Wind driven generator load reduction design and safety coefficient checking method based on reliability

Abstract

The invention discloses a reliability-based wind driven generator load reduction design and safety coefficient checking method, which considers the multi-source uncertainty of wind regime parameters, material attributes, aerodynamic characteristics and the like of the whole life cycle of a wind driven generator, and converts the multi-source uncertainty into the uncertainty of load and structural resistance. And reliability checking is carried out on the optimal design under different safety coefficients, and the optimal design meeting the reliability requirement is found. According to the method, the subitem safety coefficients are innovatively introduced into the reliability optimization design process of the wind driven generator component, the optimization of the design variables is carried out by adjusting the subitem safety coefficients and combining the deterministic design process, the reliability analysis frequency in the optimization design process is greatly reduced, and the calculation efficiency is improved. According to the method, the optimal design considering the reliability is provided, and meanwhile, the subentry safety coefficient after checking is provided. And by utilizing the checked safety coefficient, a designer of the wind driven generator can use a lower load design value, so that the load reduction design of the wind driven generator is realized.

Description

Technical Field

[0001] The invention belongs to the field of uncertainty optimization design of wind turbine components, and in particular relates to a reliability-based wind turbine load reduction design and safety factor verification method. Background Art

[0002] Most of the existing wind turbine design theories adopt a deterministic design process, that is, based on the physical mechanism of the failure form, the characteristic values ​​of the load and structural resistance are calculated, and the partial safety factor is combined to form a deterministic design constraint. This method is simple and feasible, so it is widely used in the wind power design industry. However, the current values ​​of the partial safety factor are mostly determined directly based on engineering experience. Due to the lack of guidance from reliability analysis theory and the randomness analysis of loads and structural resistance caused by multi-source uncertainties such as wind parameters, material properties, and aerodynamic characteristics of wind turbines, it is difficult to establish a mapping between the existing partial safety factor values ​​and the reliability of wind turbines, which usually leads to overly redundant designs. Therefore, the reliability-based design optimization (RBDO) theory has been widely studied in recent years and has been applied to a certain extent in the industry.

[0003] RBDO makes full use of the multi-source uncertainty statistical model of wind turbines, and verifies the design through reliability analysis, replacing the deterministic design equation based on partial safety factors, thereby achieving low-cost design while ensuring design reliability. However, reliability analysis itself requires a lot of computing resources, and in the traditional RBDO process, once the design changes, a reliability analysis is required, so the amount of calculation is greatly increased compared to the current deterministic design process. In addition, wind turbines have complex structures, high-dimensional design variables, and many failure modes. Directly applying the traditional RBDO process to the design of wind turbine components will cause problems with difficult iterative convergence. Summary of the invention

[0004] In order to solve the shortcomings of the prior art, realize the load reduction design of wind turbines, reduce the safety factor redundancy, and reduce the load design value in the deterministic design process, the present invention adopts the following technical solutions:

[0005] The reliability-based wind turbine load reduction design and safety factor verification method includes the following steps:

[0006] Step S1: determining the components of the wind turbine for load reduction design, and determining the design variables, objective functions and failure modes;

[0007] Step S2: Establishing a multi-source uncertainty characterization model for wind turbines, including but not limited to wind condition parameter uncertainty and other uncertainties such as aerodynamic properties and material properties;

[0008] Step S3: Select the design load condition of the wind turbine generator, determine the wind condition parameters within a certain time range and the corresponding probability of the wind condition parameters within a certain regression period; simulate the performance model of the wind turbine generator under the wind condition parameters within a certain time range, and fit the maximum conditional probability distribution of the random load under the wind condition parameters within the regression period;

[0009] Step S4: For the remaining uncertainties except the wind condition parameters, a corresponding load uncertainty factor statistical model is established to characterize the influence of the remaining uncertainty sources on the random load alone;

[0010] Step S5: Based on the failure mode mechanism, construct design equations containing safety factors and the loads and structural resistance characteristic values, as well as limit state equations containing the random loads and structural resistance; determine a set of safety factors, and construct and solve a deterministic optimization problem of minimizing the prime number objective function with the design equations as constraints to obtain the optimal design variables; perform reliability verification on the optimal design variables in combination with the limit state equations, and if the reliability verification passes, slightly reduce the safety factor, otherwise, slightly increase the safety factor; re-determine a set of safety factors for the above verification, and when the algorithm meets the convergence requirements, select the design variables with the smallest objective function and that meet the reliability requirements and their corresponding safety factors as the results of the wind turbine load reduction design and safety factor verification.

[0011] Furthermore, in step S3, the wind condition parameter samples {(v i ,c i )},i=1,…,N W , and the probability of each sample P(v i ,c i ), v i represents the average wind speed sample within a certain time range, c i Representing the remaining wind parameters under the design load condition includes the following steps:

[0012] Step S3.1: Determine the probability distribution of wind parameters within a certain time range based on measured data or design standards i=1,…,d c , d c The dimension representing the wind condition parameters within a certain time range;

[0013] Step S3.2: According to the Rosenblatt transformation, all wind parameters within a certain time range are converted into standard normal random variables, that is:

[0014] Φ(u v )=F V (v)

[0015]

[0016] Among them, u v It represents the standard normal random variable of the average wind speed within a certain time range after Rosenblatt transformation. represents the standard normal random variable after Rosenblatt transformation of wind parameters within a certain time range, Φ(·) represents the cumulative distribution function of the standard normal random variable;

[0017] Step S3.3: Calculate the wind condition parameters within a certain time range, and the corresponding probability within a certain regression period is β 1Y ;

[0018]

[0019] β 1Y =-Φ -1 (P) = 4.119

[0020] Step S3.4: According to the inverse first-order reliability analysis method, Need to meet:

[0021]

[0022] Step S3.5: According to the inverse transformation of step S3.2, all wind parameters within a certain time range need to satisfy:

[0023]

[0024] Among them, Φ -1 (·) represents the inverse function of the cumulative distribution function of the standard normal random variable;

[0025] Step S3.6: according to the cut-in and cut-out wind speed ranges of the selected wind turbine type, the average wind speed within a certain time range is sampled at a certain interval speed ΔV (usually 2m / s), and the remaining wind condition parameter samples are determined through step S3.5;

[0026] Step S3.7: Determine the probability P(v i ,c i ).

[0027] Furthermore, in step S3, in each certain time range, the wind condition parameter samples {(v i ,c i )}, multiple simulations of wind turbine performance models considering turbulent wind were performed to fit {(v i ,c iThe conditional probability distribution F of the annual maximum value of the random load L under L (l|v i ,c i ), where F (·) represents the cumulative distribution function, v i represents the average wind speed sample within a certain time range, c i represents the other wind parameters under the design load condition, and l represents the load extreme value of the wind turbine within a certain time range.

[0028] Furthermore, in step S4, the establishment of the load uncertainty factor statistical model includes the following steps:

[0029] Step S4.1: Based on the statistical model of the remaining uncertainties, collect N X samples;

[0030] Step S4.2: At the rated wind speed of the current wind turbine, generate N W A time series of turbulent wind within a certain time range;

[0031] Step S4.3: Select a turbulent wind time series, traverse all other uncertainty source samples, simulate the performance of wind turbines within a certain time range, obtain the load time series, and obtain N X load extreme value samples;

[0032] Step S4.4: For N X The load extreme value samples are normalized and the normalized load extreme value standard deviation σ is calculated;

[0033] Step S4.5: Repeat steps S4.3 to S4.4 until all N W Turbulent wind time series, get N W The standard deviation of the load extremes;

[0034] Step S4.6: Establish the load uncertainty factor X (·) The statistical model has a distribution type specified by the design criteria, a sample mean of 1, and a sample standard deviation of N obtained in step S4.5. W The standard deviations of the load extremes are averaged.

[0035] Furthermore, in step S5, the deterministic optimization problem of determining a set of safety factors is specifically:

[0036] minimize Cost(d)

[0037] Subject to G i (d; L k ,R k ,γ)>0,i=1,...,n

[0038] d L ≤d≤d U ,d∈R d

[0039] Among them, Cost(·) represents the objective function, G i (·) represents the design equation, L k ,R k denote the characteristic values ​​of load and structural resistance, γ denotes the safety factor, and d L ,d U They represent the upper and lower bounds of the design variables, n represents the number of design equations, and d represents the dimension of the design variables.

[0040] Furthermore, in step S5, the reliability check is performed by determining whether the following equations are all valid for i=1, ..., n:

[0041] β i =-Φ -1 (P f,i )≥β t

[0042] Among them, β i Indicates the reliability level of the calibration, Φ -1 (·) represents the inverse function of the cumulative distribution function of the standard normal random variable, β t Indicates the required reliability level, P f,i represents the failure probability of the i-th design equation, and the calculation formula is:

[0043] P f,i =P(g i (d; l, r, X) < 0)

[0044] Among them, g i (·) represents the limit state function, L represents the random load, R represents the structural resistance, and X represents the load uncertainty factor.

[0045] Furthermore, in step S5, the failure probability and reliability calculation of each limit state function includes the following steps:

[0046] Step S5.1: According to the failure probability required by the design standard, under each wind parameter sample within a certain time range, determine the number of samples in each round and the total number of sampling rounds;

[0047] Step S5.2: According to the statistical distribution of the characterized random loads and structural resistance, collect joint samples of each random load, structural resistance and uncertainty factor to calculate the limit state equation and obtain the number of failure samples;

[0048] Step S5.3: Calculate the failure probability and the error of the failure probability estimation by the number of failure samples and the number of samples;

[0049] Step S5.4: If the error is less than the threshold or there is no failed sample or the round ends, the probability of the limit state being less than 0 is taken as the failure probability calculation result. Otherwise, a new round is performed and steps S5.2 and S5.3 are executed;

[0050] Step S5.5: Repeat steps S5.1 to S5.3 until all wind condition parameter samples {(v i ,c i )};

[0051] Step S5.6: Based on the obtained failure probability calculation result and the probability of each sample obtained in step S3, calculate the total failure probability of the limit state function, and calculate the reliability index based on the total failure probability.

[0052] Furthermore, in step S5.1, according to the failure probability P required by the design standard, f,t =Φ(-β t ), in each certain time range T, the wind condition parameter sample {(v i ,c i )}, determine the number of samples N in each round b =T / P f,t And the total number of sampling rounds K, set the sampling round k = 1, v i represents the average wind speed sample within a certain time range, c i Represents the remaining wind parameters under the design load condition;

[0053] In step S5.2, according to the statistical distribution model of the random load and structural resistance, N b The combined sample of random load, structural resistance and uncertainty factor is calculated to obtain N b The value of the limit state equation is recorded, and the failure, that is, the limit state g i The number of samples N < 0 f,k ;

[0054] In step S5.3, the failure probability P is calculated. f,k The error δ with the failure probability estimate k :

[0055]

[0056] Furthermore, in step S5.4, if δ k The number of samples N that are less than the threshold or limit state f,k= 0 or round k is the total number of rounds K, the reliability analysis calculation is terminated, and the probability P(g<0|v i ,c i )=P f,k As {(v i ,c i )}, otherwise, let k = k + 1 and repeat steps S5.2 and S5.3, v i represents the average wind speed sample within a certain time range, c i Represents the remaining wind parameters under this design load condition.

[0057] Furthermore, in step S5.6, the failure probability of the limit state function is:

[0058]

[0059] The calculated reliability index is:

[0060] β=-Φ -1 (P f ).

[0061] Constructing the design equation and the limit state equation to the deterministic optimization problem of minimizing the objective function and determining a set of safety factors correspond to two different optimization problems, which can be solved using optimization algorithms such as the interior point method, genetic algorithm, and sequential quadratic programming.

[0062] The advantages and beneficial effects of the present invention are:

[0063] In response to the existing deterministic design redundancy of wind turbines and the problem that the overall calculation time of the RBDO process is too long, the present invention introduces partial safety factors into the traditional RBDO process, and optimizes the design variables through the deterministic design process, which greatly reduces the number of reliability analysis calculations in the optimization design process. When the design variable dimension is high, the method of the present invention can converge to the optimal design more quickly. In addition, while outputting the optimal design that takes reliability into consideration, the method of the present invention also provides a verified partial safety factor. Using the verified safety factor, wind turbine designers can use lower load design values, thereby realizing the reduced load design of wind turbines. The verified partial safety factor can be transferred to similar models, and the reduced load design can be quickly realized directly through the deterministic design process. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a flow chart of the method of the present invention.

[0065] Figure 2 It is a schematic diagram of a wind turbine tower according to an embodiment of the present invention.

[0066] Figure 3 Schematic diagram of wind parameter sampling under DLC 1.3 working condition for an embodiment of the present invention.

[0067] Figure 4 Schematic diagram of wind parameter sampling under DLC 1.4 working condition for an embodiment of the present invention.

[0068] Figure 5 It is a comparison diagram before and after the optimized design of the diameter of each section of the tower according to the embodiment of the present invention.

[0069] Figure 6 It is a comparison diagram before and after the optimized design of the wall thickness of each section of the tower according to the embodiment of the present invention. DETAILED DESCRIPTION

[0070] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0071] The reliability-based wind turbine load reduction design and safety factor verification method of the present invention establishes an uncertainty model of load and structural resistance while considering the multi-source uncertainty of the wind turbine. The present invention iteratively adjusts the partial safety factor, quickly obtains the design variables through the deterministic design process, and then performs a verification based on reliability analysis, thereby searching for the optimal design of the wind turbine components that meets the reliability requirements of the design standards, and at the same time obtains the partial safety factor results after verification. Figure 1 As shown, the specific steps include:

[0072] Step S1: determining the components of the wind turbine for load reduction design, and determining the design variables, objective functions and failure modes;

[0073] Step S2: Establishing a multi-source uncertainty characterization model for wind turbines, including but not limited to uncertainties such as wind parameters, aerodynamic properties, and material properties;

[0074] Step S3: Select the design load condition of the wind turbine generator and determine the 10-minute wind condition parameter sample {(v i ,c i )},i=1,…,N W , and the probability of each sample P(v i ,c i ) where v i represents the 10-minute average wind speed sample, c i are the other wind parameters under the design load condition; in each 10-minute wind parameter sample {(v i ,c i )}, multiple simulations of wind turbine performance models considering turbulent wind were performed to fit {(vi ,c i The conditional probability distribution F of the annual maximum value of the random load L under L (l|v i ,c i ), where F (·) represents the cumulative distribution function;

[0075] Specifically, the determination of the 10-minute wind condition parameter sample includes the following steps:

[0076] Step S3.1: Determine the probability distribution of each 10-minute wind condition parameter based on measured data or design standards i=1,…,d c , d c is the dimension of the wind condition parameters for the remaining 10 minutes;

[0077] Step S3.2: According to the Rosenblatt transformation, all 10-minute wind parameters are transformed into standard normal random variables, that is:

[0078] Φ(u v )=F V (v)

[0079]

[0080] Step S3.3: Calculate the probability of 10-minute wind parameters with a regression period of 1 year:

[0081]

[0082] β 1Y =-Φ -1 (P) = 4.119

[0083] Step S3.4: According to the inverse first-order reliability analysis method, Need to meet:

[0084]

[0085] Step S3.5: According to the inverse transformation of step S3.2, all 10-minute wind condition parameters need to satisfy:

[0086]

[0087] Step S3.6: According to the cut-in and cut-out wind speed ranges of the selected wind turbine type, the 10-minute average wind speed is sampled at intervals of ΔV (usually 2 m / s), and the remaining wind condition parameter samples are determined through step S3.5.

[0088] Step S3.7: Determine the probability P(v i ,c i ).

[0089] Step S4: For the remaining uncertainties except the wind condition parameters, establish the corresponding load uncertainty factor X (·) A statistical model characterizing the effect of this uncertainty source alone on the random loads;

[0090] Specifically, the load uncertainty factor X (·) The calculation of the statistical model includes the following steps:

[0091] Step S4.1: According to the statistical model of the uncertainty, collect N X samples;

[0092] Step S4.2: Generate N at the rated wind speed of the model W A 10-minute turbulent wind time series;

[0093] Step S4.3: Select a turbulent wind time series, traverse all uncertainty source samples, perform a 10-minute wind turbine performance simulation, obtain the load time series, and obtain N X load extreme value samples;

[0094] Step S4.4: For N X The load extreme value samples are normalized and the normalized load extreme value standard deviation σ is calculated;

[0095] Step S4.5: Repeat steps S4.3 to S4.4 until all N W Turbulent wind time series, get N W The standard deviation of the load extremes;

[0096] Step S4.6: Load uncertainty factor X (·) The statistical model has a distribution type specified by the design criteria, a sample mean of 1, and a sample standard deviation of N obtained in step S4.5. W The standard deviations of the load extremes are averaged.

[0097] Step S5: Based on the failure mode mechanism, construct design equations containing safety factors and loads, structural resistance characteristic values, and limit state equations containing random loads and structural resistance respectively; determine a set of safety factors, and construct and solve the deterministic optimization problem of minimizing the objective function with the design equations as constraints to obtain the optimal design variables; perform reliability verification on the optimal design variables in combination with the limit state equations, and if the reliability verification passes, slightly reduce the safety factor, otherwise, slightly increase the safety factor, re-determine a set of safety factors, and perform subsequent verification; when the algorithm meets the convergence requirements, select the design variables with the smallest objective function and that meet the reliability requirements and their corresponding safety factors as the results of the wind turbine load reduction design and safety factor verification.

[0098] Specifically, the deterministic optimization problem of determining a set of safety factors is:

[0099] minimize Cost(d)

[0100] Subject to G i (d; L k ,R k ,γ)>0,i=1,...,n

[0101] d L ≤d≤d U ,d∈R d

[0102] Among them, G i (·) represents the design equation, L k ,R k represent the characteristic values ​​of load and structural resistance respectively, γ represents the safety factor, d L ,d U They represent the upper and lower bounds of the design variables, n represents the number of design equations, and d represents the dimension of the design variables.

[0103] Specifically, the reliability check is performed by equivalently checking whether the following equation holds for all i=1,…,n:

[0104] β i =-Φ -1 (P f,i )≥β t

[0105] Among them, P f,i is the failure probability, and the calculation formula is:

[0106] P f,i =P(g i (d; L, R, X) < 0)

[0107] Among them, g i(·) represents the limit state function, L represents the random load, R represents the structural resistance, and X is the load uncertainty factor.

[0108] Specifically, the failure probability and reliability calculation of each limit state function includes the following steps:

[0109] Step S5.1: Failure probability P according to design standard requirements f,t =Φ(-β t ), in each 10-minute wind parameter sample {(v i ,c i )}, determine the number of samples N in each round b =20 / P f,t And the total number of sampling rounds K, set the sampling round k = 1;

[0110] Step S5.2: Based on the statistical distribution model of random loads and structural resistance characterized in steps S3 and S4, collect

[0111] N b The combined sample of random load, structural resistance and uncertainty factor is calculated to obtain N b The value of the limit state equation is recorded, and the failure (i.e. g i <0) f,k ;

[0112] Step S5.3: Calculate the failure probability P f,k The error δ with the failure probability estimate k :

[0113]

[0114] Step S5.4: If any of the following three convergence conditions is met, the reliability analysis calculation is terminated.

[0115] P(g<0|v i ,c i )=P f,k As {(v i ,c i )}, otherwise set k = k + 1 and repeat the steps

[0116] S5.2 and step S5.3: (1) δ k Less than a certain threshold (usually 0.1 or 0.05); (2) N f,k =0; (3) k = K;

[0117] Step S5.5: Repeat steps S5.1 to S5.3 until all {(v i ,c i )};

[0118] Step S5.6: The failure probability of the limit state function is calculated as:

[0119]

[0120] The calculated reliability index is:

[0121] β=-Φ -1 (P f ).

[0122] Furthermore, the design equations and the limit state equations are constructed to the deterministic optimization problem of minimizing the objective function, and a set of safety factors are determined, which correspond to two different optimization problems and can be solved using optimization algorithms such as the interior point method, genetic algorithm, and sequential quadratic programming.

[0123] Example

[0124] In order to verify the effectiveness of the present invention, an optimization design was carried out on a tower of a certain type of Dongfang Wind Power Co., Ltd. Figure 2 As shown, the tower has 7 sections with a total of 47 sections, including five conical sections and two cylindrical sections. The wall thickness of each section of the tower (denoted as t1, t2, ..., t 47 ) and diameter (denoted as d1, d2, ..., d 47 ) is a design variable. Since the top section is connected to the nacelle flange, its diameter d 47 and wall thickness t 47 In addition, only the diameter of the bottom of the tower cone section (in this case, d1, d2, d 15 ,d 23 ,d 39 ), the diameter of the remaining sections can be obtained by linear interpolation based on the height of the section. Therefore, the design variable in this example is d = [d1, d2, d 15 ,d 23 ,d 39 ,t1,t2,…,t 46 ], a total of 51.

[0125] The multi-source uncertainties of the wind turbine considered in this example mainly include the uncertainties of wind parameters, site conditions, aerodynamic properties, simulation model errors, dynamic properties and material properties. For the selection of sample points of wind parameters, this example selects the "DLC 1.3 Extreme Turbulent Wind (ETM)" and "DLC 1.4 Extreme Gusts with Wind Direction Changes (ECD)" design load conditions required by the IEC61400-1 standard. The wind parameter samples under DLC 1.3 and DLC 1.4 obtained by step S3 of the present invention are as follows: Figure 3 , Figure 4As shown in Figure 2, for the uncertainty of aerodynamic properties, the wind tunnel test data of the National Renewable Energy Laboratory (NREL) of the United States is used; for material properties, this embodiment is based on the recommendation of the Joint Committee on Structural Safety (JCSS), assuming that the Young's modulus of the material follows a log-normal distribution, and the sample mean is 2.1×10 5 MPa, and the coefficient of variation is 0.03. Through step S5 of the present invention, the load uncertainty factor X corresponding to the aerodynamic properties is obtained respectively. aero Load uncertainty factor X corresponding to material property uncertainty mat Statistical model. For the load uncertainty factors corresponding to the site conditions, simulation model errors and dynamic properties, the statistical distribution model required by the IEC 61400-9 standard is adopted, as shown in Table 1;

[0126] Table 1 Statistical distribution model of load uncertainty factors

[0127] Sources of uncertainty Load uncertainty factor Statistical distribution type Sample mean Coefficient of variation Site conditions <![CDATA[X site ]]> Lognormal distribution 1 0.10 Simulation model error <![CDATA[X sim ]]> Lognormal distribution 1 0.05 Dynamic properties <![CDATA[X dyn ]]> Lognormal distribution 1 0.05

[0128] In this example, the objective function cost(d) is the amount of structural steel used for the tower, that is,

[0129]

[0130] Among them, h i represents the tower height of the i-th section.

[0131] In this example, the failure modes of the tower considered include axial buckling failure, tangential buckling failure and maximum stress failure. These failure modes need to be checked for each section of the tower. If any section of the tower fails, the entire tower is considered to have failed. The limit state function of axial buckling failure is:

[0132]

[0133] Among them, χ x is the axial buckling reduction factor, which is calculated according to Section 8.5.2 of EN 1993-1-6:2007 based on the diameter, thickness, Young's modulus and yield strength of each tower section; f y is the yield strength of structural steel; F z represents the axial structural response force generated at the section of the tower; A represents the cross-sectional area of ​​the tower; M xy W represents the structural response moment generated in the tower section; b represents the bending stiffness of the tower section. Accordingly, the design equation for axial buckling failure is:

[0134]

[0135] Among them, γ M Represents the material partial safety factor, γ G Indicates the partial safety factor of gravity load (due to F z Mainly generated by the gravity of the nacelle and wind rotor acting on the tower), γ W Represents the partial safety factor of wind load, f yk ,F z,k ,M xy,k They respectively represent the characteristic values ​​of the yield strength of structural steel, the axial structural response force, and the structural response moment within the section.

[0136] The limit state function of tangential buckling failure is:

[0137]

[0138] Among them, χ τ is the axial buckling reduction factor, which is calculated according to Section 8.5.2 of EN 1993-1-6:2007 based on the diameter, thickness, Young's modulus and yield strength of each tower section; F xy represents the tangential structural response force generated at the tower section; M z represents the structural response torque generated at the tower section. Accordingly, the design equation for tangential buckling failure is:

[0139]

[0140] Among them, F xy,k ,M z,k They represent the eigenvalues ​​of the tangential structural response force and the structural response torque respectively.

[0141] The limit state function of maximum stress failure is:

[0142]

[0143] Among them, σ x ,τ are the maximum axial and tangential stresses on the tower section:

[0144]

[0145] Accordingly, the design equation for maximum stress failure is:

[0146]

[0147] In this example, the reliability level required in step S5 is β t =3.3, corresponding to the failure probability P f,t=5×10 -4 In this example, the fixed material partial safety factor γ M is 1.10, and the partial safety factor of the load is γ W ,γ G Carry out verification.

[0148] The results obtained by the method of the present invention are shown in Table 2 (wherein, the reliability " / " represents that the failure probability calculated by the method of step S9 of the present invention is extremely low). In contrast, the traditional RBDO method (for example: Oza, K, & Gea, HC. "Two-Level Approximation Method for Reliability-Based Design Optimization." Proceedings of the ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 1: 30th Design Automation Conference. Salt Lake City, Utah, USA. September 28–October 2, 2004. pp. 885-891. ASME. https: / / doi.org / 10.1115 / DETC2004-57463) faces this high-dimensional design problem. Because it needs to perform a large amount of reliability analysis when optimizing the design variables in each round of iteration, the calculation amount is too large and the design result cannot be given within an acceptable time. As a comparison, the method proposed in the present invention successfully provides the results of the optimized design within an acceptable calculation time (the optimized tower diameter and wall thickness are as follows: Figure 5 , Figure 6 The comparison of the design results given in Table 2 shows that the current wind turbine design standard IEC 61400-1 stipulates that the safety factor (γ W =1.35,γ G =1.10) under different working conditions, the various failure modes have a certain degree of reliability margin. The optimized design obtained by the present invention reduces the steel consumption by about 3.19%, and the axial buckling failure reliability under the DLC 1.4 working condition is 3.31, which is close to the design requirement boundary and reduces the design redundancy. The load partial safety factor after verification is: γ W =1.281,γ G =1.082, wind load (F xy ,M xy ,M z) and gravity load (M z ) design values ​​decreased by 5.11% and 1.64% respectively, achieving the load reduction design of the wind turbine. This result fully illustrates that the method of the present invention, by combining the safety factor and the eigenvalue to establish the design equation, avoids the reliability analysis in the process of optimizing the design variables, and only performs a reliability check on the iterative design once, thereby greatly reducing the amount of calculation in high-dimensional design problems. At the same time, the method of the present invention fully combines the characteristics of RBDO, reducing design redundancy while ensuring reliability.

[0149] Table 2 Comparison of calculation results of the embodiments

[0150]

[0151] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A reliability-based wind turbine load reduction design and safety factor verification method, characterized by: The steps include: Step S1: determining the components of the wind turbine for load reduction design, and determining the design variables, objective functions and failure modes; Step S2: Establishing a multi-source uncertainty characterization model for wind turbines, including wind condition parameter uncertainty and other uncertainties; Step S3: Select the design load condition of the wind turbine generator, determine the wind condition parameters within a certain time range and the corresponding probability of the wind condition parameters within a certain regression period; simulate the performance model of the wind turbine generator under the wind condition parameters within a certain time range, and fit the maximum conditional probability distribution of the random load under the wind condition parameters within the regression period; Step S4: For the remaining uncertainties, a corresponding load uncertainty factor statistical model is established to characterize the influence of the remaining uncertainty sources on the random load alone; Step S5: Based on the failure mode mechanism, constructing a design equation containing a safety factor and the load, a characteristic value of structural resistance, and a limit state equation containing the random load and structural resistance; A set of safety factors is determined, and a deterministic optimization problem of minimizing the prime objective function is constructed and solved with the design equation as a constraint to obtain the optimal design variables; reliability verification is performed on the optimal design variables in combination with the limit state equation, and if the reliability verification passes, the safety factor is slightly lowered, otherwise, the safety factor is slightly increased; a set of safety factors is re-determined for the above verification, and when the convergence requirements are met, the design variables with the smallest objective function and meeting the reliability requirements and their corresponding safety factors are selected as the results of the wind turbine load reduction design and safety factor verification.

2. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 1 is characterized in that: In step S3, the wind condition parameter samples {(v i ,c i )},i=1,…,N W , and the probability P(v i ,c i ), v i represents the average wind speed sample within a certain time range, c i Representing the remaining wind parameters under the design load condition includes the following steps: Step S3.1: Determine the probability distribution F of wind parameters within a certain time range according to measured data or design standards V (v), i=1,…,d c , d c The dimension representing the wind condition parameters within a certain time range; Step S3.2: According to the Rosenblatt transformation, all wind parameters within a certain time range are converted into standard normal random variables, that is: Φ(u v )=F V (v) Among them, u v It represents the standard normal random variable of the average wind speed within a certain time range after Rosenblatt transformation. represents the standard normal random variable after Rosenblatt transformation of wind parameters within a certain time range, Φ(·) represents the cumulative distribution function of the standard normal random variable; Step S3.3: Calculate the wind condition parameters within a certain time range, and the corresponding probability within a certain regression period is β 1Y ; Step S3.4: According to the inverse first-order reliability analysis method, u v , Need to meet: Step S3.5: According to the inverse transformation of step S3.2, all wind parameters within a certain time range need to satisfy: Among them, Φ -1 (·) represents the inverse function of the cumulative distribution function of the standard normal random variable; Step S3.6: based on the cut-in and cut-out wind speed ranges of the selected wind turbine type, the average wind speed within a certain time range is sampled at a certain interval speed ΔV, and the remaining wind condition parameter samples are determined through step S3.5; Step S3.7: Determine the probability P(v i ,c i ).

3. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 1 is characterized in that: In step S3, the wind condition parameter samples {(v i ,c i )}, multiple simulations of wind turbine performance models considering turbulent wind were performed to fit {(v i ,c i The conditional probability distribution F of the annual maximum value of the random load L under L (l|v i ,c i ), where F (·) represents the cumulative distribution function, v i represents the average wind speed sample within a certain time range, c i represents the other wind parameters under the design load condition, and l represents the load extreme value of the wind turbine within a certain time range.

4. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 1 is characterized in that: In step S4, the establishment of the load uncertainty factor statistical model includes the following steps: Step S4.1: Based on the statistical model of the remaining uncertainties, collect N X samples; Step S4.2: At the rated wind speed of the current wind turbine, generate N W A time series of turbulent wind within a certain time range; Step S4.3: Select a turbulent wind time series, traverse all other uncertainty source samples, simulate the performance of wind turbines within a certain time range, obtain the load time series, and obtain N X load extreme value samples; Step S4.4: For N X The load extreme value samples are normalized and the normalized load extreme value standard deviation σ is calculated; Step S4.5: Repeat steps S4.3 to S4.4 until all N W Turbulent wind time series, get N W The standard deviation of the load extremes; Step S4.6: Establish the load uncertainty factor X (·) The statistical model has a distribution type specified by the design criteria, a sample mean of 1, and a sample standard deviation of N obtained in step S4.

5. W The standard deviations of the load extremes are averaged.

5. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 1 is characterized in that: In step S5, the deterministic optimization problem of determining a set of safety factors is specifically: minimize Cost(d) Subject to G i (d;L k ,R k ,γ)>0,i=1,...,n d L ≤d≤d U ,d∈R d Among them, Cost(·) represents the objective function, G i (·) represents the design equation, L k ,R k denote the characteristic values ​​of load and structural resistance, γ denotes the safety factor, and d L ,d U They represent the upper and lower bounds of the design variables, n represents the number of design equations, and d represents the dimension of the design variables.

6. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 1 is characterized in that: In step S5, the reliability check is performed by determining whether the following equations are all valid for i=1, ..., n: b i =-Φ -1 (P f,i )≥β t Among them, β i Indicates the reliability level of the calibration, Φ -1 (·) represents the inverse function of the cumulative distribution function of the standard normal random variable, β t Indicates the required reliability level, P f,i represents the failure probability of the i-th design equation, and the calculation formula is: P f,i =P(g i (d;L,R,X)<0) Among them, g i (·) represents the limit state function, L represents the random load, R represents the structural resistance, and X represents the load uncertainty factor.

7. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 1 is characterized in that: In step S5, the failure probability and reliability calculation of each limit state function includes the following steps: Step S5.1: According to the failure probability required by the design standard, under each wind parameter sample within a certain time range, determine the number of samples in each round and the total number of sampling rounds; Step S5.2: According to the statistical distribution of the characterized random loads and structural resistance, collect joint samples of each random load, structural resistance and uncertainty factor to calculate the limit state equation and obtain the number of failure samples; Step S5.3: Calculate the failure probability and the error of the failure probability estimation by the number of failure samples and the number of samples; Step S5.4: If the error is less than the threshold or there is no failed sample or the round ends, the probability of the limit state being less than 0 is taken as the failure probability calculation result. Otherwise, a new round is performed and steps S5.2 and S5.3 are executed; Step S5.5: Repeat steps S5.1 to S5.3 until all wind condition parameter samples are traversed; Step S5.6: Based on the obtained failure probability calculation result and the probability of each sample obtained in step S3, calculate the total failure probability of the limit state function, and calculate the reliability index based on the total failure probability.

8. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 7 is characterized in that: In step S5.1, according to the failure probability P required by the design standard f,t , in each certain time range T, the wind condition parameter sample {(v i ,c i )}, determine the number of samples N in each round b =T / P f,t And the total number of sampling rounds K, v i represents the average wind speed sample within a certain time range, c i Represents the remaining wind parameters under the design load condition; In step S5.2, according to the statistical distribution model of the random load and structural resistance, N b The combined sample of random load, structural resistance and uncertainty factor is calculated to obtain N b The value of the limit state equation is recorded, and the failure, i.e., the limit state g i The number of samples N < 0 f,k ; In step S5.3, the failure probability P is calculated. f,k The error δ with the failure probability estimate k :

9. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 7 is characterized in that: In step S5.4, if δ k The number of samples N that are less than the threshold or limit state f,k = 0 or round k is the total number of rounds K, the reliability analysis calculation is terminated, and the probability P(g<0|v i ,c i )=P f,k As {(v i ,c i )}, otherwise, let k = k + 1 and repeat steps S5.2 and S5.3, v i represents the average wind speed sample within a certain time range, c i Represents the remaining wind parameters under this design load condition.

10. The reliability-based wind turbine load reduction design and safety factor verification method according to claim 7, characterized in that: In step S5.6, the failure probability of the limit state function is: The calculated reliability index is: β=-Φ -1 (P f )。

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