Reliability Integration Optimization Method and System for Offshore Floating Wind Turbine Mooring System

Abstract

Reliability integration optimization method and system for a mooring system of an offshore floating wind turbine, belonging to the technical field of mooring system design optimization. To solve the problems of fast and reliable mooring optimization of floating wind turbines, the technical key points are that the optimization algorithm iteratively searches for the optimal mooring design scheme that satisfies the constraint conditions and has the minimum objective function value among all mooring design schemes within the numerical range of the optimization variables; wherein, in each iteration, the optimization algorithm obtains the output response of the floating wind turbine system of the parameterized model corresponding to the current mooring design scheme through a surrogate model; the optimization algorithm determines whether the output response of the floating wind turbine system satisfies the constraint conditions, and calculates the objective 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 invention belongs to the technical field of mooring system design optimization, and in particular relates to a reliability optimization method for an offshore floating wind turbine mooring system. Background Art

[0002] The mooring system of floating wind turbines plays a vital role in maintaining their normal operation at sea. In recent years, mooring system failure events have become more and more 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 huge costs. Typically, mooring costs account for more than 10% of the total cost of floating wind turbines. Therefore, it becomes crucial to conduct floating wind turbine 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 multiple combinations of mooring parameters. This approach relies on design experience to select potential combinations and verify the dynamic response of the floating structure and mooring system integrity, which incurs a lot of computing time and cost. Existing mooring design analysis methods usually use static or dynamic methods. Although the static method has a fast solution speed, it cannot accurately reflect the complex nonlinear performance of wind turbine mooring. The dynamic method can provide a more accurate response but requires a lot of computing resources. It is urgent to propose a faster and more accurate mooring analysis method to improve the efficiency of mooring system optimization design.

[0004] In addition, although some existing studies have carried out optimization work on floating wind turbine mooring systems, the reliability of current design methods needs to be improved. Existing design standards mainly consider the uncertainties under various mooring system load conditions in a deterministic way, using methods such as partial safety factors. However, such simplifications in the design process may lead to overestimation or underestimation of uncertainties. Therefore, it is necessary to integrate structural reliability analysis methods into the design optimization process. Developing a reliability optimization method for floating wind turbine mooring systems is the main goal of the present invention. Summary of the invention

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

[0006] Wherein, in each iteration, the optimization algorithm obtains the output response of the floating wind turbine system of the parameterized model corresponding to the current mooring design scheme through the proxy model; the optimization algorithm determines whether the output response of the floating wind turbine system satisfies the constraint condition, and calculates the objective function value according to the output response of the floating wind turbine system;

[0007] The input of the proxy model is the current mooring design solution, and the output is the floating wind turbine system output response of the parameterized model of the current mooring design solution.

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

[0009] S10. Build a training database;

[0010] S20. Using the training database to train the model to obtain the proxy model;

[0011] Wherein, building a training database in step S10 includes:

[0012] S110. Expanding the mooring design scheme according to the mooring cable optimization variables and their numerical ranges, and establishing a parameterized model of the expanded mooring design scheme;

[0013] S120. Performing a static mooring response analysis on the parameterized model to obtain a first constraint parameter of the parameterized model;

[0014] S130. The extended mooring design scheme in which the first constraint parameter satisfies the first constraint condition is a first mooring design feasible scheme;

[0015] S140. Performing a dynamic mooring response analysis on the parameterized model of the first mooring design feasible solution to obtain a second constraint parameter;

[0016] S150. The first mooring design feasible solution whose second constraint parameter satisfies the second constraint condition is the second mooring design feasible solution;

[0017] S160. Constructing an input-output database between the second mooring design feasible solution and the constraint parameters of the second mooring design feasible solution to obtain the training database;

[0018] The constraint parameters include a first constraint parameter and a second constraint parameter, the data representing the input in the database is the second mooring design feasible solution, the data representing the output is the constraint parameter, and the constraint parameter is the output response of the floating wind turbine system.

[0019] According to the reliability integrated optimization method of the offshore floating wind turbine mooring system in some embodiments of the present application, the first constraint parameter includes the pre-tension F of the mooring system. pre , the floating fan moves along the X-axis period P sur , the vertical position Z of the first 1 / 10 of the mooring line length measured from the anchor point chain , and the vertical coordinate Z of the bottom section of the wire rope segment rope ;

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

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

[0022] S121. The mooring design scheme whose first constraint parameters satisfy the first constraint conditions g2 and g5 is a preliminary first mooring design feasible scheme;

[0023] S122. Apply the maximum platform offset limit X lim The first mooring design feasible solution whose first parameter satisfies the first constraint conditions g3 and g4 is the first mooring design feasible solution;

[0024] Wherein, in step S140, the second constraint condition includes g1, g3, g4 and g6;

[0025] The constraints include:

[0026] g1: floating wind turbine platform offset X FOWT Does not exceed the maximum offset limit X lim ;

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

[0028] g3: The length of the mooring line touching the ground must be at least one tenth of the total mooring line length;

[0029] g4: vertical coordinate Z of the bottom section of the mooring wire rope rope Always above the seabed;

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

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

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

[0033] Wherein, the objective function is expressed as the material cost of the mooring line segment that meets the constraint condition is the lowest;

[0034] Wherein, the objective function is as follows:

[0035]

[0036] Where f(X) represents the total cost of the mooring segment, M represents the number of mooring lines, N represents the number of segments on a single mooring line, and D i and L i Respectively represent the diameter and length of the segment, a i and b i is the material price coefficient of the mooring line.

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

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

[0039] Where, X FOWT represents the floating wind turbine platform offset, X lim Indicates the maximum offset limit;

[0040]

[0041] In the formula, F pre Indicates the pretension of the mooring system, F min Indicates the minimum pretension, F max Indicates the maximum pre-tension;

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

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

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

[0045] In the formula, Z rope Indicates the vertical coordinate of the bottom section of the wire rope segment;

[0046]

[0047] Where P sur represents the motion period of the floating fan along the X-axis, P min It represents the minimum period of the floating fan moving along the X axis, P max It represents the maximum period of movement of the floating fan along the X-axis;

[0048] g6=β-β lim <0 (6)

[0049] In the formula, β represents the reliability index of mooring strength, β lim A standard indicator of reliability that represents the strength of a mooring; where:

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

[0051] Where 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 reliability integrated optimization method of the offshore floating wind turbine mooring system in some embodiments of the present application, the constraint condition is as follows:

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

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

[0055]

[0056] In the formula, F pre Indicates the pretension of the mooring system, F min Indicates the minimum pretension, F max Indicates the maximum pre-tension;

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

[0058] In the formula, Z chain It represents the vertical position of the first 1 / 10 of the mooring line 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 Indicates the vertical coordinate of the bottom section of the wire rope segment;

[0061]

[0062] Where P sur represents the motion period of the floating fan along the X-axis, P min It represents the minimum period of the floating fan moving along the X axis, P max It represents the maximum period of movement of the floating fan 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 Indicates the safety factor.

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

[0066] Minimum pretension F min =1500kN, maximum pretension F max =2500kN;

[0067] Water depth H = 200m;

[0068] Minimum period P of floating fan along X-axis min = 60s, the maximum period of movement of the floating fan along the X-axis P max =150s;

[0069] Reliability standard index β of mooring strength lim =3.291.

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

[0071] In a second aspect, an embodiment of the present application further provides an electronic device, comprising: 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, which, when executed by the electronic device, enable the electronic device to execute the first aspect and any possible technical solution of the first aspect.

[0072] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a computer program. When the computer program runs on an electronic device, the electronic device executes the first aspect and any possible technical solution of the first aspect.

[0073] Beneficial effects:

[0074] In the first aspect, the present invention provides a reliability integrated optimization method for a floating wind turbine mooring system. Through the trained proxy model, the output response of the floating wind turbine system of all mooring design schemes within the numerical range of the optimization variable is quickly output, so as to search for the floating wind turbine system output response that satisfies the constraint conditions and the optimal solution of the objective function in the iteration, and obtain the mooring design scheme corresponding to the response. Compared with the existing optimization algorithm, the dynamic response analysis method is used in each iteration to calculate the output of the floating wind turbine system of the mooring design scheme, which has the defect of extremely long response time and is not suitable for the calculation scenario of a larger number of iterations. The present invention saves the simulation calculation time of the dynamic response analysis, can realize the response output of the complex system quickly, can realize more rounds of iterative calculations in a very short time, and quickly and accurately obtain the optimal design scheme. Therefore, the present invention integrates the proxy modeling technology, takes the uncertainty factor as one of the optimization constraints into the design optimization process to carry out the reliability optimization work, optimizes the calculation speed, and greatly reduces the time consumption. This integrated optimization method can quickly identify the optimal mooring design scheme in a wide design space, saving the expensive numerical simulation calculation time. Moreover, the integrated optimization method proposed in the present invention has been rigorously verified and has extremely high reliability, providing an efficient and robust method for economical and reliable optimization of floating wind turbine mooring systems.

[0075] In the second aspect, when constructing a database for training the proxy model, the present invention adopts a hybrid 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 based on the constraint parameters suitable for fast static response analysis in the present invention. Static analysis is used to quickly exclude mooring design schemes that are obviously not feasible, greatly reducing the range of dynamic response analysis samples, and greatly saving response analysis calculation time. Based on the speed improvement of the above response analysis, the present invention can expand the number of samples of more mooring design schemes in a shorter time and perform fast response analysis on them. Therefore, this means allows the construction of a model training database of the present invention with a large number of extended samples, while the prior art is extremely time-consuming due to the use of a separate dynamic response analysis, so it is difficult to use a large number of extended samples for model training database construction. Therefore, the present invention adopts a hybrid 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 purpose of which is to increase the amount of original sample data and complete the response analysis in a short time, and to provide the basic sample amount of the training database quickly, accurately and comprehensively, so as to use the database to train the model and improve the model accuracy.

[0076] On the third side, the accuracy of the trained proxy model is improved, and the model replacement response analysis method can be truly applied in the iteration of the optimization algorithm, truly realizing accurate and fast optimization implementation plans and greatly reducing the optimization calculation costs.

[0077] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A schematic flow chart of a reliability integrated optimization method for an offshore floating wind turbine mooring system provided by the present invention.

[0079] Figure 2 An overall schematic diagram of the example design of the present invention.

[0080] Figure 3 Schematic diagram of the optimization object and optimization variables designed in the example of the present invention, (a) side view, (b) top view.

[0081] Figure 4 Schematic diagram of the motion process designed by the example of the present invention.

[0082] Figure 5 Maximum displacement and maximum force diagram of the floating body operation and mooring system designed in the example of the present invention, (a) maximum displacement, (b) maximum force.

[0083] Figure 6 The proxy model fitting diagram designed by the example of the present invention.

[0084] Figure 7 Cost iteration comparison chart of the example design of the present invention, (a) deterministic optimization, (b) reliability optimization.

[0085] Figure 8 Schematic diagram of constraint iteration of the example design of the present invention, (a1) g1 constraint of deterministic optimization, (b1) g1 constraint with reliability optimization, (a2) g2 constraint of deterministic optimization, (b2) g2 constraint with reliability optimization, (a3) ​​g6 constraint of deterministic optimization, (b3) g7 constraint with reliability optimization.

[0086] Fig. 9 Schematic diagram of variable iteration of the design of the example of the present invention, (a1) the position of the fairlead hole 31 optimized by determinism, (b1) the position of the fairlead hole 31 optimized by reliability, (a2) the diameter 32 of the mooring section 1 optimized by determinism, (b2) the diameter 32 of the mooring section 1 optimized by reliability, (a3) ​​the length 33 of the mooring section 1 optimized by determinism, (b3) the length 33 of the mooring section 1 optimized by reliability, (a4) the diameter 34 of the mooring section 2 optimized by determinism, (b4) the diameter 35 of the mooring section 3 optimized by reliability Diameter 34 of mooring section 2, (a5) length 35 of mooring section 2 optimized by determinism, (b5) length 35 of mooring section 2 optimized by reliability, (a6) diameter 36 of mooring section 3 optimized by determinism, (b6) diameter 36 of mooring section 3 optimized by reliability, (a7) length 37 of mooring section 3 optimized by determinism, (b7) length 37 of mooring section 3 optimized by reliability, (a8) mooring radius 38 optimized by determinism, (b8) mooring radius 38 optimized by reliability. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present invention is further described below in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

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

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

[0090] Step S1, define the optimization problem, determine the optimization object, and select the optimization variables, constraints and objective equation.

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

[0092] Step S3, static-dynamic hybrid mooring analysis method. The "static-dynamic" hybrid method is used to analyze the response of the mooring system. First, the static mooring analysis method (implemented by MIMOSA software) is used to pre-screen the samples according to the constraints defined in step S1, and then the dynamic method (implemented by SIMA software) is used to obtain the accurate response of the nonlinear system, avoiding time domain simulation of all mooring design schemes, thereby saving a lot of computing cost and time.

[0093] Step S4, establishing a proxy model. The mooring design scheme obtained after pre-screening in step S3 and the corresponding wind turbine system dynamic response are used as input (mooring design scheme)-output (wind turbine system dynamic response) data to establish a database. Constructing a proxy model to capture the relationship between input and output, replacing the time domain simulation in the subsequent optimization iteration process, further saving optimization cost and computing time.

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

[0095] The present invention provides a reliability integrated optimization method for a floating wind turbine mooring system. The method uses a hybrid mooring analysis method that combines static and dynamic analysis to simulate and calculate the mooring system, so as to quickly and accurately obtain its dynamic response. In addition, the proxy modeling technology is integrated to consider the uncertainty factor as one of the optimization constraints and incorporate it into the design optimization process to carry out reliability optimization work. This integrated optimization method can quickly identify the optimal mooring design solution in a wide design space, saving expensive numerical simulation calculation time. The integrated optimization method proposed in the present invention has been strictly verified and provides an efficient and robust method for the economical and reliable optimization of floating wind turbine mooring systems.

[0096] In some embodiments, referring to step S1, defining the optimization problem, determining the optimization object, selecting the optimization variables, constraints and objective equations comprises the following steps:

[0097] The selection of mooring design parameters is crucial. This embodiment prefers the mooring design configuration that is currently most widely used on offshore platforms: multi-stage mooring, consisting of anchor chain-wire rope-anchor chain. The mooring system consists of three symmetrical mooring cables, such as Figure 3 As shown, it includes fairlead hole position 31, mooring section 1 diameter 32, mooring section 1 length 33, mooring section 2 diameter 34, mooring section 2 length 35, mooring section 3 diameter 36, mooring section 3 length 37 and mooring radius 38. Through these 8 optimization variables, a mooring line can be defined and constructed. The embodiment intends to optimize these 8 optimization variables, with the purpose of obtaining a composition scheme with the lowest cost that meets the constraints.

[0098] For reliability optimization problems, it is also necessary to consider the uncertainty of optimization variables and incorporate the inherent uncertainty in materials and construction processes into the design optimization, that is, it is necessary to introduce random variables. In the embodiment, the present invention considers the inherent uncertainty in the construction process by considering the fairlead hole position 31 as a random variable. In addition, the catenary mooring system is usually equipped with a towing anchor. Taking into account the construction errors in the installation process and the unstable factors caused by long-term environmental and geological changes, the mooring radius 38 is also regarded as a random variable.

[0099] Therefore, the optimization variables and random variables in the present invention are summarized in Table 1. The lower and upper limits are selected based on the physical configuration of the mooring system and the material properties of the mooring line material. The performance of the chain and 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, 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. Its design needs to ensure the safe and stable operation of the floating wind turbine system. A series of constraints need to be considered in the optimization design process. In the embodiment, the constraints are considered to meet the constraints under the extreme sea conditions of once in 50 years to ensure the safe operation of the floating wind turbine system throughout the entire operating life cycle. The constraints include:

[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, considering preventing the power transmission cable from being damaged, X limIt is set to 25m, that is, under the limitation of the mooring system, the maximum displacement of the floating wind turbine platform under the action of wind and waves cannot exceed 25m.

[0106] (2) Pretension F of the mooring system pre Keep it within a reasonable range. Excessive pre-tension may cause the mooring line to be subjected to stress exceeding its strength limit, while insufficient pre-tension may cause excessive displacement of the floating wind turbine platform under environmental loads, affecting the stability of the wind turbine system.

[0107]

[0108] In the embodiment, the minimum pretension F is calculated based on the engineering design experience. min is limited to 1500kN, the maximum pretension F max Limited to 2500kN.

[0109] (3) The mooring cable maintains a sufficient length on the seabed under maximum deflection. The movement of the floating wind turbine platform under the action of wind and waves will drive the movement of the mooring cable, thereby lifting the bottom-lying mooring cable from the seabed. Figure 4 As shown. Due to its low cost and easy installation, catenary mooring design is usually equipped with a towing anchor. Excessive oblique tension may cause the towing anchor to fail. Therefore, it is necessary to ensure that a certain bottom-lying section of the mooring cable is retained under the maximum offset of the floating wind turbine platform to avoid the towing anchor from being subjected to oblique tension. In the embodiment, the length of the ground-touching section under the maximum offset limit must be at least one-tenth of the total mooring cable length.

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

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

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

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

[0114] (5) Floating fan motion period P along the X axis sur When the catenary mooring system adopts different design schemes, the P of the floating wind turbine system surwill change. Usually, it is necessary to ensure that P sur Avoid the common wave period (5-30s) in my country's sea areas to prevent excessive vibration caused by wave excitation, accelerate structural fatigue damage, 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, too large a P sur This means that the mooring system is too “loose”, which may cause the floating wind turbine maximum excursion limit constraint g1 to fail to meet the requirements. Therefore, in this study, the minimum P min is limited to 60s, the maximum P max Limited to 150s.

[0115]

[0116] (6) The mooring cable strength is safe during operation and no breakage occurs. The maximum mooring tension S under the limit state should always be lower than the breaking strength R. In the reliability optimization proposed in the present 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 recommended by the relevant design specifications is 5E-4, corresponding to β lim is 3.291. As pointed out in the background, in the current optimization method of floating wind turbine mooring system, the deterministic safety factor design method is basically adopted to consider the strength safety of mooring cable. In order to compare the difference between using reliability index as mooring strength constraint and using safety factor as constraint, the present invention also performs deterministic optimization. Therefore, the constraint in the deterministic optimization is g7. According to the relevant design specifications, the safety factor f of each mooring cable under complete conditions is s 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] Where 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.

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

[0122] Under the premise of satisfying all the constraints of the mooring system, the present invention selects the cost of the mooring system as the optimization target in the optimization process. Generally, the cost of the mooring system includes the material cost of the mooring cable, anchor foundation, connection 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 lines, N represents the number of segments on a single mooring line, D and L represent the diameter and length of the segment respectively, and a i and b i is the material price factor provided by the mooring line manufacturer.

[0125] In some embodiments, referring to step S2, parametric modeling of the mooring system includes generating 20,000 random samples representing various mooring design schemes according to the design range of 8 optimization variables using the Latin hypercube sampling method, and using a programming method to realize automated and batch parametric modeling of the 20,000 random samples by the mooring analysis software.

[0126] In some embodiments, referring to step S3, a static-dynamic hybrid method is used to perform mooring response analysis, including the present invention first using MIMOSA software to perform static mooring analysis, preliminarily screening out unfeasible mooring design solutions, so as to reduce the subsequent dynamic simulation cost and narrow the design space for optimization search. The average analysis time of each mooring design solution in static mooring analysis is 2s. In static analysis, the catenary equation can be solved to obtain F pre To determine whether g2 meets the requirements; introduce the hydrodynamic information of the floating wind turbine to solve the mass stiffness matrix of the coupling system, so as to determine the system P sur And evaluate whether the constraint g5 meets the requirements. For the mooring design scheme that meets g2 and g5, impose the maximum platform offset limit X limTo determine whether the mooring cable still has a bottom-lying section and whether the wire rope contacts the seabed under the maximum platform offset (25m in this embodiment), so as to screen out the mooring design schemes that do not meet g3 and g4. However, it should be noted that for the g3 and g4 constraints, only the mooring design schemes that do not meet the constraints under the maximum offset are screened out in the static analysis. Whether the actual offset results of the floating wind platform and the corresponding offsets meet the requirements of g3 and g4 needs to be further calculated and checked in the dynamic analysis. In short, the constraints used for preliminary sample screening in the static analysis are g2-g5, and the constraints used in the static analysis are constraints that do not require dynamic calculations and are not seriously affected by the nonlinearity of complex sea conditions. The above method preliminarily screened out unfeasible mooring design schemes, leaving 1028 feasible mooring design schemes, greatly reducing the subsequent dynamic simulation costs.

[0127] In order to accurately solve the response of the nonlinear mooring system under nonlinear environmental loads, the embodiment uses SIMA software to dynamically simulate the remaining 1028 feasible mooring design schemes. Environmental conditions (wave height, wave period, wind speed) on the east coast of the United States are selected to evaluate the performance of floating wind turbines under extreme design conditions. Figure 5 The maximum platform offset and maximum mooring force of a floating wind turbine under a 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 for each mooring design sample under given marine environmental conditions is obtained. FOWT , the vertical position Z of the first 1 / 10 of the mooring line length measured from the anchor point chain , the vertical coordinate Z of the bottom section of the wire rope rope , and the maximum mooring tension S, thereby screening the remaining constraints and finally obtaining 1000 mooring design solutions that meet the design constraints. Thus, an input-output database between these 1000 mooring design solutions and these output responses is constructed.

[0128] In some embodiments, referring to step S4, a proxy model is established, including using 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. Thereby, 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. It is difficult to obtain prior knowledge that the accuracy of the proxy model is consistent with the complexity of the research problem, so it is necessary to compare different proxy models, and preferably the proxy model that is most suitable for the optimization problem. The embodiment selects three proxy models, namely Kriging, polynomial response surface (PRS) model and artificial neural network (ANN) model, for comparison, and uses RAAE, RMSE and R 2Three statistical indicators are used to test the accuracy of the proxy model. Taking the tension of mooring section 2 as an example, the comparison results are as follows.

[0129] Table 2 Comparative analysis of the maximum tension proxy model error in mooring section 2

[0130]

[0131] It can be seen that the Kriging model performs best. Different error indicators RAAE and RMSE have the lowest error values, and the determination coefficient R 2 The Kriging model has a good fit for nonlinear relationships and is fast to train. Figure 6 The prediction performance of the Kriging proxy model is demonstrated by taking the drift of a floating wind turbine platform as an example. It can be observed that the prediction of the Kriging model is very consistent with the simulated 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 adopts the sequential quadratic programming (SQP) optimization algorithm to perform nonlinear constrained optimization on the floating wind turbine mooring system. By giving the optimization algorithm an initial point, in this embodiment, it is the mooring design scheme composed of the 8 optimization variables defined in step S1 within the corresponding range. The optimization algorithm will continuously search and iterate in the design space, and 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 objective function (i.e., the mooring system design cost). Under the framework of this method, the Kriging proxy model trained in step S4 will be combined in the iterative process of the optimization algorithm to replace the dynamic simulation during the search for the optimal design scheme to achieve rapid iteration. At the same time, since the distribution of the training data samples is static-dynamically screened, the algorithm is not "blind" in the optimization iteration process. It will actively search in the direction that meets the constraints, thereby greatly reducing the search space and thus reducing the optimization calculation cost. In reliability optimization, it is necessary to solve the mooring strength reliability index for each scheme in the iterative process. The reliability index is simulated by Monte Carlo simulation method. The mooring design scheme in each iteration 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 proxy model, which greatly reduces the computational burden compared with direct dynamic analysis.

[0133] Figure 7 The cost iteration comparison diagram of deterministic optimization (a) and reliability optimization (b) of the example design of the present invention is shown. Figure 8 The diagram shows the constraint iteration diagram of deterministic optimization (a) and reliability optimization (b) of the example design of the present invention. Fig. 9The variable iteration schematic diagram of the deterministic optimization (a) and reliability optimization (b) designed in the example of the present invention is shown. Among them, the constraints used in the deterministic optimization (a), that is, the deterministic optimization in step S1 of the present invention are represented as g1-g5 and g7. Reliability optimization (b), that is, the constraints used in the reliability integrated optimization method in step S1 of the present invention are g1-g6. The accuracy of this method is verified by taking the deterministic optimization results as an example as shown in Table 3. At the same time, Table 4 provides the optimization results and related constraints of the two optimization methods of deterministic optimization and reliability optimization.

[0134] Table 3. Deterministic optimization results and constraint verification

[0135]

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

[0137]

[0138] It can be seen that in both optimization results, the optimization algorithm greatly reduces the cost of the initial mooring system design, with a reduction of up to 68.3%. The reliability optimization (b) achieves a lower cost while meeting the reliability constraint, with a reduction of up to 68.5%. According to Table 4 and Figure 8 Comparison between (a1) and (a2) shows that it exhibits better platform excursion limitation performance. This further indicates that the mooring strength safety factor used in the deterministic optimization (a) is conservative. Using the method provided by the present invention, the constraint conditions can be dynamically adjusted according to the actual engineering problem requirements without increasing a large amount of computational cost, which further illustrates the efficiency and accuracy of the method. The optimal solution was verified using time domain simulation (dynamic response analysis), and the results were consistent with those obtained in the proxy model, with a maximum relative error of less than 5.0%.

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

[0140] The above description of the specific implementation is intended to describe and illustrate the technical solution of the present invention, and the above specific implementation is only illustrative and not restrictive. Without departing from the scope of the present invention and the scope protected by the claims, ordinary technicians in this field can also make more specific changes under the enlightenment of the present invention, which all belong to the protection scope of the present invention.

[0141] Based on the above embodiments, the embodiments of the present application further provide a computer program, which, when executed on a computer, enables the computer to execute the methods provided in the above embodiments.

[0142] Based on the above embodiments, the embodiments of the present application further provide a computer storage medium, in which a computer program is stored. When the computer program is executed by a computer, the computer executes the method provided in the above embodiments.

[0143] The storage medium may be any available medium that can be accessed by a computer. For example, but not limited to, a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0144] Based on the above embodiments, an embodiment of the present application further provides a chip, which is used to read a computer program stored in a memory to implement the method provided in the above embodiments.

[0145] Based on the above embodiments, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the method provided in the above embodiments is implemented.

[0146] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include 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 process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A reliability integrated optimization method for an offshore floating wind turbine mooring system, characterized in that: The optimization algorithm iteratively searches for the best mooring design solution that satisfies the constraints and has the minimum objective function value among all mooring design solutions within the numerical range of the optimization variables; Wherein, in each iteration, the optimization algorithm obtains the output response of the floating wind turbine system of the parameterized model corresponding to the current mooring design scheme through the proxy model; the optimization algorithm determines whether the output response of the floating wind turbine system satisfies the constraint condition, and calculates the objective function value according to the output response of the floating wind turbine system; The input of the proxy model is the current mooring design solution, and the output is the floating wind turbine system output response of the parameterized model of the current mooring design solution.

2. The reliability integrated optimization method for offshore floating wind turbine mooring system according to claim 1 is characterized in that: in, The proxy model is obtained based on the following method: S10. Build a training database; S20. Using the training database to train the model to obtain the proxy model; Wherein, building a training database in step S10 includes: S110. Expanding the mooring design scheme according to the mooring cable optimization variables and their numerical ranges, and establishing a parameterized model of the expanded mooring design scheme; S120. Performing a static mooring response analysis on the parameterized model to obtain a first constraint parameter of the parameterized model; S130. The extended mooring design scheme in which the first constraint parameter satisfies the first constraint condition is a first mooring design feasible scheme; S140. Performing a dynamic mooring response analysis on the parameterized model of the first mooring design feasible solution to obtain a second constraint parameter; S150. The first mooring design feasible solution whose second constraint parameter satisfies the second constraint condition is the second mooring design feasible solution; S160. Constructing an input-output database between the second mooring design feasible solution and the constraint parameters of the second mooring design feasible solution to obtain the training database; The constraint parameters include a first constraint parameter and a second constraint parameter, the data representing the input in the database is the second mooring design feasible solution, the data representing the output is the constraint parameter, and the constraint parameter is the output response of the floating wind turbine system.

3. The reliability integrated optimization method for offshore floating wind turbine mooring system according to claim 2 is characterized in that: in, The first constraint parameter includes the pretension F of the mooring system pre , the floating fan motion period along the X axis P sur , the vertical position Z of the first 1 / 10 of the mooring line length measured from the anchor point chain , and the vertical coordinate Z of the bottom section of the wire rope segment rope ; Wherein, the second constraint parameter includes the floating wind turbine platform offset X FOWT , the vertical position Z of the first 1 / 10 of the mooring line length measured from the anchor point chain , the vertical coordinate Z of the bottom section of the wire rope rope , and maximum mooring tension S; In step S130, the first constraint condition includes g2, g5, g3 and g4, wherein judging whether the first constraint parameter satisfies the first constraint condition includes: S121. The mooring design scheme whose first constraint parameters satisfy the first constraint conditions g2 and g5 is a preliminary first mooring design feasible scheme; S122. Apply the maximum platform offset limit X lim The first mooring design feasible solution whose first parameter satisfies the first constraint conditions g3 and g4 is the first mooring design feasible solution; Wherein, in step S140, the second constraint condition includes g1, g3, g4 and g6; The constraints include: g1: floating wind turbine platform offset X FOWT Does not exceed the maximum offset limit X lim ; g2: Pretension F of the mooring system pre Keep within certain limits; g3: The length of the mooring line touching the ground must be at least one tenth of the total mooring line length; g4: vertical coordinate Z of the bottom section of the mooring wire rope rope Always above the seabed; g5: The movement cycle of the floating fan along the X-axis is kept within a certain range; g6: The maximum mooring tension in the limit state is always lower than the breaking strength.

4. The reliability integrated optimization method for offshore floating wind turbine mooring system according to any one of claims 1 to 3, characterized in that: in, The mooring cable optimization variables include fairlead hole position, mooring segment diameter, mooring segment length and mooring radius; wherein the mooring segment includes a first starting anchor chain segment, a second steel wire rope segment and a third terminal anchor chain segment; Wherein, the objective function is expressed as the material cost of the mooring line segment that meets the constraint condition is the lowest; Wherein, the objective function is as follows: Where f(X) represents the total cost of the mooring segment, M represents the number of mooring lines, N represents the number of segments on a single mooring line, and D i and L i Respectively represent the diameter and length of the segment, a i and b i is the material price coefficient of the mooring line.

5. The reliability integrated optimization method for offshore floating wind turbine mooring system according to any one of claims 1 to 4, characterized in that: in, The constraints include: g1=X FOWT -X lim <0 (1) Where, X FOWT represents the floating wind turbine platform offset, X lim Indicates the maximum offset limit; In the formula, F pre Indicates the pretension of the mooring system, F min Indicates the minimum pretension, F max Indicates the maximum pre-tension; g3=Z chain +H<0 (3) In the formula, Z chain It represents the vertical position of the first 1 / 10 of the mooring line length measured from the anchor point, and H represents the water depth; g4=-(Z rope +H)<0 (4) In the formula, Z rope Indicates the vertical coordinate of the bottom section of the wire rope segment; Where P sur represents the movement period of the floating fan along the X-axis, P min It represents the minimum period of the floating fan moving along the X axis, P max It represents the maximum period of movement of the floating fan along the X-axis; g6＝β-β lim ＜0 (6) In the formula, β represents the reliability index of mooring strength, β lim A standard indicator of reliability that represents the strength of a mooring; where: β=-Φ -1 (P f )=-Φ -1 (Pr{S>R}) (7) Where 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.

6. The reliability integrated optimization method for offshore floating wind turbine mooring system according to any one of claims 1 to 4, characterized in that: in, The constraint condition is as follows: g1=X FOWT -X lim <0 (1) Where, X FOWT represents the floating wind turbine platform offset, X lim Indicates the maximum offset limit; In the formula, F pre Indicates the pretension of the mooring system, F min Indicates the minimum pretension, F max Indicates the maximum pre-tension; g3=Z chain +H<0 (3) In the formula, Z chain It represents the vertical position of the first 1 / 10 of the mooring line length measured from the anchor point, and H represents the water depth; g4=-(Z rope +H)<0 (4) In the formula, Z rope Indicates the vertical coordinate of the bottom section of the wire rope segment; Where P sur represents the movement period of the floating fan along the X-axis, P min It represents the minimum period of the floating fan moving along the X axis, P max It represents the maximum period of movement of the floating fan along the X-axis; g6=S·f s -R<0 (8) In the formula, S represents the maximum mooring tension, R represents the breaking strength, and f s Indicates the safety factor.

7. The reliability integrated optimization method for offshore floating wind turbine mooring system according to claim 5 or 6, characterized in that: in, Maximum offset limit X lim =25m; Minimum pretension F min =1500kN, maximum pretension F max =2500kN; Water depth H = 200m; Minimum period P of floating fan moving along X axis min = 60s, the maximum period of movement of the floating fan along the X-axis P max =150s; Reliability standard index β of mooring strength lim =3.

291.

8. The reliability integrated optimization method for offshore floating wind turbine mooring system according to any one of claims 1 to 7, characterized in that: The proxy model is a Kriging model.

9. An offshore floating wind turbine mooring system reliability integrated optimization system, the system comprising: 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, which, when executed by the system, cause the system to perform any one of the methods of claims 1-8.

10. A computer-readable storage medium, comprising a computer program, and when the computer program is executed on an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 8.

Images