity between the simulation data and the measured data at any data point.
[0048] S33: Derive the extended range of the measured data based on the numerical similarity.
[0049] S34: Based on the extended range, determine the security verification test value of the simulation data, and calculate the deviation between the security verification test value of the measured data and the security verification test value of the simulation data.
[0050] S35: Based on the operating condition type, adjust the simulation parameters of the co-simulation system or the training parameters of the error prediction model, and return to S1.
[0051] In practical applications, based on simulation accuracy Derivation of data points Numerical similarity between simulated data and measured data: Based on data points Derivation of the measured data extension range from the prediction accuracy range of the empirical table of numerical similarity (accuracy) between simulated data and actual data: in, The test value for the security verification test of the measured data is the threshold value when it reaches the security verification value of the measured data. At this point, the system is in a critical state, posing a security risk. It is a safety verification threshold calculated based on relevant standards and design specifications. (Due to data points...) The dynamic changes in simulation accuracy. The verification value of the security verification test with simulation data The difference is also dynamic.
[0052] To achieve a reasonable simplification of the calculation process, matching data sequences can be extracted from both measured and simulated data. The numerical similarity (accuracy) of the simulation data to the measured data for a few noteworthy data points serves as a consistent simulation accuracy. The extended range of the measured data is derived, and under safe operating conditions, the following formula should be satisfied: in, The test value is the safety verification value of the measured data.
[0053] Security verification threshold for measured data in intervals containing zero points Based on the extended range of measured data, the safety verification threshold for simulation data was reset. The difference between the safety verification thresholds of simulation data and measured data should be equal to the difference between their safety verification test values, and should satisfy the following formula: when Reaching the security verification threshold of measured data hour, It also meets the safety verification threshold of simulation data. .
[0054] Due to numerical similarity Therefore, when the security verification threshold of the simulation data is actually lower than that of the measured data, using the measured data's security verification threshold for the simulation data in a co-simulation system may overlook security risks and create certain security vulnerabilities. In this case, the deviation between the security verification test value of the measured data and the security verification test value of the simulation data is: The deviation between the safety verification test value of the measured data and the safety verification test value of the simulated data can be used to derive the deviation between the safety verification threshold of the measured data and the safety verification threshold of the simulated data. In practical applications, the deviation between the safety verification threshold and the deviation between the safety verification test value are numerically equal.
[0055] Based on the extended range of measured data derived from numerical similarity, the safety verification threshold of the co-simulation system under general test conditions is corrected to form a dynamic safety verification threshold. : When the test value Reaching the dynamic security verification threshold season Substituting into the above formula, we obtain the security verification threshold based on simulation accuracy correction. : When the test value When it is less than the security verification threshold, When the test value When equal to the security verification threshold, When the test value When it exceeds the security verification threshold, Therefore, the safety verification threshold is based on simulation accuracy correction. It is more convenient to determine whether the test value exceeds the security check threshold and to divide the time period when the threshold is exceeded.
[0056] The warning range is set according to the degree of deviation between the detected value and the threshold. The deviation value is used to determine the dynamic safety verification threshold. Calculate safety warning value : Based on the dynamic safety verification threshold, the deviation values of the safety verification thresholds at various points from the main shaft of the drive motor to the main shaft of the five-degree-of-freedom loading device to the coupling to the low-speed shaft of the wind turbine under test are analyzed under the current simulation accuracy of the tested unit.
[0057] This application provides a safety verification system for a dynamic characteristic simulation device, including: The operation module is used to run the wind turbine in the joint simulation system and the ground test platform under different operating conditions, and to determine the simulation data and measured data of the dynamic characteristic simulation device under any operating condition. The dynamic characteristic simulation device includes a drive motor, a five-degree-of-freedom loading device, and a wind turbine. The different operating conditions include classic operating conditions and general operating conditions. The classic operating conditions are test conditions with measured data, and the general operating conditions are test conditions without measured data. The simulation accuracy determination module is used to calculate the simulation accuracy of the simulation data relative to the measured data based on the working condition type. The deviation value calculation module is used to calculate the deviation value between the safety verification test value of the measured data and the safety verification test value of the simulated data based on the simulation accuracy. The dynamic safety verification threshold determination module is used to correct the safety verification threshold of the co-simulation system under the general operating conditions based on the deviation value, and determine the dynamic safety verification threshold. The safety analysis report generation module is used to perform safety verification on the dynamic characteristic simulation device based on the dynamic safety verification threshold and generate a safety analysis report.
[0058] The virtual test simulation data of the transmission chain part of the ground test platform (drive motor main shaft - five-degree-of-freedom loading device main shaft - coupling - low-speed shaft of the wind turbine under test) under normal working conditions is used as the safety verification object dataset. The sources of its deviation are decomposed into electromagnetic torque fluctuation under power grid fault under test conditions, vibration of drive motor in measured data, and vibration of five-degree-of-freedom loading device based on the controllable part of the model.
[0059] Based on the simulation data and simulation accuracy calculations for each part, the corresponding extended range of measured data is derived, such as... As shown, the simulation accuracy of the co-simulation system varies depending on the simulation data under different operating conditions, and the corresponding safety verification thresholds should also have different deviations. In testing, the accuracy test results table for classic operating conditions can be used as a reference for the simulation accuracy of systems operating under similar conditions.
[0060] The deviation values of the three parts of the measured and simulated safety verification thresholds obtained from the above disassembly are calculated and evaluated on the impact on the original transmission chain load safety verification. The processed simulation data and thresholds are then fed back to the safety verification system for a dynamic characteristic simulation device provided in this application to evaluate the magnitude of its impact on transmission chain safety.
[0061] Taking the drive motor as an example, IEC 61400-1 "Wind Turbine Generator Sets - Part 1: Design Requirements" states that during extreme operating conditions, the maximum speed and torque must never continuously exceed 110% of the rated value. Therefore, 110% of the rated value is selected as the safe threshold for the output speed and torque of the drive motor shaft. However, under extreme wind speed conditions, the aerodynamic load fluctuations of the wind turbine affect the results of the transmission chain safety verification. During virtual testing, the aerodynamic torque is input to the drive motor, and the output torque and speed values of the drive motor are measured. A safety verification analysis is then performed using a co-simulation system. Figures 2-3 As shown, based on the safety verification threshold corrected for simulation accuracy, the output torque of the drive motor was found to exceed the threshold for one time period within the range of 298s to 311s, with the peak value exceeding the safety threshold, indicating a safety risk. The safety verification system then sends an alarm message.
[0062] Please take the following ①-③ as Substitution , , , and .
[0063] ① The deviation between the measured grid-connected output power and the simulated grid-connected output power of the wind turbine under test conditions is used as the deviation between the measured electromagnetic torque and the simulated electromagnetic torque of the wind turbine under test, and is taken as the reverse input load deviation signal for the transmission chain, or according to... The formula calculates the deviation value of the electromagnetic torque, where For the low-speed shaft rotation of the wind turbine, its hub is connected to the flange of the five-degree-of-freedom loading device via a coupling. ① The active power of the wind turbine connected to the grid; ② The deviation between the measured output power and the simulated output power of the drive motor under test conditions, and the deviation between the measured mechanical torque and the simulated mechanical torque of the direct drive motor spindle output, are selected as the positive input load deviation signal for the transmission chain, or according to... The formula and calculation output mechanical torque, where To drive the motor spindle speed, Input power to drive the motor, The energy conversion efficiency of the motor from electrical energy to mechanical energy; ③ The deviation between the measured output load and the simulated output load of the horizontal and vertical hydraulic actuators of the five-degree-of-freedom loading device under test conditions, which serves as the five-degree-of-freedom load deviation signal for the transmission chain.
[0064] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the above-described methods.
[0065] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0066] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0067] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0068] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0069] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0070] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0071] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0072] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for checking the security of a dynamic characteristic simulation device, characterized in that The method comprises the following steps: loading dynamic characteristic simulation devices in a joint simulation system and a ground test platform respectively to run different working conditions, and determining simulation data and measured data of the dynamic characteristic simulation devices under any working condition; the dynamic characteristic simulation devices comprise a drag motor, a five-degree-of-freedom loading device and a wind turbine; the different working conditions comprise classic working conditions and general working conditions; the classic working conditions are test working conditions with measured data; the general working conditions are test working conditions without measured data; calculating simulation accuracy of the simulation data with respect to the measured data based on the working condition type; calculating a deviation value between a safety checking test inspection value of the measured data and a safety checking inspection value of the simulation data based on the simulation accuracy; correcting a safety checking threshold of the joint simulation system under the general working condition according to the deviation value, and determining a dynamic safety checking threshold; performing safety checking on the dynamic characteristic simulation devices according to the dynamic safety checking threshold, and generating a safety analysis report.
2. The safety verification method for a dynamic characteristic simulation device according to claim 1, characterized in that, The method for calculating simulation accuracy of the simulation data with respect to the measured data based on the working condition type comprises the following steps: if the working condition type is a classic working condition, calculating simulation accuracy of the simulation data with respect to the measured data according to a deviation value of the measured data and the simulation data in the classic working condition; if the working condition type is a general working condition, extracting feature parameters in the simulation data; the feature parameters comprise a peak factor, a coefficient of variation, a skewness, a kurtosis, a maximum gradient, a gradient standard deviation, an average gradient amplitude, a high gradient area ratio, a maximum curvature, a curvature standard deviation, an average curvature amplitude, a high energy index, a frequency spectrum, an extreme point density and an average distance between extreme values; performing standardization processing on the feature parameters to determine standardized features; training an error prediction model according to the standardized features, and performing cross-validation on the trained error prediction model according to a root mean square error of a prediction error and a determination coefficient to determine the trained error prediction model; predicting a deviation value of the simulation data and the measured data under the general working condition according to the trained error prediction model; calculating simulation accuracy of the simulation data with respect to the measured data according to the deviation value of the simulation data and the measured data under the general working condition.
3. The safety verification method for a dynamic characteristic simulation device according to claim 1, characterized by, The method for calculating a deviation value between a safety checking test inspection value of the measured data and a safety checking inspection value of the simulation data based on the simulation accuracy comprises the following steps: determining whether the simulation accuracy meets simulation accuracy requirements; if yes, deriving a numerical similarity of the simulation data with respect to the measured data at any data point according to the simulation accuracy; deriving an extended interval of the measured data according to the numerical similarity; determining the safety checking inspection value of the simulation data according to the extended interval, and calculating the deviation value between the safety checking test inspection value of the measured data and the safety checking inspection value of the simulation data. If not, based on the working condition type, adjust the simulation parameters of the co-simulation system or the training parameters of the error prediction model, and return to "loading wind turbines in the co-simulation system and the ground test platform respectively, running different working conditions, determining the simulation data and the measured data of the dynamic characteristic simulation device under any working condition".
4. The safety verification method for a dynamic characteristic simulation device according to claim 1, characterized by, The deviation value between the safety check test value of the measured data and the safety check test value of the simulation data at the data point is: is: wherein, is the safety check test value of the real data, under the classical working condition, is the safety check test value of the real data, under the general working condition, ; is the numerical similarity of the simulation data to the real data, i.e. the simulation accuracy, in the selected data sequence with the length of ; is the value of the simulation data at the data point , i.e. the safety check test value of the simulation data; is the value of the real data at the data point , i.e. the safety check test value of the real data.
5. The safety verification method for a dynamic characteristic simulation device according to claim 4, characterized in that, The dynamic security check threshold Is: wherein is the safety check threshold for the measured data.
6. The safety verification method for a dynamic characteristic simulation device according to claim 5, characterized in that, According to the deviation value, the safety checking threshold of the co-simulation system under the general working condition is corrected to determine the dynamic safety checking threshold, and then the method further comprises: determining a safety check threshold of the modified simulation data according to the dynamic safety check threshold and the simulation accuracy ; .
7. A safety verification system for a dynamic characteristic simulation device, characterized in that, Comprising: The running module is used for loading wind turbines in the co-simulation system and the ground test platform respectively, running different working conditions, and determining the simulation data and the measured data of the dynamic characteristic simulation device under any working condition; the dynamic characteristic simulation device comprises a drag motor, a five-degree-of-freedom loading device, and a wind turbine; different working conditions include classic working conditions and general working conditions; wherein the classic working condition is a test working condition with measured data; the general working condition is a test working condition without measured data; The simulation accuracy determination module is used for calculating the simulation accuracy of the simulation data for the measured data based on the working condition type; The deviation value calculation module is used for calculating the deviation value between the safety checking test value of the measured data and the safety checking test value of the simulation data based on the simulation accuracy; The dynamic safety checking threshold determination module is used for correcting the safety checking threshold of the co-simulation system under the general working condition according to the deviation value to determine the dynamic safety checking threshold; The safety analysis report generation module is used for performing safety checking on the dynamic characteristic simulation device according to the dynamic safety checking threshold, and generating a safety analysis report.
8. A computer device comprising: The memory, the processor, and the computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the safety checking method for the dynamic characteristic simulation device according to any one of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the safety checking method for the dynamic characteristic simulation device according to any one of claims 1-6.
10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the safety checking method for the dynamic characteristic simulation device according to any one of claims 1-6.