A wind turbine design robustness assessment and early warning method and system

Abstract

The present invention discloses a wind turbine design robustness assessment and early warning method and system, the method comprising S1, establishing design models of various components of the wind turbine, combining different structural parameters with aerodynamic parameters, and generating multiple derivative design models; S2, performing dynamic simulation, and recording various variables in simulation data; S3, calculating statistical values ​​of various variables in the simulation data; S4, extracting derivative design model parameters that have changed relative to initial values ​​in the derivative design model, outputting a parameter stability boundary table, and establishing an abnormal feature database; S5, calculating data features, and performing early warning; the present invention can determine model parameters and related components that have a greater impact on system stability during the design phase, so as to avoid subsequent wind turbine load over-limit and wind turbine system instability due to inconsistent actual wind turbine parameters; at the same time, predicting abnormal features caused by model inconsistency, and more quickly locating inconsistent components when model parameter inconsistency occurs on site.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine control design, and in particular to a wind turbine design robustness assessment and early warning method and system. Background Art

[0002] Before manufacturing a wind turbine, it is necessary to establish a design model based on historical test data and theoretical models, and then use simulation software to verify the rationality of the design. The wind turbine control system is designed based on the linear model of the whole machine. First, it is necessary to use dynamic simulation software to establish a nonlinear model of the whole machine under nominal conditions, and then linearize the nonlinear model to obtain a linearized model. However, in the process of establishing the whole machine model, the fan structural parameters and aerodynamic parameters used are idealized theoretical parameters. This leads to inaccurate manufacturing processes or theoretical models, and the actual unit characteristics on site are inconsistent with the design model, which in turn leads to degradation of control performance. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a wind turbine design robustness assessment and early warning method and system, which determine the model parameters and related components that have a greater impact on system stability during the design stage, so as to avoid subsequent wind turbine load overload and wind turbine system instability due to inconsistent actual wind turbine parameters; at the same time, the abnormal characteristics caused by model inconsistency are predicted, and when model parameter inconsistency occurs on site, the inconsistent components can be located more quickly.

[0004] The purpose of the present invention is achieved through the following technical solution: A wind turbine design robustness assessment and early warning method comprises the following steps:

[0005] S1. Establish design models for various components of the wind turbine generator set, so that the structural parameters and aerodynamic parameters of each design model vary within a preset range, and different structural parameters and aerodynamic parameters are combined to generate multiple derivative design models;

[0006] S2, inputting the initial controller of the wind turbine into each derivative design model for dynamic simulation, and recording each variable in the simulation data;

[0007] S3, calculating the statistical value of each variable of the simulation data, and storing the statistical value in the simulation database of each derivative design model;

[0008] S4. Set a threshold, record the derived design models whose statistical values ​​of the variables are greater than the threshold, extract the derived design model parameters that have changed relative to the initial values ​​in the derived design models, and output a parameter stability boundary table; and record the derived design model features whose statistical values ​​of the variables are greater than the threshold, and establish an abnormal feature database;

[0009] S5. Calculate the data features of the real-time operating data variables of the on-site wind turbines, and compare the calculated data features with the abnormal feature database. If the data features are consistent with the abnormal features in the abnormal data feature database, output an alarm; if the data features are inconsistent with the abnormal features in the abnormal data feature database, there is no need to output an alarm.

[0010] Further, the step S1 comprises the following steps:

[0011] According to the design data of each component of the wind turbine, an initial design model is established, and the initial values ​​of key structural parameters and aerodynamic parameters are automatically extracted from the design model, and the structural parameters and aerodynamic parameters are kept within the range of [-c, c]*r n where c is a non-zero positive number, r n is the initial value of the parameter; different combinations of structural parameters and aerodynamic parameters generate derivative design models under different combinations of structural parameters and aerodynamic parameters, wherein each combination of structural parameters and aerodynamic parameters has at least 10.

[0012] Further, the step S3 comprises the following steps:

[0013] Calculate the statistical values ​​of each variable of the simulation data, and store the statistical values ​​in the simulation database of each derived design model; wherein the variables include generator speed, generator torque, pitch angle, pitch rate and power generation, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude.

[0014] Further, in step S4, the output parameter stability boundary table includes the following steps:

[0015] A threshold is set, and the derived design models whose statistical values ​​of variables are greater than the threshold are recorded. The derived design model parameters that have changed relative to the initial values ​​in the derived design model are extracted, and the model parameters are sorted from large to small according to the absolute value of the change ratio relative to the initial value, and a parameter stability boundary table is output; wherein the variables include generator speed, generator torque, pitch angle, pitch rate and power generation, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude; a database of derived design model parameters and associated components is established, and the database includes model parameter names, parameter sources, component names affected by the parameters, parameter stability boundaries and parameter influence levels.

[0016] Further, in step S4, the establishment of the abnormal feature database includes the following steps:

[0017] Set a threshold, record the simulation data of the derived design model whose statistical value of the variable is greater than the threshold, record different simulation data characteristics under different derived design model parameters, and establish an abnormal feature database; the abnormal feature database includes the changed model parameters, the involved components, the change range of the model parameters, the abnormal variable names, the abnormal data feature names and the abnormal data feature values.

[0018] Further, the step S5 comprises the following steps:

[0019] The data features of the real-time operating data variables of the on-site wind turbines are calculated, and the calculated data features are compared with the abnormal feature database. If the data features are consistent with the abnormal features in the abnormal data feature database, an alarm is output, and the alarm displays the model parameters, involved components and abnormal features corresponding to the abnormal features; if the data features are inconsistent with the abnormal features in the abnormal data feature database, there is no need to output an alarm.

[0020] A wind turbine design robustness assessment and early warning system, used in the above-mentioned wind turbine design robustness assessment and early warning method, comprising:

[0021] A derivative design model database, which includes wind turbine design models under different combinations of structural parameters and aerodynamic parameters;

[0022] A derivative model simulation database, which includes statistical values ​​of variables output by dynamics simulation of all models in the derivative design model database;

[0023] A database of model parameters and associated components, which includes the model parameter name, parameter source, component name affected by the parameter, parameter stability boundary, and parameter impact level;

[0024] An abnormal feature database, which includes the changed model parameters, the involved components, the change range of the model parameters, the abnormal variable names, the abnormal data feature names and the abnormal data feature values;

[0025] The alarm system is used to compare the data features with the abnormal feature database, and output an alarm if the data features are consistent with the abnormal features in the abnormal feature database.

[0026] Furthermore, the variables include generator speed, generator torque, pitch angle, pitch rate and power generation, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude.

[0027] A non-transitory computer-readable medium storing instructions, when the instructions are executed by a processor, executes the steps of the above-mentioned wind turbine design robustness assessment and early warning method.

[0028] A computing device comprises a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned wind turbine design robustness assessment and early warning method is implemented.

[0029] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0030] 1. The present invention changes key structural parameters and aerodynamic parameters during the design phase, generates multiple derivative design models under parameter uncertainty, and performs simulation in combination with controller parameters to determine the boundary parameters of system stability;

[0031] 2. The present invention can locate the parameters of components that are prone to instability, and can guide the design of such components during design to avoid system instability during the on-site operation stage;

[0032] 3. The present invention can detect in advance the abnormal characteristics of the unit response when there is a large inconsistency between the design parameters and the actual parameters on site, and issue an early warning based on the abnormal characteristics, so that the fault point can be quickly located during on-site operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Flowchart of robustness assessment and early warning method designed for wind turbines. DETAILED DESCRIPTION

[0034] The present invention will be further described below in conjunction with specific embodiments.

[0035] Example 1

[0036] See also Figure 1 As shown, the wind turbine design robustness assessment and early warning method provided in this embodiment includes the following steps:

[0037] S1. Establishing design models of various components of a wind turbine generator set, making the structural parameters and aerodynamic parameters of each design model vary within a preset range, and combining different structural parameters with aerodynamic parameters to generate multiple derivative design models, including the following steps:

[0038] According to the design data of each component of the wind turbine, an initial design model is established, and the initial values ​​of key structural parameters and aerodynamic parameters are automatically extracted from the design model, and the structural parameters and aerodynamic parameters are kept within the range of [-c, c]*r n where c is a non-zero positive number, r n is the initial value of the parameter; different combinations of structural parameters and aerodynamic parameters generate derivative design models under different combinations of structural parameters and aerodynamic parameters, wherein each combination of structural parameters and aerodynamic parameters has at least 10.

[0039] S2, inputting the initial controller of the wind turbine into each derivative design model for dynamic simulation, and recording each variable in the simulation data;

[0040] S3, calculating the statistical value of each variable of the simulation data, and storing the statistical value in the simulation database of each derivative design model, including the following steps:

[0041] Calculate the statistical values ​​of each variable of the simulation data, and store the statistical values ​​in the simulation database of each derived design model; wherein the variables include generator speed, generator torque, pitch angle, pitch rate and power generation, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude.

[0042] S4. Set a threshold, record the derived design models whose statistical values ​​of the variables are greater than the threshold, extract the derived design model parameters that have changed relative to the initial values ​​in the derived design models, output the parameter stability boundary table, and determine the boundary parameters used to evaluate the stability of the system; and record the derived design model features whose statistical values ​​of the variables are greater than the threshold, and establish an abnormal feature database;

[0043] The output parameter stability boundary table comprises the following steps:

[0044] A threshold is set, and the derived design models whose statistical values ​​of variables are greater than the threshold are recorded. The derived design model parameters that have changed relative to the initial values ​​in the derived design model are extracted, and the model parameters are sorted from large to small according to the absolute value of the change ratio relative to the initial value, and a parameter stability boundary table is output; wherein the variables include generator speed, generator torque, pitch angle, pitch rate and power generation, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude; a database of derived design model parameters and associated components is established, and the database includes model parameter names, parameter sources, component names affected by the parameters, parameter stability boundaries and parameter influence levels.

[0045] The establishment of the abnormal feature database comprises the following steps:

[0046] Set a threshold, record the simulation data of the derived design model whose statistical value of the variable is greater than the threshold, record different simulation data characteristics under different derived design model parameters, and establish an abnormal feature database; the abnormal feature database includes the changed model parameters, the involved components, the change range of the model parameters, the abnormal variable names, the abnormal data feature names and the abnormal data feature values.

[0047] S5, calculating data features of the real-time operation data variables of the on-site wind turbines, comparing the calculated data features with the abnormal feature database, and outputting an alarm if the data features are consistent with the abnormal features in the abnormal data feature database; if the data features are inconsistent with the abnormal features in the abnormal data feature database, there is no need to output an alarm, including the following steps:

[0048] The data features of the real-time operating data variables of the on-site wind turbines are calculated, and the calculated data features are compared with the abnormal feature database. If the data features are consistent with the abnormal features in the abnormal data feature database, an alarm is output, and the alarm displays the model parameters, involved components and abnormal features corresponding to the abnormal features; if the data features are inconsistent with the abnormal features in the abnormal data feature database, there is no need to output an alarm.

[0049] Example 2

[0050] The wind turbine design robustness assessment and early warning system provided in this embodiment is used to implement the wind turbine design robustness assessment and early warning method described in Embodiment 1, including:

[0051] A derivative design model database, which includes wind turbine design models under different combinations of structural parameters and aerodynamic parameters;

[0052] A derivative model simulation database, which includes statistical values ​​of variables output by dynamic simulation of all models in the derivative design model database; the variables include generator speed, generator torque, pitch angle, pitch rate and power generation power, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude.

[0053] A database of model parameters and associated components, which includes the model parameter name, parameter source, component name affected by the parameter, parameter stability boundary, and parameter impact level;

[0054] An abnormal feature database, which includes the changed model parameters, the involved components, the change range of the model parameters, the abnormal variable names, the abnormal data feature names and the abnormal data feature values;

[0055] The alarm system is used to compare the data features with the abnormal feature database, and output an alarm if the data features are consistent with the abnormal features in the abnormal feature database.

[0056] Example 3

[0057] This embodiment discloses a non-transitory computer-readable medium storing instructions. When the instructions are executed by a processor, the steps of the wind turbine design robustness assessment and early warning method according to Embodiment 1 are executed.

[0058] The non-temporary computer-readable medium in this embodiment may be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a USB flash drive, a mobile hard disk, or other media.

[0059] Example 4

[0060] This embodiment discloses a computing device, including a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the wind turbine design robustness assessment and early warning method described in Embodiment 1 is implemented.

[0061] The computing device described in this embodiment may be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer, a programmable logic controller (PLC), or other terminal devices with a processor function.

[0062] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A wind turbine design robustness assessment and early warning method, characterized in that: The following steps are involved: S1. Establishing design models of various components of a wind turbine generator set, making the structural parameters and aerodynamic parameters of each design model vary within a preset range, and combining different structural parameters with aerodynamic parameters to generate multiple derivative design models, including the following steps: According to the design data of each component of the wind turbine, an initial design model is established, and the initial values ​​of key structural parameters and aerodynamic parameters are automatically extracted from the design model, and the structural parameters and aerodynamic parameters are kept within the range of [-c, c]*r n where c is a non-zero positive number, r n is the initial value of the parameter; different combinations of structural parameters and aerodynamic parameters generate derivative design models under different combinations of structural parameters and aerodynamic parameters, wherein each combination of structural parameters and aerodynamic parameters has at least 10; S2, inputting the initial controller of the wind turbine into each derivative design model for dynamic simulation, and recording each variable in the simulation data; S3, calculating the statistical value of each variable of the simulation data, and storing the statistical value in the simulation database of each derivative design model; S4. Set a threshold, record the derived design models whose statistical values ​​of the variables are greater than the threshold, extract the derived design model parameters that have changed relative to the initial values ​​in the derived design models, output the parameter stability boundary table, and determine the boundary parameters used to evaluate the stability of the system; and record the derived design model features whose statistical values ​​of the variables are greater than the threshold, and establish an abnormal feature database; S5. Calculate the data features of the real-time operating data variables of the on-site wind turbines, and compare the calculated data features with the abnormal feature database. If the data features are consistent with the abnormal features in the abnormal data feature database, output an alarm; if the data features are inconsistent with the abnormal features in the abnormal data feature database, there is no need to output an alarm.

2. A wind turbine design robustness assessment and early warning method according to claim 1, characterized in that: The step S3 comprises the following steps: Calculate the statistical values ​​of each variable of the simulation data, and store the statistical values ​​in the simulation database of each derived design model; wherein the variables include generator speed, generator torque, pitch angle, pitch rate and power generation, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude.

3. A wind turbine design robustness assessment and early warning method according to claim 1, characterized in that: In step S4, the output parameter stability boundary table includes the following steps: A threshold is set, and the derived design models whose statistical values ​​of variables are greater than the threshold are recorded. The derived design model parameters that have changed relative to the initial values ​​in the derived design model are extracted, and the model parameters are sorted from large to small according to the absolute value of the change ratio relative to the initial value, and a parameter stability boundary table is output; wherein the variables include generator speed, generator torque, pitch angle, pitch rate and power generation, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude; a database of derived design model parameters and associated components is established, and the database includes model parameter names, parameter sources, component names affected by the parameters, parameter stability boundaries and parameter influence levels.

4. A wind turbine design robustness assessment and early warning method according to claim 1, characterized in that: In step S4, the establishment of an abnormal feature database includes the following steps: Set a threshold, record the simulation data of the derived design model whose statistical value of the variable is greater than the threshold, record different simulation data characteristics under different derived design model parameters, and establish an abnormal feature database; the abnormal feature database includes the changed model parameters, the involved components, the change range of the model parameters, the abnormal variable names, the abnormal data feature names and the abnormal data feature values.

5. A wind turbine design robustness assessment and early warning method according to claim 1, characterized in that: The step S5 comprises the following steps: The data features of the real-time operating data variables of the on-site wind turbines are calculated, and the calculated data features are compared with the abnormal feature database. If the data features are consistent with the abnormal features in the abnormal data feature database, an alarm is output, and the alarm displays the model parameters, involved components and abnormal features corresponding to the abnormal features; if the data features are inconsistent with the abnormal features in the abnormal data feature database, there is no need to output an alarm.

6. A wind turbine design robustness assessment and early warning system, characterized in that: The method for evaluating and warning the robustness of wind turbine design according to any one of claims 1 to 5 comprises: A derivative design model database, which includes wind turbine design models under different combinations of structural parameters and aerodynamic parameters; A derivative model simulation database, which includes statistical values ​​of variables output by dynamics simulation of all models in the derivative design model database; A database of model parameters and associated components, which includes the model parameter name, parameter source, component name affected by the parameter, parameter stability boundary, and parameter impact level; An abnormal feature database, which includes the changed model parameters, the involved components, the change range of the model parameters, the abnormal variable names, the abnormal data feature names and the abnormal data feature values; The alarm system is used to compare the data features with the abnormal feature database, and output an alarm if the data features are consistent with the abnormal features in the abnormal feature database.

7. A wind turbine design robustness assessment and early warning system according to claim 6, characterized in that: The variables include generator speed, generator torque, pitch angle, pitch rate and generated power, and the statistical values ​​include the difference between the maximum and minimum values, the standard deviation and the power spectrum amplitude.

8. A non-transitory computer-readable medium storing instructions, characterized in that: When the instruction is executed by the processor, the steps of the wind turbine design robustness assessment and early warning method according to any one of claims 1 to 5 are performed.

9. A computing device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the wind turbine design robustness assessment and early warning method described in any one of claims 1 to 5 is implemented.

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