Offshore floating wind turbine mooring system reliability integrated optimization method and system

Abstract

The offshore floating wind turbine mooring system reliability integrated optimization method and system belong to the technical field of mooring system design optimization, in order to solve the problem of fast and reliable optimization of floating wind turbine mooring, the technical points are that the optimization algorithm iteratively searches for the optimal mooring design scheme that meets the constraint condition and has the minimum target function value among all mooring design schemes in the numerical range of optimization variables; wherein, in each iteration, the optimization algorithm obtains the output response of the floating wind turbine system corresponding to the parameterized model of the current mooring design scheme through the surrogate model; the optimization algorithm judges whether the output response of the floating wind turbine system meets the constraint condition, and calculates the target function value according to the output response of the floating wind turbine system; wherein, the input of the surrogate model is the current mooring design scheme, and the output is the output response of the floating wind turbine system of the parameterized model of the current mooring design scheme.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mooring system design optimization, and particularly relates to a reliability optimization method for a mooring system of a floating offshore wind turbine. BACKGROUND

[0002] The mooring system of a floating wind turbine plays a vital role in maintaining its normal operation at sea. In recent years, mooring system failure events have become increasingly frequent, causing serious economic losses and potential damage. The mooring system must meet strict performance requirements and comply with constraints related to platform offset and mooring tension safety factors, which requires significant costs. Typically, the mooring cost accounts for more than 10% of the total cost of a floating wind turbine. Therefore, it is crucial to carry out mooring optimization design work to balance the cost and safety performance of the mooring system.

[0003] Traditional mooring system design involves multiple iterations to explore various combinations of mooring parameters. This method relies on design experience to select potential combinations and verify the dynamic response of the integrity of the floating structure and the mooring system, which results in a large amount of calculation time and cost. Existing mooring design analysis methods usually use static or dynamic methods. Although the static method is fast in solving, it cannot accurately reflect the complex nonlinear performance of the wind turbine mooring. The dynamic method can provide more accurate responses but requires a large amount of computational resources. There is an urgent need to develop a more rapid and accurate mooring analysis method to improve the efficiency of mooring system optimization design work.

[0004] In addition, although some existing research has carried out optimization work on the mooring system of a floating wind turbine, the reliability of the current design method needs to be improved. Existing design standards mainly consider the uncertainty under various mooring system load conditions in a deterministic manner, using methods such as partial safety factors. However, this simplification in the design process can lead to overestimation or underestimation of uncertainty. Therefore, it is necessary to integrate structural reliability analysis methods into the design optimization process. Developing a reliability optimization method for the mooring system of a floating wind turbine is the main goal of the present application. SUMMARY

[0005] To solve the problem of fast and reliable optimization of the mooring system of a floating wind turbine, in a first aspect, according to the offshore floating wind turbine mooring system reliability integrated optimization method in some embodiments of the present application, the optimization algorithm iteratively searches for the optimal mooring design scheme that meets the constraint conditions and has the minimum objective function value among all mooring design schemes in the numerical range of the optimization variables.

[0006] wherein, in each iteration, the optimization algorithm obtains the floating wind turbine system output response of the parametric model corresponding to the current mooring design scheme through the surrogate model; the optimization algorithm judges whether the floating wind turbine system output response meets the constraint condition, and calculates the objective function value according to the floating wind turbine system output response;

[0007] wherein, the input of the surrogate model is the current mooring design scheme, and the output is the floating wind turbine system output response of the parametric model of the current mooring design scheme.

[0008] According to the offshore floating wind turbine mooring system reliability integrated optimization method in some embodiments of the application, the surrogate model is obtained based on the following manner:

[0009] S10. Construct a training database;

[0010] S20. Train the model using the training database to obtain the surrogate model;

[0011] wherein, the step S10 of constructing the training database comprises:

[0012] S110. Expand the mooring design scheme according to the mooring cable optimization variable and its numerical range, and establish a parametric model of the expanded mooring design scheme;

[0013] S120. Perform static mooring response analysis on the parametric model to obtain the first constraint parameter of the parametric model;

[0014] S130. The expanded mooring design scheme whose first constraint parameter meets the first constraint condition is a first mooring design feasible scheme;

[0015] S140. Perform dynamic mooring response analysis on the parametric model of the first mooring design feasible scheme to obtain a second constraint parameter;

[0016] S150. The first mooring design feasible scheme whose second constraint parameter meets the second constraint condition is a second mooring design feasible scheme;

[0017] S160. Construct an input-output database between the second mooring design feasible scheme and the constraint parameter of the second mooring design feasible scheme to obtain the training database;

[0018] wherein, the constraint parameter comprises the first constraint parameter and the second constraint parameter, the data representing the input in the database is the second mooring design feasible scheme, the data representing the output is the constraint parameter, and the constraint parameter is the floating wind turbine system output response.

[0019] According to the reliability integration optimization method for offshore floating wind turbine mooring systems in some embodiments of this application, the first constraint parameter includes the pretension F of the mooring system. pre The floating wind turbine moves along the X-axis with a period P sur The vertical position Z, measured from the anchor point, is the first 1 / 10 of the mooring cable length. chain and the vertical coordinate Z of the bottom section of the wire rope rope ;

[0020] The second constraint parameter includes the floating wind turbine platform offset X. FOWT The vertical position Z, measured from the anchor point, is the first 1 / 10 of the mooring cable length. chain The vertical coordinate Z of the bottom section of the wire rope rope and the maximum mooring tension S;

[0021] In step S130, the first constraint condition includes g2, g5, g3, and g4, wherein determining whether the first constraint parameter satisfies the first constraint condition includes:

[0022] S121. The mooring design scheme that satisfies the first constraint parameters g2 and g5 is a preliminary feasible first mooring design scheme.

[0023] S122. Applying the maximum platform offset limit X lim The preliminary first mooring design feasible scheme that satisfies the first constraints g3 and g4 is the first mooring design feasible scheme.

[0024] In step S140, the second constraint conditions include g1, g3, g4, and g6.

[0025] The constraints include:

[0026] g1: Floating wind turbine platform offset X FOWT Not exceeding the maximum offset limit X lim ;

[0027] g2: Pretension F of the mooring system pre Keep it within a certain range;

[0028] g3: The length of the mooring cable contact point must be at least one-tenth of the total length of the mooring cable;

[0029] g4: Vertical coordinate Z of the bottom section of the mooring cable's wire rope. rope Always located above the seabed;

[0030] g5: The period of motion of the floating fan along the X-axis is kept within a certain range;

[0031] g6: the maximum mooring tension under the limit state is always lower than the breaking strength.

[0032] According to the offshore floating wind turbine mooring system reliability integrated optimization method in some embodiments of the application, the mooring cable optimization variables include a fairlead position, a mooring section diameter, a mooring section length, and a mooring radius; and the mooring section includes a first section starting anchor chain section, a second section steel wire rope section, and a third section ending anchor chain section.

[0033] The target function is expressed as the lowest material cost of the mooring line section that meets the constraint condition.

[0034] The target function is expressed as the lowest material cost of the mooring line section that meets the constraint condition.

[0035]

[0036] In the formula, f(X) represents the total cost of the mooring section, M represents the number of mooring cables, N represents the number of sections on a single mooring cable, D i and L i respectively represent the diameter and length of the section, a i and b i are the material price coefficients of the mooring cable.

[0037] According to the offshore floating wind turbine mooring system reliability integrated optimization method in some embodiments of the application, the constraint conditions include:

[0038] g1=X FOWT -X lim <0 (1)

[0039] In the formula, X FOWT represents the floating wind turbine platform offset, X lim represents the maximum offset limit.

[0040]

[0041] In the formula, F pre represents the pre-tension of the mooring system, F min represents the minimum pre-tension, and F max represents the maximum pre-tension.

[0042] g3=Z chain +H<0 (3)

[0043] In the formula, Z chain represents the vertical position of the first 1 / 10 mooring cable length measured from the anchor point, and H represents the water depth.

[0044] g4=-(Z rope +H)<0 (4)

[0045] wherein Z rope represents the vertical coordinate of the bottom section of the wire rope section;

[0046]

[0047] wherein P sur represents the period of the floating wind turbine along the X axis movement, P min represents the minimum period of the floating wind turbine along the X axis movement, P max represents the maximum period of the floating wind turbine along the X axis movement;

[0048] g6= β- β lim <0 (6)

[0049] wherein β represents the reliability index of the mooring strength, β lim represents the reliability standard index of the mooring strength; wherein,

[0050] β= -Φ -1 (P f ) = -Φ -1 (Pr{S>R}) (7)

[0051] wherein P f represents the failure probability, Φ -1 represents the inverse of the standard normal distribution, Pr{} represents the probability event, S represents the maximum mooring tension, and R represents the breaking strength.

[0052] According to the offshore floating wind turbine mooring system reliability integrated optimization method in some embodiments of the present application, the constraint condition is as follows:

[0053] g1= X FOWT -X lim <0 (1)

[0054] wherein X FOWT represents the floating wind turbine platform offset, X lim represents the maximum offset limit;

[0055]

[0056] wherein F pre represents the pre-tension of the mooring system, F min represents the minimum pre-tension, F max represents the maximum pre-tension;

[0057] g3= Z chain +H<0 (3)

[0058] wherein Z chain represents the vertical position of the first 1 / 10 mooring cable length measured from the anchor point, and H represents the water depth;

[0059] g4 = -(Z rope + H) < 0 (4)

[0060] In the formula, Z rope represents the vertical coordinate of the bottom section of the wire rope section;

[0061]

[0062] In the formula, P sur represents the period of movement of the floating wind turbine along the X axis, P min represents the minimum period of movement of the floating wind turbine along the X axis, P max represents the maximum period of movement of the floating wind turbine along the X axis;

[0063] g6 = S f s -R < 0 (8)

[0064] In the formula, S represents the maximum mooring tension, R represents the breaking strength, and f s represents the safety factor.

[0065] According to the offshore floating wind turbine mooring system reliability integrated optimization method in some embodiments of the present application, the maximum offset limit X lim = 25 m;

[0066] The minimum pretension F min = 1500 kN, and the maximum pretension F max = 2500 kN;

[0067] The water depth H = 200 m;

[0068] The minimum period of movement of the floating wind turbine along the X axis P min = 60 s, and the maximum period of movement of the floating wind turbine along the X axis P max = 150 s;

[0069] The reliability standard index of the mooring strength is β lim = 3.291.

[0070] According to the offshore floating wind turbine mooring system reliability integrated optimization method in some embodiments of the present application, the proxy model is a Kriging model.

[0071] In a second aspect, the embodiments of the present application also provide an electronic device, including one or more processors, a memory, and one or more programs; wherein the one or more programs are stored in the memory, and the one or more programs include instructions, when the instructions are executed by the electronic device, causing the electronic device to perform the first aspect and any possible technical solutions of the first aspect.

[0072] In a third aspect, the embodiments of the present application also provide a computer-readable storage medium, which includes a computer program, when the computer program is executed on an electronic device, causes the electronic device to execute the first aspect and any possible technical solutions of the first aspect.

[0073] Advantages:

[0074] In the first aspect, the present application provides a reliability integrated optimization method for a floating wind turbine mooring system. Through the trained surrogate model, the floating wind turbine system output responses of all mooring design schemes in the numerical range of the optimization variables are quickly outputted to search for the optimal solution of the floating wind turbine system output responses that meet the constraint conditions and the objective function in the iteration, and the mooring design scheme corresponding to the response is obtained. Compared with the existing optimization algorithm that uses dynamic response analysis method to calculate the floating wind turbine system output of the mooring design scheme each time, the present application has the defect that the response takes a very long time and is not suitable for the calculation scenario of more iterations. The present application saves the simulation calculation time of the dynamic response analysis, can realize the response output of the complex system quickly, and can realize more rounds of iteration calculation in a very short time, so as to quickly and accurately obtain the optimal design scheme. Therefore, the present application integrates the agent modeling technology, considers the uncertainty factors as one of the optimization constraint conditions, and carries out the reliability optimization work in the design optimization process, so as to optimize the calculation speed and greatly reduce the time consumption. This integrated optimization method can quickly identify the optimal mooring design scheme in a wide design space, save the calculation time of expensive numerical simulation, and has high reliability after strict verification. The integrated optimization method provides an efficient and stable method for the economic and reliable optimization of the floating wind turbine mooring system.

[0075] In the second aspect, when constructing the database of the trained agent model, based on the constraint parameters suitable for fast static response analysis in the present application, the present application adopts a mixed mooring analysis method combining static and dynamic analysis to simulate and calculate the output response of the floating wind turbine system of the mooring system. The static analysis is used to quickly exclude obviously unfeasible mooring design schemes, greatly reducing the sample size range of dynamic response analysis and greatly saving the response analysis calculation time. Based on the speed improvement of the above response analysis, the present application can expand the sample size of more mooring design schemes in a shorter time and perform fast response analysis on them. Therefore, this method realizes the possibility of expanding a large number of samples for model training database construction. However, due to the extremely time-consuming dynamic response analysis in the prior art, it is difficult to use a large number of expanded samples for model training database construction. Therefore, the above-mentioned mixed mooring analysis method combining static and dynamic analysis to simulate and calculate the output response of the mooring system of the floating wind turbine system is used to increase the original sample size and complete the response analysis in a short time, which can provide a basic sample size for the training database in a fast, accurate and comprehensive manner, so as to train the model using the database and improve the model precision.

[0076] In the third aspect, the trained agent model has improved precision, can be truly applied to the model replacement response analysis method in the optimization algorithm iteration, and truly realizes accurate and fast optimization of the implementation scheme and greatly reduces the optimization calculation cost.

[0077] Additional aspects and advantages of the present application will be partially given in the following description, partially will become obvious from the following description, or will be understood by the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1 The present application provides a reliability integrated optimization method flowchart of a floating offshore wind turbine mooring system.

[0079] Figure 2 The overall schematic diagram of the example design of the present application.

[0080] Figure 3 The optimization object and optimization variable schematic diagram of the example design of the present application, (a) side view, (b) top view.

[0081] Figure 4 The motion process schematic diagram of the example design of the present application.

[0082] Figure 5 The floating body operation and mooring system maximum deviation and maximum force diagram of the example design of the present application, (a) maximum deviation, (b) maximum force.

[0083] Figure 6 The agent model fitting diagram of the example design of the present application.

[0084] Figure 7 Cost iteration comparison chart designed by the present application example, (a) deterministic optimization, (b) reliability optimization.

[0085] Figure 8 Constraint iteration schematic diagram designed by the present application example, (a1) g1 constraint of deterministic optimization, (b1) g1 constraint of reliability optimization, (a2) g2 constraint of deterministic optimization, (b2) g2 constraint of reliability optimization, (a3) g6 constraint of deterministic optimization, (b3) g7 constraint of reliability optimization.

[0086] Figure 9 Variable iteration schematic diagram designed by the present application example, (a1) fairlead position 31 of deterministic optimization, (b1) fairlead position 31 of reliability optimization, (a2) mooring section 1 diameter 32 of deterministic optimization, (b2) mooring section 1 diameter 32 of reliability optimization, (a3) mooring section 1 length 33 of deterministic optimization, (b3) mooring section 1 length 33 of reliability optimization, (a4) mooring section 2 diameter 34 of deterministic optimization, (b4) mooring section 2 diameter 34 of reliability optimization, (a5) mooring section 2 length 35 of deterministic optimization, (b5) mooring section 2 length 35 of reliability optimization, (a6) mooring section 3 diameter 36 of deterministic optimization, (b6) mooring section 3 diameter 36 of reliability optimization, (a7) mooring section 3 length 37 of deterministic optimization, (b7) mooring section 3 length 37 of reliability optimization, (a8) mooring radius 38 of deterministic optimization, (b8) mooring radius 38 of reliability optimization. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described below in combination with the drawings and examples. The specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0088] In one embodiment, the offshore floating wind turbine mooring system reliability integrated optimization method is applied to a large floating wind turbine IEA 15MW wind turbine (see Figure 2 ), which operates in a 200-meter water depth environment, and the prototype design mooring system is composed of three pure anchor chains.

[0089] As Figure 1 shown, in the design of a floating wind turbine mooring system, an offshore floating wind turbine mooring reliability integrated optimization method includes

[0090] Step S1, define the optimization problem. Determine the optimization object, select the optimization variables, constraint conditions and target equation.

[0091] Step S2, parameterized modeling of mooring system. According to the selected optimization variables, a reasonable design space is determined for each optimization variable, Latin hypercube sampling method is carried out in the design space, and then a certain number of mooring design schemes are obtained by random combination. Then, through software interface programming of mooring analysis software (the commonly used marine mooring analysis software MIMOSA and SIMA are used in the example of the present application), parameterized modeling of the mooring system in the mooring analysis software is realized, and the model of the above mooring design scheme is quickly established.

[0092] Step S3, static-dynamic mixed mooring analysis method. The static-dynamic mixed method is used to analyze the response of the mooring system. First, the static mooring analysis method (MIMOSA software implementation) is used to pre-screen the samples according to the constraint conditions defined in step S1, and then the dynamic method (SIMA software implementation) is used to obtain the accurate response of the nonlinear system, avoiding time domain simulation for all mooring design schemes, thereby saving a large amount of calculation cost and time.

[0093] Step S4, establishment of surrogate model. The mooring design schemes obtained after pre-screening in step S3 and the corresponding wind turbine system dynamic response are taken as input (mooring design scheme)-output (wind turbine system dynamic response) data to establish a database. A surrogate model is constructed to capture the relationship between the input and the output, replacing the time domain simulation in the subsequent optimization iteration process, and further saving the optimization cost and calculation time.

[0094] Step S5, mooring optimization based on reliability. The surrogate model is combined with the optimization algorithm, the well-trained surrogate model is used, and the gradient-based algorithm is used to explore the design space and quickly identify the optimal mooring solution.

[0095] The present application provides a reliability integrated optimization method for floating wind turbine mooring system. The method uses a mixed mooring analysis method combining static and dynamic analysis to simulate and calculate the mooring system, so as to quickly and accurately obtain the dynamic response thereof. In addition, the proxy modeling technology is integrated, and the uncertainty factor is considered as one of the optimization constraint conditions in the design optimization process to carry out reliability optimization work. This integrated optimization method can quickly identify the optimal mooring design scheme in a wide design space, saving the calculation time of expensive numerical simulation. The integrated optimization method proposed in the present application has been strictly verified, and provides an efficient and robust method for economic and reliable optimization of floating wind turbine mooring system.

[0096] In some embodiments, the optimization problem is defined with reference to step S1, the optimization object is determined, the optimization variables, constraint conditions and target equation are selected, and the following steps are taken:

[0097] The selection of mooring design parameters is critical. The embodiment preferably configures the most widely used mooring design scheme for current offshore platforms: multi-section mooring, composed of anchor chain-steel wire rope-anchor chain. The mooring system is composed of three symmetrical mooring lines, as shown in Figure 3 Figure 1, including the fairlead position 31, the diameter of mooring section 1 32, the length of mooring section 1 33, the diameter of mooring section 2 34, the length of mooring section 2 35, the diameter of mooring section 3 36, the length of mooring section 3 37, and the mooring radius 38. By these 8 optimization variables, a mooring line can be defined. The embodiment intends to optimize these 8 optimization variables, with the purpose of obtaining the lowest cost composition scheme that meets the constraint conditions.

[0098] For the reliability optimization problem, the uncertainty of the optimization variables also needs to be considered, and the inherent uncertainty in the material and construction process needs to be taken into account in the design optimization, that is, random variables need to be introduced. In the embodiment, the fairlead position 31 is considered as a random variable to consider the inherent uncertainty in the construction process. In addition, the catenary mooring system is usually equipped with a drag anchor. Considering the construction error in the installation process and the instability factors brought by long-term environmental and geological changes, the mooring radius 38 is also considered as a random variable.

[0099] Therefore, the optimization variables and random variables in the embodiment are summarized in Table 1. The lower and upper limits are selected according to the physical configuration of the mooring system and the material properties of the mooring line material. The performance of the chain and steel wire rope is characterized by its nominal outer diameter, mass density, elastic modulus (EA), and breaking strength. For a catenary mooring system, given the fairlead height, the total length of the mooring line, and the mooring radius, the catenary equation can be used to determine the chain configuration and pretension.

[0100] Table 1. Mooring line optimization variables and ranges

[0101]

[0102] The mooring system is an important part of the floating wind turbine, and its design needs to ensure the safe and stable operation of the floating wind turbine system. A series of constraint conditions need to be considered in the optimization design process. In the embodiment, the constraint conditions are met to ensure the safe operation of the floating wind turbine system during the entire operating life cycle under the 50-year extreme sea conditions, including:

[0103] (1) Floating wind turbine platform offset X FOWT cannot exceed the maximum offset limit X lim .

[0104] g1 = X FOWT -X lim < 0 (1)

[0105] In the embodiment, X lim25m, i.e. the maximum displacement of the floating wind turbine platform under the action of wind and wave cannot exceed 25m under the restriction of the mooring system.

[0106] (2) The pre-tension F of the mooring system pre is kept within a reasonable range. Excessive pre-tension can cause the mooring line to bear stress exceeding its strength limit, while insufficient pre-tension can cause excessive displacement of the floating wind turbine platform under environmental load, affecting the stability of the wind turbine system.

[0107]

[0108] In the embodiment, the minimum pre-tension F min is limited to 1500kN, and the maximum pre-tension F max is limited to 2500kN.

[0109] (3) The mooring line maintains sufficient laying length on the seabed under maximum displacement. The floating wind turbine platform will move the mooring line during movement under the action of wind and wave, thereby lifting the bottom-lying mooring line from the seabed, as shown in Figure 4 Due to low cost and easy installation, the catenary mooring design is usually equipped with a drag anchor. Excessive oblique tension can cause the drag anchor to fail. Therefore, it is necessary to ensure that a certain bottom-lying mooring line is retained under the maximum displacement of the floating wind turbine platform to avoid oblique tension on the drag anchor. In the embodiment, the length of the grounding section under the maximum displacement limitation must be at least one-tenth of the total mooring line length.

[0110] g3 = Z chain + H < 0 (3)

[0111] wherein Z chain represents the vertical position of the first 1 / 10 mooring line length measured from the anchor point, and H represents the water depth (i.e. 200 meters). The Z axis is in the positive direction upward.

[0112] (4) During the operation of the floating wind turbine system, the steel wire rope section (mooring section 2) should not contact the seabed. The steel wire rope in contact with the seabed is prone to biological contamination. In addition, the wear resistance of the steel wire rope material is not high, and contact with the seabed should be avoided to minimize wear and prolong the service life of the steel wire rope. The constraint can be expressed as the vertical coordinate Z rope of the bottom section of the steel wire rope always being above the seabed.

[0113] g4 = -(Z rope + H) < 0 (4)

[0114] (5) The movement period P sur of the floating wind turbine along the X axis is kept within a reasonable range. When different design schemes are adopted for the catenary mooring system, the P surChanges will occur. Generally, it is necessary to ensure that P sur Avoid the common wave period (5-30s) in the sea area of our country to prevent excessive vibration caused by wave excitation, accelerate the fatigue damage of the structure, and reduce the stability of the power system. From the perspective of design experience, a value greater than 60 seconds is sufficient to avoid common wave periods. In addition, excessive P sur means a too "loose" mooring system, which can lead to the maximum offset limit constraint g1 of the floating wind turbine not meeting the requirements. Therefore, in this study, the minimum P min is limited to 60s, and the maximum P max is limited to 150s.

[0115]

[0116] (6) The mooring cable strength is safe during operation, and no fracture occurs. The maximum mooring tension S under the limit state should always be lower than the fracture strength R. In the reliability optimization proposed in this invention, the safety of the mooring strength is considered from the perspective of reliability. The reliability index β of the mooring strength needs to meet the standard requirements. In the embodiment, the nominal annual failure probability is 5E-4 according to the relevant design specification, and the corresponding β lim is 3.291. As pointed out in the background of the current floating wind turbine mooring system optimization method, the deterministic safety factor design method is basically adopted to consider the safety of the mooring cable strength. In order to compare the difference between the reliability index as the mooring strength constraint and the safety factor as the constraint, this invention also carries out deterministic optimization. Therefore, the constraint in the deterministic optimization is g7, and according to the relevant design specification, the safety factor f s of each mooring cable under complete conditions should be greater than 1.67.

[0117] g6 = β - β lim < 0 (6)

[0118] β = -Φ -1 (P f ) = -Φ -1 (Pr{S > R}) (7)

[0119] g7 = S·f s - R < 0 (8)

[0120] In the formula, P f represents the failure probability, Φ -1 represents the inverse of the standard normal distribution, Pr{} represents the probability event, S represents the maximum mooring tension, and R represents the fracture strength.

[0121] It can be understood that the constraint conditions used in the reliability integrated optimization method proposed in this invention are g1-g6, while the constraints used in the comparative work deterministic optimization are represented as g1-g5 and g7.

[0122] In the premise of meeting all the constraints of the mooring system, the cost of the mooring system is selected as the optimization objective in the optimization process. Generally, the cost of the mooring system includes the material cost of the mooring cable, anchor foundation, connecting components, construction cost, etc. For simplicity, only the material cost of the mooring line segment is considered in the embodiment. Therefore, the objective function is expressed as

[0123]

[0124] where f(X) represents the total cost of the mooring segment, M represents the number of mooring cables, N represents the number of segments on a single mooring cable, D and L represent the diameter and length of the segment, respectively, a i and b i are the material price coefficients provided by the mooring cable manufacturer.

[0125] In some embodiments, with reference to step S2, the mooring system is parameterized modeling, including generating 20000 random samples representing various mooring design schemes according to the design range of 8 optimization variables by using the Latin hypercube sampling method, and realizing the automatic and batch modeling of the 20000 random samples by using the programming method to realize the automatic and batch modeling of the 20000 random samples.

[0126] In some embodiments, with reference to step S3, the static-dynamic hybrid method is carried out to analyze the mooring response, including that the MIMOSA software is first used to carry out the static mooring analysis to preliminarily screen out the unfeasible mooring design scheme, so as to reduce the cost of subsequent dynamic simulation and narrow the design space of optimization search. The static mooring analysis takes an average of 2s for each mooring design scheme. In the static analysis, F pre is solved by using the catenary equation to judge whether g2 meets the requirements; the hydrodynamic information of the floating wind turbine is introduced to solve the coupled system mass stiffness matrix, so as to determine the P sur of the system, and evaluate whether the constraint g5 meets the requirements. For the mooring design scheme meeting g2 and g5, the maximum platform offset limit X limThe maximum platform offset (25 m in this embodiment) is used to determine whether the mooring cable has a bottom lying section and whether the wire rope contacts the seabed, thereby screening out mooring design schemes that do not meet g3 and g4. However, it should be noted that for the g3 and g4 constraints, only mooring design schemes that do not meet the constraints at the maximum offset are screened out in the static analysis. The actual offset of the floating wind turbine platform and whether g3 and g4 are met at the corresponding offset need to be further calculated and checked in the dynamic analysis. In summary, the constraint conditions used for preliminary sample screening in the static analysis are g2-g5, and the constraint conditions used in the static analysis do not need to be passed through dynamic calculation, and the complex sea state nonlinearity has no serious impact on the results. Through the above method, 1028 feasible mooring design schemes are preliminarily screened out, greatly reducing the cost of subsequent dynamic simulation.

[0127] To accurately solve the response of the nonlinear mooring system under the nonlinear environmental load, the embodiment uses the SIMA software to perform dynamic simulation on the remaining 1028 feasible mooring design schemes. The environmental conditions (wave height, wave period, wind speed) of the east coast of the United States are selected to evaluate the performance of the floating wind turbine under extreme design conditions. Figure 5 The maximum platform offset of the floating wind turbine and the maximum mooring force under a certain mooring design sample are shown. It can be observed that these values do not reach the constraint limit, indicating that it is possible to optimize the entire mooring system. After the dynamic calculation is completed, the floating wind turbine platform offset X FOWT , the vertical position Z chain of the first 1 / 10 mooring cable length measured from the anchor point, the vertical coordinate Z rope of the bottom section of the wire rope, and the maximum mooring tension S are obtained, thereby screening the remaining constraints, and finally obtaining 1000 mooring design schemes that meet the design constraints. An input-output database is constructed between the 1000 mooring design schemes and these output responses.

[0128] In some embodiments, with reference to step S4, a proxy model is established, including the input (mooring design sample)-output (floating wind turbine system output response) database obtained in step S3, for proxy model training, prediction, and accuracy verification. A mapping relationship between the mooring design scheme and the corresponding floating wind turbine system output response is constructed, thereby replacing the dynamic calculation in the later optimization iteration process, further saving optimization time. The accuracy of the proxy model is consistent with the prior knowledge of the complexity of the research problem, which is difficult to obtain, so different proxy models need to be compared to select the most suitable proxy model for the optimization problem. In the embodiment, Kriging, polynomial response surface (PRS) model, and artificial neural network (ANN) model are selected for comparison, and RAAE, RMSE, and R 2Three statistical indicators are used to test the accuracy of the surrogate model. Taking the tension of mooring section 2 as an example, the comparison results are as follows.

[0129] Table 2 Comparison of maximum tension error of mooring section 2 surrogate model

[0130]

[0131] It can be seen that the Kriging model performs best, and the error indicators RAAE and RMSE are the lowest, and the determination coefficient R 2 is the highest. The Kriging model is good for fitting nonlinear relationships and has fast training speed. Figure 6 The prediction performance of the Kriging surrogate model is demonstrated by taking the offset of the floating wind turbine platform as an example. It can be observed that the prediction of the Kriging model is in good agreement with the simulation value. The Kriging model can be used in subsequent optimization tasks of the floating wind turbine mooring system.

[0132] Referring to step S5, the embodiment uses a sequential quadratic programming (SQP) optimization algorithm to perform nonlinear constraint optimization on the floating wind turbine mooring system. By giving the optimization algorithm an initial point, which is the mooring design scheme composed of the 8 optimization variables in the corresponding range defined in step S1 in this embodiment, the optimization algorithm will continuously search and iterate in the design space, continuously improve the current solution to gradually approach the optimal solution, so as to obtain the optimal solution (optimal mooring system design scheme) that satisfies all constraints and has the lowest target function (i.e. mooring system design cost). In the framework of the method, the Kriging surrogate model trained in step S4 will be combined during the iteration process of the optimization algorithm to replace dynamic simulation during the search for the optimal design scheme, so as to realize fast iteration. At the same time, since the distribution of the training data samples is screened by static-dynamic screening, the process of searching for the optimal solution by the algorithm during the optimization iteration process is not "blind", but actively searches in the direction that meets the constraint conditions, thereby greatly reducing the search space and thus reducing the optimization calculation cost. In reliability optimization, the mooring strength reliability index needs to be calculated for each scheme in the iteration process. The reliability index is simulated by the Monte Carlo simulation method, and each mooring design scheme in the iteration process needs to be calculated 200,000 times to obtain the strength reliability index of the mooring design scheme. The calculation of the reliability index further proves the superiority of the surrogate model, which greatly reduces the calculation burden compared with direct dynamic analysis.

[0133] Figure 7 The figure shows the comparison of the cost iteration of the deterministic optimization (a) and the reliability optimization (b) of the example design of the present application, Figure 8 The figure shows the constraint iteration schematic diagram of the deterministic optimization (a) and the reliability optimization (b) of the example design of the present application, Figure 9The certainty optimization (a) and reliability optimization (b) variable iteration schematic diagram of the example design of the present application is shown. In the certainty optimization (a), the constraint conditions used in the certainty optimization in step S1 of the present application are represented as g1-g5 and g7. In the reliability optimization (b), the constraint conditions used in the reliability integrated optimization method in step S1 of the present application are g1-g6. The accuracy of the present method is verified by taking the certainty optimization result as an example, as shown in Table 3. Meanwhile, Table 4 provides the optimization results and related constraints of the certainty optimization and the reliability optimization.

[0134] Table 3. Certainty optimization result and constraint verification

[0135]

[0136] Table 4. Comparison of certainty optimization and reliability optimization results

[0137]

[0138] It can be seen that in the two optimization results, the optimization algorithm greatly reduces the cost of the initial mooring system design scheme, and the reduction can reach 68.3%. The reliability optimization (b) realizes a lower cost while meeting the reliability constraints, and the reduction can reach 68.5%. According to the comparison of (a1) and (a2) expressed in Table 4 and Figure 8 It can be seen that in the two optimization results, the optimization algorithm greatly reduces the cost of the initial mooring system design scheme, and the reduction can reach 68.3%. The reliability optimization (b) realizes a lower cost while meeting the reliability constraints, and the reduction can reach 68.5%. According to the comparison of (a1) and (a2) expressed in Table 4 and

[0139] The offshore floating wind turbine mooring system reliability integrated optimization method provided by the present application can quickly predict the system response of different mooring solutions under survival conditions, and provides a high-performance and low-cost reliable design solution for the floating wind turbine mooring system.

[0140] The above description of the specific embodiments is intended to describe and illustrate the technical solutions of the present application, and the specific embodiments described above are merely illustrative and not restrictive. Without departing from the purpose of the present application and the scope protected by the claims, those skilled in the art can make more forms of specific changes under the inspiration of the present application, and these all belong to the protection scope of the present application.

[0141] Based on the above embodiments, the embodiments of the present application further provide a computer program, which, when executed on a computer, causes the computer to perform the method provided by the above embodiments.

[0142] Based on the above embodiments, the embodiments of the present application further provide a computer storage medium, which stores a computer program, and the computer program, when executed by a computer, causes the computer to perform the method provided by the above embodiments.

[0143] The storage medium can be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

[0144] Based on the above embodiments, the embodiments of the present application further provide a chip for reading a computer program stored in a memory, and implementing the method provided by the above embodiments.

[0145] Based on the above embodiments, the embodiments of the present application provide a computer program product, which, when executed on an electronic device, implements the method provided by the above embodiments.

[0146] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0147] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0148] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0150] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for reliability integrated optimization of offshore floating wind turbine mooring system, characterized in that, The optimization algorithm iteratively searches for an optimal mooring design scheme that meets the constraint condition and has the minimum target function value among all mooring design schemes in the numerical range of the mooring cable optimization variable; In each iteration, the optimization algorithm obtains the output response of the parameterized model of the current mooring design scheme through the surrogate model; the optimization algorithm judges whether the output response of the floating wind turbine system meets the constraint condition, and calculates the target function value according to the output response of the floating wind turbine system; The input of the surrogate model is the current mooring design scheme, and the output is the output response of the parameterized model of the floating wind turbine system; the surrogate model is obtained based on the following methods: S10. Constructing a training database; S20. Training the model using the training database to obtain the surrogate model; In step S10, the training database is constructed, including: S110. Expanding the mooring design scheme according to the mooring cable optimization variable and its numerical range, and establishing a parameterized model of the expanded mooring design scheme; S120. Performing static mooring response analysis on the parameterized model to obtain the first constraint parameter of the parameterized model; S130. The expanded mooring design scheme whose first constraint parameter meets the first constraint condition is a first mooring design feasible scheme; S140. Performing dynamic mooring response analysis on the parameterized model of the first mooring design feasible scheme to obtain the second constraint parameter; S150. The first mooring design feasible scheme whose second constraint parameter meets the second constraint condition is a second mooring design feasible scheme; S160. Constructing an input-output database between the second mooring design feasible scheme and the constraint parameter of the second mooring design feasible scheme to obtain the training database; The constraint parameters include the first constraint parameter and the second constraint parameter, the data representing the input in the database is the second mooring design feasible scheme, the data representing the output is the constraint parameter, and the constraint parameter is the output response of the floating wind turbine system.

2. The offshore floating wind turbine mooring system reliability integrated optimization method of claim 1, wherein, The mooring design scheme includes the mooring cable optimization variable, wherein the mooring cable optimization variable includes a fairlead position, a mooring segment diameter, a mooring segment length, and a mooring radius; the mooring segment includes a first segment starting anchor segment, a second segment steel wire rope segment, and a third segment ending anchor segment; The first constraint parameter includes a pre-tension F of the mooring system pre The vertical position Z of the first 1 / 10 mooring line length measured from the anchor point sur The vertical position Z of the first 1 / 10 mooring line length measured from the anchor point chain The vertical position Z of the first 1 / 10 mooring line length measured from the anchor point rope The vertical position Z of the first 1 / 10 mooring line length measured from the anchor point wherein the second constraint parameter comprises a floating wind turbine platform offset X FOWT a vertical position Z of a first 1 / 10 mooring line length measured from the anchor point chain a vertical coordinate Z of the bottom section of the wire rope rope and a maximum mooring tension S.

3. The offshore floating wind turbine mooring system reliability integrated optimization method of claim 2, wherein, The constraint condition includes: The step S130 includes , , and wherein determining that the first constraint parameter satisfies the first constraint condition comprises: S121. The first constraint parameter satisfies the first constraint condition. , The mooring design scheme is a preliminary feasible scheme for the first mooring design; S122. In applying the maximum platform offset limit X lim The first parameter satisfies the first constraint condition and The preliminary first mooring design feasible solution is the first mooring design feasible solution; In the step S140, the second constraint condition includes , , and ; 4. The offshore floating wind turbine mooring system reliability integrated optimization method according to claim 3, wherein : floating wind turbine platform offset X FOWT not exceeding maximum offset limit X lim ; : pre-tension of the mooring system F pre is kept within a certain range; : The length of the mooring line ground contact section must be at least one tenth of the total mooring line length; : bottom segment vertical coordinate Z of the steel wire rope segment of the mooring line rope always above the seabed; : the floating wind turbine keeps the period of movement along the X axis within a certain range; : The maximum mooring tension in ultimate limit state is always below the breaking strength. The target function represents the lowest material cost of the mooring line segment that meets the constraint condition; wherein, The target function is as follows: The constraint condition includes: (9) where f(X) represents the total cost of the mooring section, M represents the number of mooring lines, N represents the number of sections on a single mooring line, and D i and L i represent the diameter and length of the section, respectively, and a i and b i are the material price coefficients of the mooring line.

5. The offshore floating wind turbine mooring system reliability integrated optimization method of claim 4, wherein, The constraint condition is as follows: The constraint condition is as follows: (1) where X FOWT represents the offset of the floating wind turbine platform, X lim represents the maximum offset limit; (2) where F pre represents the pre-tension of the mooring system, F min represents the minimum pre-tension, F max represents the maximum pre-tension; (3) wherein Z chain represents the vertical position of the first 1 / 10 of the length of the mooring line measured from the anchor point, H represents the water depth; (4) wherein Z rope represents the vertical coordinate of the bottom segment of the wire rope segment (5) wherein P sur represents the period of the floating wind turbine's movement along the X-axis, P min represents the minimum period of the floating wind turbine's movement along the X-axis, P max represents the maximum period of the floating wind turbine's movement along the X-axis; (6) In the formula, β represents a reliability index of the mooring strength, β lim represents a reliability standard index of the mooring strength; wherein, (7) wherein denotes the probability of failure, denotes the inverse of the standard normal distribution, denotes the probability event, S denotes the maximum mooring tension, and R denotes the breaking strength.

6. The offshore floating wind turbine mooring system reliability integrated optimization method of claim 4, wherein, The water depth H is 200 m; The surrogate model is a Kriging model. (1) where X FOWT represents the offset of the floating wind turbine platform, X lim represents the maximum offset limit; (2) where F pre represents the pre-tension of the mooring system, F min represents the minimum pre-tension, F max represents the maximum pre-tension; (3) wherein Z chain represents the vertical position of the first 1 / 10 of the length of the mooring line measured from the anchor point, H represents the water depth; (4) wherein Z rope represents the vertical coordinate of the bottom segment of the wire rope segment (5) wherein P sur represents the period of the floating wind turbine's movement along the X-axis, P min represents the minimum period of the floating wind turbine's movement along the X-axis, P max represents the maximum period of the floating wind turbine's movement along the X-axis; (8) where S represents the maximum mooring tension, R represents the breaking strength, and f s represents the safety factor.

7. The offshore floating wind turbine mooring system reliability integrated optimization method according to claim 5 or 6, characterized in that, One or more processors, memories, and one or more programs; wherein the one or more programs are stored in the memories, and the one or more programs include instructions that, when executed by the system, cause the system to perform the method of any one of claims 1-8. Maximum excursion limit X lim= 25 m; Minimum pretension force F min= 1500 kN, maximum pretension force F max= 2500 kN; ​ Minimum period P of floating fan movement along the X-axis min Maximum period P of floating fan movement along the X-axis max = 150 s Reliability-based design of mooring strength lim = 3.

291.

8. The offshore floating wind turbine mooring system reliability integrated optimization method of claim 1, wherein, ​ 9. An offshore floating wind turbine mooring system reliability integrated optimization system, the system comprising: ​ 10. A computer readable storage medium comprising a computer program which, when run on an electronic device, causes the electronic device to perform any of the methods of claims 1-8.

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