Method and device for optimizing the arrangement of transport routes of wind turbine generator sets

Abstract

The present disclosure provides a method and device for optimizing the arrangement of the transport path of a wind turbine generator set. The method includes: initializing a transport network according to the transport process nodes of various components, the transport process nodes including the shipping address, wind farm address and transshipment port address of various components; generating multiple chromosome individuals as an initial population through chromosome encoding according to the transport network, each chromosome individual including a feasible transport path for transporting each component from the shipping address to the wind farm address; calculating the fitness value of each chromosome individual in the initial population according to the fitness function; selecting chromosome individuals in the initial population whose fitness value is greater than a predetermined threshold to perform crossover and mutation operations until the number of iterations reaches the maximum evolutionary generation or the set convergence standard; outputting the chromosome individual with the maximum fitness value as the optimal solution; and performing chromosome decoding on the optimal solution to generate an arrangement optimization scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method and device for optimizing the arrangement of a transport path of a wind power generator set. Background Art

[0002] On the one hand, with the vigorous development of wind power projects, multiple wind power projects will be started and constructed at the same time, and usually a factory producing wind turbine components is required to supply multiple wind power projects.

[0003] On the other hand, wind turbine components (e.g., blades, generators, etc.) are usually produced by factories in different regions, and there are situations where the same transport ship transports multiple components. At the same time, the transportation of components for offshore wind power projects involves multiple links such as land transportation, port transshipment, and sea transportation, and each link is affected by many factors such as cost factors, weather factors, and ship resource factors. Summary of the invention

[0004] The purpose of the exemplary embodiments of the present invention is to provide a method and device for optimizing the arrangement of a transportation path of a wind turbine generator set, so as to overcome the deficiencies in the prior art.

[0005] According to an exemplary embodiment of the present invention, a method for optimizing the layout of a transport path of a wind turbine generator set is provided, the method comprising: initializing a transport network of the wind turbine generator set according to transport process nodes of various components of the wind turbine generator set, the transport process nodes comprising a shipping address, a wind farm address and a transshipment port address of the various components; generating a plurality of chromosome individuals as an initial population according to the transport network through chromosome encoding, each chromosome individual comprising a feasible transport path for transporting each component of the wind turbine generator set from the shipping address to the wind farm address; calculating the fitness value of each chromosome individual in the initial population according to a fitness function; selecting chromosome individuals in the initial population whose fitness values ​​are greater than a predetermined threshold to perform crossover and mutation operations until the number of iterations reaches a maximum evolutionary generation or a set convergence criterion; outputting the chromosome individual with the maximum fitness value as an optimal solution; and performing chromosome decoding on the optimal solution to generate an optimized layout scheme for the transport path of the wind turbine generator set.

[0006] Optionally, according to the transportation process nodes of various components of the wind turbine generator set, the step of initializing the transportation network of the wind turbine generator set includes: obtaining data associated with the transportation process nodes, the data associated with the transportation process nodes including ship data, meteorological data, hydrological data, port data, route data, shipping place data, wind farm data and at least one of data about constraint conditions; and inputting the data associated with the transportation process nodes into the transportation network.

[0007] Optionally, the chromosome individual includes gene segments representing the following information: the shipping address of various components of the wind turbine, the address of the wind farm, the total transport volume of a single shipment, and the following items: the transportation route from the shipping place to the wind farm; and / or, the address of the transshipment port, the transportation route from the shipping place to the transshipment port, and the transportation route from the transshipment port to the wind farm; and / or, the address of the transshipment port, the transportation route from the shipping place to the transshipment port, the transportation route from the transshipment port to the wind farm, and the transportation routes between multiple transshipment ports.

[0008] Optionally, according to the transportation network, after chromosome encoding, the step of generating multiple chromosome individuals as the initial population includes: selecting the shipping address of each component to form a shipping address group; associating the shipping address group with each wind farm address; selecting a transit port and a transportation route between the shipping address group and the associated wind farm address; and generating multiple chromosome individuals.

[0009] Optionally, according to the transportation network, the step of generating multiple chromosome individuals as the initial population through chromosome encoding also includes: eliminating gene segments that do not meet the constraints, and regenerating chromosome individuals until chromosome individuals with gene segments that have not been eliminated are generated; and using the chromosome individuals with gene segments that have not been eliminated as the initial population.

[0010] Optionally, the step of eliminating gene segments that do not meet the constraints includes: calculating the transportation time of the feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path; eliminating gene segments whose transportation time exceeds the predetermined transportation time and / or whose ship transportation capacity exceeds the ship transportation capacity constraint of the corresponding node.

[0011] Optionally, the fitness function is:

[0012]

[0013] Among them, Z is the fitness value of the feasible transport path corresponding to the chromosome individual, C l is the land transportation cost of the feasible transportation path corresponding to the chromosome individual, C o is the ocean transportation cost of the feasible transportation path corresponding to the chromosome individual, C t is the loading and unloading cost of the feasible transportation path corresponding to the chromosome individual, C s is the storage cost of the feasible transportation path corresponding to the chromosome individual, C to is the delay penalty cost of the feasible transportation path corresponding to the chromosome individual, C zis the demurrage penalty cost of the feasible transport path corresponding to the chromosome individual, f1 is a parameter set to ensure the ship transport capacity. If the ship transport capacity meets the ship transport capacity constraint, f1 is equal to 1, if the ship transport capacity does not meet the ship transport capacity constraint, f1 is equal to 0.

[0014] Optionally, the ship transport capacity constraints include at least one of the following conditions: ship size ≥ 1.2× unit size, ship load ≥ 1.2× unit component weight + fixture weight, minimum water depth of the sea area ≥ ship draft, ship size ≥ the size of ships that can be berthed in the port, speed conditions meet the predetermined speed range, the dynamic response of the ship meets the meteorological and hydrological requirements of the sea area, and the ship leasing method and the costs incurred meet economic requirements.

[0015] According to an exemplary embodiment of the present invention, a layout optimization device for a transportation path of a wind turbine generator set is provided, the layout optimization device comprising: a transportation network initialization unit, configured to initialize the transportation network of the wind turbine generator set according to the transportation process nodes of various components of the wind turbine generator set, the transportation process nodes including the shipping address, wind farm address and transshipment port address of various components; a chromosome encoding unit, configured to generate a plurality of chromosome individuals as an initial population according to the transportation network through chromosome encoding, each chromosome individual including each component of the wind turbine generator set transported from the shipping address to the wind farm A feasible transportation path for the address; a fitness value calculation unit, configured to calculate the fitness value of each chromosome individual in the initial population according to the fitness function; a crossover and mutation unit, configured to select chromosome individuals with fitness values ​​greater than a predetermined threshold in the initial population for crossover operations and mutation operations, until the number of iterations reaches a maximum evolutionary generation or a set convergence standard; an optimal solution output unit, configured to output the chromosome individual with the maximum fitness value as the optimal solution; a chromosome decoding unit, configured to perform chromosome decoding on the optimal solution to generate an optimized arrangement plan for the transportation path of the wind turbine generator set.

[0016] Optionally, the transportation network initialization unit is further configured to: obtain data associated with the transportation process node, the data associated with the transportation process node including ship data, meteorological data, hydrological data, port data, route data, shipping place data, wind farm data and at least one of data about constraints; and input the data associated with the transportation process node into the transportation network.

[0017] Optionally, the chromosome individual includes gene segments representing the following information: the shipping address of various components of the wind turbine, the address of the wind farm, the total volume of a single shipment; and the following items: the transportation route from the shipping place to the wind farm; and / or, the address of the transshipment port, the transportation route from the shipping place to the transshipment port, and the transportation route from the transshipment port to the wind farm; and / or, the address of the transshipment port, the transportation route from the shipping place to the transshipment port, the transportation route from the transshipment port to the wind farm, and the transportation routes between multiple transshipment ports.

[0018] Optionally, the chromosome encoding unit is further configured to: select the shipping address of each component to form a shipping address group; associate the shipping address group with each wind farm address; select a transit port and a transportation route between the shipping address group and the associated wind farm address; and generate multiple chromosome individuals.

[0019] Optionally, the chromosome encoding unit is further configured to: remove gene segments that do not meet the constraint conditions, and regenerate chromosome individuals until chromosome individuals with gene segments that have not been removed are generated; and use chromosome individuals with gene segments that have not been removed as the initial population.

[0020] Optionally, the chromosome encoding unit is further configured to: calculate the transportation time of the feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path; and eliminate gene segments whose transportation time exceeds the predetermined transportation time and / or gene segments whose ship transportation capacity exceeds the ship transportation capacity constraint of the corresponding node.

[0021] Optionally, the fitness function is:

[0022]

[0023] Among them, Z is the fitness value of the feasible transport path corresponding to the chromosome individual, C l is the land transportation cost of the feasible transportation path corresponding to the chromosome individual, C o is the ocean transportation cost of the feasible transportation path corresponding to the chromosome individual, C t is the loading and unloading cost of the feasible transportation path corresponding to the chromosome individual, C s is the storage cost of the feasible transportation path corresponding to the chromosome individual, C to is the delay penalty cost of the feasible transportation path corresponding to the chromosome individual, C z is the demurrage penalty cost of the feasible transport path corresponding to the chromosome individual, f1 is a parameter set to ensure the ship transport capacity. If the ship transport capacity meets the ship transport capacity constraint, f1 is equal to 1, if the ship transport capacity does not meet the ship transport capacity constraint, f1 is equal to 0.

[0024] Optionally, the ship transport capacity constraints include at least one of the following conditions: ship size ≥ 1.2× unit size, ship load ≥ 1.2× unit component weight + fixture weight, minimum water depth of the sea area ≥ ship draft, ship size ≥ the size of ships that can be berthed in the port, speed conditions meet the predetermined speed range, the dynamic response of the ship meets the meteorological and hydrological requirements of the sea area, and the ship leasing method and the costs incurred meet economic requirements.

[0025] According to an exemplary embodiment of the present disclosure, a computer-readable storage medium storing a computer program is provided. When the computer program is executed by a processor, the method for optimizing the arrangement of a transport path of a wind turbine generator set as described above is implemented.

[0026] According to an exemplary embodiment of the present disclosure, a computing device is provided, the computing device comprising: a processor; a memory storing a computer program, and when the computer program is executed by the processor, the method for optimizing the arrangement of the transport path of the wind turbine generator set as described above is implemented.

[0027] The method, device and storage medium for optimizing the layout of the wind turbine transportation path according to the exemplary embodiment of the present invention can provide scientific decision-making guidance for transportation strategies and plans; comprehensively consider various factors that affect the layout planning of the wind turbine transportation path, model and digitize the wind turbine transportation process affected by complex variable factors, save labor costs, improve the accuracy and precision of the transportation path layout planning, and provide a transportation strategy that is more in line with engineering practice; consider all aspects of wind turbine transportation, and optimize the layout of wind turbine transportation paths and transportation strategies based on genetic algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and other objects and features of the present invention will become more apparent from the following description in conjunction with the accompanying drawings.

[0029] Figure 1 is a flow chart of a method for optimizing the arrangement of a wind turbine transport path according to an exemplary embodiment of the present disclosure.

[0030] Figure 2 is a flowchart of a process of generating an initial population in an arrangement optimization method according to an exemplary embodiment of the present disclosure.

[0031] Figure 3 is a schematic diagram of a device for optimizing the arrangement of a transport path of a wind turbine generator set according to an exemplary embodiment of the present disclosure.

[0032] Figure 4 is a schematic diagram of a computing device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] Today, with the booming development of wind power generation, summarizing theories in combination with practice and applying theories to practice and verifying them through practice are the inevitable requirements for the leapfrog development of wind power generation. With the booming development of wind power generation projects, there will be multiple wind power generation projects under construction at the same time, thus the problem of needing a factory producing wind turbine components to supply multiple wind power generation projects arises.

[0034] On the other hand, wind turbine components (such as blades, generators, towers, etc.) are usually produced by factories in different regions, and there are situations where the same transport ship transports multiple components. For example, the transportation of components for offshore wind power projects involves multiple links such as land transportation, port transshipment, and sea transportation, and each link will be affected by many factors such as cost factors, weather factors, and ship resource factors.

[0035] With the large-scale development of wind power projects, the factors that need to be analyzed in transportation plans are growing exponentially. How to integrate the control principles of ship and capacity conditions, consider external influencing factors, and reflect the principle of human judgment to arrange the optimal transportation strategy for wind power projects has become an urgent problem to be solved. The existing wind turbine transportation route planning is still in the stage of manual judgment based on experience and relevant data. With the increase in wind power construction projects and the increase in transportation distances, the amount of calculation will inevitably grow exponentially, and it is difficult to obtain the optimization of wind turbine transportation route arrangement through manual technology.

[0036] The present disclosure proposes a layout optimization method and a layout optimization device for the transportation path of a wind turbine generator set, which can use a genetic algorithm to generate a layout optimization plan for the transportation path of a wind turbine generator set. The layout optimization plan for the transportation path of a wind turbine generator set can be used to transport various components of the wind turbine generator set from the shipping address to each wind farm address to complete the construction of each wind farm project. The layout optimization method and the layout optimization device can comprehensively consider various factors that affect the layout planning of the transportation path of a wind turbine generator set based on a genetic algorithm, while saving labor costs, improving the accuracy and precision of the transportation path layout planning, and realizing the layout optimization of the transportation path of a wind turbine generator set or the optimization of the transportation strategy.

[0037] Now, exemplary embodiments of the present disclosure will be described more fully with reference to the accompanying drawings. Examples of the embodiments are shown in the accompanying drawings, wherein the same reference numerals represent the same components throughout. Although some exemplary embodiments are shown in the accompanying drawings, the present invention is not limited thereto.

[0038] Figure 1 is a flow chart of a method for optimizing the arrangement of a wind turbine transport path according to an exemplary embodiment of the present disclosure.

[0039] like Figure 1As shown, in operation S11, the transportation network of the wind turbine generator set can be initialized according to the transportation process nodes of various components of the wind turbine generator set. For example, the transportation process nodes may include, but are not limited to, the shipping address, wind farm address, and transit port address of various components. The transportation network may include all combinations of all feasible transportation paths generated based on the transportation process nodes of various components.

[0040] Optionally, in operation S11, data associated with the transport process node may also be obtained, and the data associated with the transport process node may be input into the transport network. The data associated with the transport process node may include, but is not limited to, at least one of ship data, meteorological data, hydrological data, port data, route data, shipping place data, wind farm data, and data about constraints. According to an exemplary embodiment of the present disclosure, the data associated with the transport process node may be requested and obtained from a cloud server or database through wired communication or wireless communication.

[0041] Ship data may include ship size, ship load, dynamic response of the ship (e.g., pitch, roll, heave), ship leasing mode (e.g., daily, monthly, annual) and corresponding leasing fee, etc., related to the transport process node. Meteorological data may include rainfall, typhoon path and level, weather forecast data, etc., related to the transport process node. Hydrological data may include water level of the sea area related to the transport process node. Port data may include the number and size of ships that can be berthed at the port related to the transport process node, the throughput of the port, the cargo category of the port, the opening hours of the port, etc. The route data may include the route ship dispatch density, the round-trip voyage time of the route, the average total loading and unloading quota of the route, the total cargo flow of the route and the cargo flow between two ports, etc., related to the transport process node. The place of shipment data may include the geographical coordinates of the place of shipment, etc. The wind farm data may include the geographical coordinates of the wind farm, etc. Data on constraints may include restrictions on the number of transfers of various components of wind turbines during transportation (for example, the limit on the number of transfers is one, that is, at most one transfer is allowed), transportation time constraints related to the transportation process nodes (for example, transportation time constraints may include land transportation time constraints, port operation time constraints, sea transportation time constraints, total transportation time constraints, etc.), ship or vehicle capacity constraints (for example, ships and vehicles are subject to constraints such as meteorological conditions, hydrological conditions, port transshipment capacity, etc.), ship capacity constraints (for example, the loading amount of wind turbine components in the transportation route must not exceed the capacity of the ship on the route), etc.

[0042] In operation S12, according to the transportation network, multiple chromosome individuals are generated as an initial population through chromosome encoding, and each chromosome individual may include a feasible transportation path for transporting each component of the wind turbine generator set from the shipping address to the wind farm address. Each chromosome individual may correspond to a set of feasible transportation paths for transporting each component of the wind turbine generator set from the shipping address to the wind farm address.

[0043] In an exemplary embodiment of the present disclosure, chromosome encoding may be performed using encoding methods such as binary encoding, floating point encoding, and symbol encoding.

[0044] According to an exemplary embodiment of the present disclosure, a chromosome individual may include a gene segment representing the following information: the shipping address, wind farm address, and total transportation volume of a single transport of various components of a wind turbine generator set, and the following items: the transportation route from the shipping place to the wind farm (i.e., the components are directly transported from the shipping address to the wind farm address without transit); and / or, the transit port address, the transportation route from the shipping place to the transit port, and the transportation route from the transit port to the wind farm (i.e., the components are transited once during the transportation process); and / or, the transit port address, the transportation route from the shipping place to the transit port, the transportation route from the transit port to the wind farm, and the transportation route between multiple transit ports (i.e., the components are transited multiple times during the transportation process). Among them, the total transportation volume of a single transport may represent the total number of wind turbine generator sets that are transported to the wind farm address after the feasible transportation path corresponding to the chromosome individual is completely transported once.

[0045] The feasible transportation path corresponding to the chromosome individual may include multiple transportation routes, for example, no transit, one transit and / or multiple transits during transportation.

[0046] According to an exemplary embodiment of the present disclosure, the operation of generating a plurality of chromosome individuals as an initial population may include a plurality of operations. Figure 2 A process of generating an initial population according to an exemplary embodiment of the present disclosure is described.

[0047] Figure 2 is a flowchart of a process of generating an initial population in an arrangement optimization method according to an exemplary embodiment of the present disclosure.

[0048] In operation S21, the shipping address of each component of the wind turbine generator set may be selected to form a shipping address group. In an exemplary embodiment of the present disclosure, the transportation network of the wind turbine generator set may include M1 shipping addresses for blades, M2 shipping addresses for nacelle generators, M3 shipping addresses for towers, and N wind farm addresses. Therefore, M1 shipping addresses for blades, M2 shipping addresses for nacelle generators, and M3 shipping addresses for towers may be selected to form a shipping address group for the wind turbine generator set. Among them, M1, M2, and M3 are all natural numbers greater than or equal to 1. For example, if only one shipping address is selected from the shipping addresses included in the transportation network for each component, the shipping address group consisting of the selected shipping addresses may include a combination of M1×M2×M3 shipping addresses. However, the present disclosure is not limited to this, and M1, M2, and M3 may be natural numbers greater than or equal to 2, and the number of shipping addresses selected for each component may be two or more.

[0049] In operation S22, a shipping address group may be associated with each wind farm address. Since each component must be shipped to each wind farm address, it is necessary to associate a shipping address group with each wind farm address. In an exemplary embodiment of the present disclosure, the above-mentioned combination of M1×M2×M3 shipping addresses may be associated with N wind farm addresses to form M1×M2×M3×N combinations of shipping addresses to wind farm addresses.

[0050] In operation S23, a transshipment port and a transportation route may be selected between the shipping address group and the associated wind farm address. For example, a transshipment port between the shipping address group and the associated wind farm address may be selected in the transportation network, and a transportation route associated with the transshipment port may be selected.

[0051] In operation S24, a plurality of chromosome individuals are generated, each chromosome individual corresponds to a set of feasible transportation paths. Then, the chromosome individuals can be further screened.

[0052] In operation S25, it may be determined whether the gene segments of the individual chromosomes all satisfy the constraint conditions. For example, it may be determined whether the gene segments of the individual chromosomes all satisfy the constraint conditions based on the data on the constraint conditions as described above.

[0053] In an exemplary embodiment of the present disclosure, the constraint condition may include a transportation time that does not exceed a predetermined transportation time, and a ship transportation capacity that does not exceed a ship transportation capacity constraint condition of a corresponding node.

[0054] If it is determined in operation S25 that the chromosome individual includes a gene segment that does not satisfy the constraint condition, operation S26 is performed. In operation S26, the gene segment that does not satisfy the constraint condition may be eliminated. For example, the transportation time of the feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path may be calculated, and the gene segment whose transportation time exceeds the predetermined transportation time and / or the gene segment whose ship transportation capacity exceeds the ship transportation capacity constraint condition of the corresponding node may be eliminated.

[0055] After the gene segments that do not meet the constraint conditions are eliminated, the process may return to operation S24 to regenerate chromosome individuals until chromosome individuals without the eliminated gene segments are generated.

[0056] If it is determined in operation S25 that the gene segments of the chromosome individuals all meet the constraint conditions, operation S27 is performed. In operation S27, the chromosome individuals whose gene segments have not been removed can be used as the initial population, that is, the initial population is generated.

[0057] In another exemplary embodiment of the present disclosure, operations S25 to S27 may be omitted.

[0058] Return to reference Figure 1 In operation S13, the fitness value of each chromosome individual in the initial population is calculated according to the fitness function. According to an exemplary embodiment of the present disclosure, the fitness function may be the following function:

[0059]

[0060] Among them, Z is the fitness value of the feasible transport path corresponding to the chromosome individual, C l is the land transportation cost of the feasible transportation path corresponding to the chromosome individual; C o is the ocean transportation cost of the feasible transportation path corresponding to the chromosome individual; C t is the loading and unloading cost of the feasible transportation path corresponding to the chromosome individual; C s is the storage cost of the feasible transportation path corresponding to the chromosome individual; C to is the delay penalty cost of the feasible transportation path corresponding to the chromosome individual; C z is the demurrage penalty cost of the feasible transportation path corresponding to the chromosome individual; f1 is a parameter set to ensure the ship transportation capacity. If the ship transportation capacity meets the ship transportation capacity constraint, f1 is equal to 1; if the ship transportation capacity does not meet the ship transportation capacity constraint, f1 is equal to 0.

[0061] For example, the transportation cost C of the land transport segment included in the feasible transportation path can be calculated based on the land transport data input into the transportation network. lThe transportation cost C of the sea transportation segment included in the feasible transportation path can be calculated based on the sea transportation data input into the transportation network o The total loading and unloading cost C of the feasible transportation path can be calculated based on the loading and unloading cost data at the shipping address, wind farm address and / or transshipment port address input into the transportation network. t The total storage cost C of the feasible transportation path can be calculated based on the storage cost data at the shipping address, wind farm address and / or transit port address input into the transportation network. s The total delay penalty cost C of the feasible transportation path can be calculated based on the delay penalty cost data at the shipping address, wind farm address and / or transit port address input into the transportation network. to The total demurrage penalty cost C of the feasible transportation path can be calculated based on the demurrage penalty cost data at the shipping address, wind farm address and / or transshipment port address input into the transportation network. z .

[0062] According to an exemplary embodiment of the present disclosure, the ship transport capacity constraint condition may include at least one of the following conditions: ship size ≥ 1.2 × unit size, ship load ≥ 1.2 × unit component weight + fixture weight, minimum water depth of the sea area ≥ ship draft, ship size ≥ the size of ships that can be moored in the port, speed conditions meet the predetermined speed range, the dynamic response of the ship meets the meteorological and hydrological requirements of the sea area, and the ship leasing method and the expenses incurred meet the economic requirements. In an exemplary embodiment of the present disclosure, the unit size may be the overall size of the wind turbine generator set.

[0063] The above fitness function is only used as an example to comprehensively measure various costs and constraints affected by various factors, but the present disclosure is not limited to this. The fitness function can also be set according to other variable factors to model and digitize the wind turbine transportation process affected by complex variable factors.

[0064] In operation S14, a chromosome individual whose fitness value is greater than a predetermined threshold value is selected from the initial population. For example, the predetermined threshold value can be set according to the overall transportation cost budget value of the wind turbine generator set, so that the overall transportation cost of the feasible transportation path corresponding to the chromosome individual selected from the initial population does not exceed the budget value.

[0065] In operation S15, a crossover operation and a mutation operation are performed.

[0066] In an exemplary embodiment of the present disclosure, partial gene segments of two paired chromosome individuals may be mutually replaced and recombined to form two new chromosome individuals, that is, a crossover operation may be performed using a crossover operator. Specifically, a crossover operator such as a single-point crossover, a two-point crossover, a multi-point crossover, a uniform crossover, and an arithmetic crossover may be used to perform the crossover operation.

[0067] After the crossover operation is completed, a mutation operation can be performed. In an exemplary embodiment of the present disclosure, the gene values ​​on certain loci in the chromosome individual coding string can be replaced with other alleles on the loci to form a new individual. Specifically, the mutation operation can be performed using mutation operators such as basic bit mutation, uniform mutation, boundary mutation, non-uniform mutation, Gaussian approximate mutation, etc.

[0068] In operation S16, it can be determined whether to stop iteration according to whether the number of iterations reaches the maximum evolutionary generation or the set convergence standard. In an exemplary embodiment of the present disclosure, the set convergence standard can be that the fitness value of the chromosome individual with the maximum fitness value no longer increases or the fitness values ​​of the chromosome population no longer increase.

[0069] If the number of iterations does not reach the maximum evolutionary generation or the set convergence standard, the process returns to operation S14 and continues to execute until the number of iterations reaches the maximum evolutionary generation or the set convergence standard.

[0070] If it is determined in operation S16 that the number of iterations reaches the maximum evolutionary generation or the set convergence standard, operation S17 is performed. In operation S17, the chromosome individual with the maximum fitness value may be output as the optimal solution.

[0071] In an exemplary embodiment of the present disclosure, the fitness value of each chromosome individual may be calculated according to the fitness function, and the fitness values ​​of all chromosome individuals may be compared to screen out the chromosome individual with the maximum fitness value.

[0072] In operation S18, chromosome decoding is performed on the optimal solution to generate an optimized arrangement plan for the transportation path of the wind turbine generator set. In an exemplary embodiment of the present disclosure, chromosome decoding may be performed according to a decoding method corresponding to the chromosome encoding method.

[0073] In an exemplary embodiment of the present disclosure, since the larger the fitness value, the lower the overall transportation cost of the corresponding feasible transportation path, the feasible transportation path obtained by chromosome decoding of the optimal solution can be used as an optimization solution for the layout of the wind turbine transportation path.

[0074] The above has been combined Figure 1 and Figure 2 The method for optimizing the arrangement of the transport path of a wind turbine generator set according to an exemplary embodiment of the present disclosure is described. Figure 3 A device for optimizing the arrangement of a transport path of a wind turbine generator set and units thereof according to an exemplary embodiment of the present disclosure are described.

[0075] Figure 3Schematic diagram of the arrangement optimization device 3 for the transport path of a wind turbine generator set according to an exemplary embodiment of the present disclosure. It should be understood that the arrangement optimization device 3 and the specific processing performed by each unit according to the exemplary embodiment of the present invention have been referred to. Figure 1 and Figure 2 The details have been described in detail and will not be repeated here.

[0076] The arrangement optimization device 3 may include a transportation network initialization unit 31 , a chromosome encoding unit 32 , a fitness value calculation unit 33 , a crossover and mutation unit 34 , an optimal solution output unit 35 and a chromosome decoding unit 36 ​​.

[0077] The transportation network initialization unit 31 may be configured to initialize the transportation network of the wind turbine generator set according to the transportation process nodes of various components of the wind turbine generator set, wherein the transportation process nodes include the shipping addresses, wind farm addresses and transshipment port addresses of various components.

[0078] In an exemplary embodiment of the present disclosure, the transportation network initialization unit 31 can be configured to: obtain data associated with the transportation process node, the data associated with the transportation process node including ship data, meteorological data, hydrological data, port data, route data, shipping place data, wind farm data and at least one of data about constraints; input the data associated with the transportation process node into the transportation network.

[0079] In an exemplary embodiment of the present disclosure, a chromosome individual may include gene segments representing the following information:

[0080] The shipping address, wind farm address, and total volume of a single shipment of various components of a wind turbine generator set; and the following items: the transportation route from the shipping place to the wind farm; and / or the address of the transshipment port, the transportation route from the shipping place to the transshipment port, and the transportation route from the transshipment port to the wind farm; and / or the address of the transshipment port, the transportation route from the shipping place to the transshipment port, the transportation route from the transshipment port to the wind farm, and the transportation routes between multiple transshipment ports.

[0081] The chromosome encoding unit 32 may be configured to generate a plurality of chromosome individuals as an initial population according to the transportation network through chromosome encoding, each chromosome individual including a feasible transportation path for transporting each component of the wind turbine generator set from the shipping address to the wind farm address.

[0082] In an exemplary embodiment of the present disclosure, the chromosome encoding unit 32 can be configured to select the shipping address of each component to form a shipping address group; associate the shipping address group with each wind farm address; select a transit port and a transportation route between the shipping address group and the associated wind farm address; and generate multiple chromosome individuals.

[0083] In an exemplary embodiment of the present disclosure, the chromosome encoding unit 32 may be configured to remove gene segments that do not meet the constraints, and regenerate chromosome individuals until chromosome individuals with gene segments that have not been removed are generated; and the chromosome individuals with gene segments that have not been removed are used as the initial population.

[0084] In an exemplary embodiment of the present disclosure, the constraint condition may include that the transportation time does not exceed the predetermined transportation time, and the ship transportation capacity does not exceed the ship transportation capacity constraint condition of the corresponding node. The chromosome encoding unit 32 may be configured to: calculate the transportation time of the feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path; and remove the gene segment whose transportation time exceeds the predetermined transportation time and / or the gene segment whose ship transportation capacity exceeds the ship transportation capacity constraint condition of the corresponding node.

[0085] The fitness value calculation unit 33 may be configured to calculate the fitness value of each chromosome individual in the initial population according to the fitness function. In an exemplary embodiment of the present disclosure, the fitness function may be equation (1) as described above.

[0086] In an exemplary embodiment of the present disclosure, the ship transport capacity constraint conditions may include at least one of the following conditions: ship size ≥ 1.2× unit size, ship load ≥ 1.2× unit component weight + fixture weight, minimum water depth of the sea area ≥ ship draft, ship size ≥ the size of ships that can be berthed in the port, speed conditions meet the predetermined speed range, the dynamic response of the ship meets the meteorological and hydrological requirements of the sea area to which it belongs, and the ship leasing method and the costs incurred meet the economic requirements.

[0087] The crossover and mutation unit 34 may be configured to select chromosome individuals in the initial population whose fitness values ​​are greater than a predetermined threshold to perform crossover and mutation operations until the number of iterations reaches a maximum evolutionary generation or a set convergence criterion.

[0088] The optimal solution output unit 35 may be configured to output the chromosome individual having the maximum fitness value as the optimal solution.

[0089] The chromosome decoding unit 36 ​​may be configured to perform chromosome decoding on the optimal solution to generate an optimized arrangement solution for the transportation path of the wind turbine generator set.

[0090] Please refer to the above Figure 1 and Figure 2 The specific details of the corresponding processing performed by the arrangement optimization device 3 and each of its units can be understood through the description, which will not be repeated here.

[0091] In addition, according to an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed, the method for optimizing the arrangement of the wind turbine transport path according to the exemplary embodiment of the present disclosure is implemented.

[0092] In an exemplary embodiment of the present disclosure, the computer-readable storage medium may carry one or more programs, and when the computer program is executed, the following steps may be implemented: according to the transportation process nodes of various components of the wind turbine, the transportation network of the wind turbine is initialized, and the transportation process nodes include the shipping address, wind farm address and transshipment port address of various components; according to the transportation network, a plurality of chromosome individuals are generated as the initial population through chromosome encoding, and each chromosome individual includes a feasible transportation path for transporting each component of the wind turbine from the shipping address to the wind farm address; according to the fitness function, the fitness value of each chromosome individual in the initial population is calculated; the chromosome individuals with fitness values ​​greater than a predetermined threshold in the initial population are selected for crossover and mutation operations until the number of iterations reaches the maximum evolutionary generation or the set convergence standard; the chromosome individual with the maximum fitness value is output as the optimal solution; the optimal solution is chromosome decoded to generate an optimized arrangement scheme for the transportation path of the wind turbine.

[0093] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In an embodiment of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a computer program that may be used by or in conjunction with an instruction execution system, device or device. The computer program contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof. The computer-readable storage medium may be contained in any device; it may also exist alone without being assembled into the device.

[0094] The above has been combined Figure 3 The arrangement optimization device of the wind turbine generator set transportation path according to the exemplary embodiment of the present disclosure is described. Figure 4 A computing device according to an exemplary embodiment of the present disclosure is described.

[0095] Figure 4 is a schematic diagram of a computing device according to an exemplary embodiment of the present disclosure.

[0096] Reference Figure 4 According to the exemplary embodiment of the present disclosure, the computing device 4 may include a memory 41 and a processor 42. A computer program 43 is stored in the memory 41. When the computer program 43 is executed by the processor 42, the method for optimizing the arrangement of the wind turbine transport path according to the exemplary embodiment of the present disclosure is implemented.

[0097] In an exemplary embodiment of the present disclosure, when the computer program 43 is executed by the processor 42, reference to Figure 1 The steps of the described method are as follows: according to the transportation process nodes of various components of the wind turbine generator set, the transportation process nodes include the shipping address, wind farm address and transit port address of various components; according to the transportation network, multiple chromosome individuals are generated as the initial population through chromosome encoding, and each chromosome individual includes a feasible transportation path for transporting each component of the wind turbine generator set from the shipping address to the wind farm address; according to the fitness function, the fitness value of each chromosome individual in the initial population is calculated; the chromosome individuals with fitness values ​​greater than a predetermined threshold in the initial population are selected for crossover operation and mutation operation until the number of iterations reaches the maximum evolutionary generation or the set convergence standard; the chromosome individual with the maximum fitness value is output as the optimal solution; the optimal solution is subjected to chromosome decoding to generate an optimized arrangement scheme for the transportation path of the wind turbine generator set.

[0098] Figure 4 The computing device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0099] The above has been referred to Figures 1 to 3 The arrangement optimization method and arrangement optimization device of the wind turbine transport path according to the exemplary embodiment of the present disclosure are described. However, it should be understood that: Figure 3 The arrangement optimization device for the transport path of wind turbine generators and its units shown in the figure can be respectively configured as software, hardware, firmware or any combination of the above items to perform specific functions. Figure 4 The computing device shown in is not limited to including the components shown above, but some components may be added or deleted as needed, and the above components may also be combined.

[0100] The use of the wind turbine transport path layout optimization method, layout optimization device, computer-readable storage medium and computing device according to the exemplary embodiment of the present invention can at least provide scientific decision-making guidance for transportation strategies and plans; comprehensively consider various factors that affect the wind turbine transport path layout planning, model and digitize the wind turbine transport process affected by complex variable factors, save labor costs, improve the accuracy and precision of the transport path layout planning, and provide a transportation strategy that is more in line with engineering practice; consider all aspects of wind turbine transportation, and realize the optimization of wind turbine transport path layout or transportation strategy based on the genetic algorithm model.

[0101] The control logic or function performed by each component or controller in the control system can be represented by a flow chart or similar diagram in one or more figures. These figures provide representative control strategies and / or logic, which can be implemented using one or more processing strategies (such as event-driven, interrupt-driven, multi-tasking, multi-threading, etc.). Therefore, the various steps or functions shown can be executed in the order shown, executed in parallel, or omitted in some cases. Although not always clearly shown, it will be appreciated by those of ordinary skill in the art that one or more steps or functions shown can be repeatedly executed according to the specific processing strategy used.

[0102] While the invention has been shown and described with reference to the preferred embodiments, it will be understood by those skilled in the art that various modifications and variations may be made to these embodiments without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A method for optimizing the layout of offshore wind turbine transport routes. It is characterized in that The method comprises: Initializing a transportation network of the offshore wind turbine generator set according to the transportation process nodes of various components of the offshore wind turbine generator set, wherein the transportation process nodes include the shipping address, the wind farm address and the transit port address of various components; According to the transportation network, a plurality of chromosome individuals are generated as an initial population through chromosome encoding, each chromosome individual includes a feasible transportation path for transporting each component of the offshore wind turbine generator set from the shipping address to the wind farm address; According to the fitness function, calculate the fitness value of each chromosome individual in the initial population; Selecting chromosome individuals with fitness values ​​greater than a predetermined threshold in the initial population to perform crossover and mutation operations until the number of iterations reaches the maximum evolutionary generation or the set convergence standard; Output the chromosome individual with the maximum fitness value as the optimal solution; The chromosome decoding of the optimal solution is performed to generate an optimized arrangement plan for the transportation path of offshore wind turbines. Wherein, the fitness function is: Among them, Z is the fitness value of the feasible transport path corresponding to the chromosome individual, C l is the land transportation cost of the feasible transportation path corresponding to the chromosome individual, C o is the ocean transportation cost of the feasible transportation path corresponding to the chromosome individual, C t is the loading and unloading cost of the feasible transportation path corresponding to the chromosome individual, C s is the storage cost of the feasible transportation path corresponding to the chromosome individual, C to is the delay penalty cost of the feasible transportation path corresponding to the chromosome individual, C z is the demurrage penalty cost of the feasible transportation path corresponding to the chromosome individual, f 1 is a parameter set to ensure the ship transport capacity. If the ship transport capacity meets the ship transport capacity constraint, then f 1 is equal to 1. If the ship transport capacity does not meet the ship transport capacity constraint, then f 1 is equal to 0, Among them, the number of transfers during the transportation of various components of offshore wind turbines is limited to no more than one transfer. The step of initializing the transportation network of the offshore wind turbine generator set according to the transportation process nodes of various components of the offshore wind turbine generator set includes: Acquire data associated with the transport process node, wherein the data associated with the transport process node includes at least one of ship data, meteorological data, hydrological data, port data, route data, shipping place data, wind farm data, and data on constraint conditions; inputting data associated with the transport process node into the transport network, The individual chromosome includes gene segments representing the following information: The shipping address of various components of offshore wind turbines, the address of the wind farm, the total volume of a single shipment, and the following items: The transportation route from the place of shipment to the wind farm; or The address of the transshipment port, the transportation route from the place of shipment to the transshipment port, and the transportation route from the transshipment port to the wind farm.

2. The method according to claim 1, It is characterized in that According to the transport network, the steps of generating a plurality of chromosome individuals as an initial population through chromosome encoding include: Select the shipping address for each component to form a shipping address group; Associating a shipping address group with each wind farm address; Selecting a transit port and a transportation route between the shipping address group and the associated wind farm address; Generate multiple chromosome individuals.

3. The method according to claim 2, It is characterized in that According to the transport network, the step of generating a plurality of chromosome individuals as the initial population through chromosome encoding also includes: Eliminate the gene segments that do not meet the constraints, and regenerate chromosome individuals until a chromosome individual with gene segments that have not been eliminated is generated; The chromosome individuals whose gene segments have not been deleted are taken as the initial population.

4. The method according to claim 3, It is characterized in that The steps to remove gene segments that do not meet the constraints include: Calculate the transportation time of the feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path; Eliminate the gene segments whose transportation time exceeds the scheduled transportation time and / or the gene segments whose ship transportation capacity exceeds the ship transportation capacity constraint of the corresponding node.

5. The method according to claim 1, It is characterized in that The ship transport capacity constraint condition includes at least one of the following conditions: The ship size is ≥ 1.2×the size of the unit, the ship load is ≥ 1.2×the weight of the unit components + the weight of the fixture, the minimum water depth of the sea area is ≥ the draft of the ship, the ship size is ≥ the size of the ship that can be berthed in the port, the speed conditions meet the predetermined speed range, the dynamic response of the ship meets the meteorological and hydrological requirements of the sea area, and the ship leasing method and the expenses incurred meet the economic requirements.

6. A device for optimizing the layout of offshore wind turbine transport routes, It is characterized in that The arrangement optimization device comprises: A transport network initialization unit is configured to initialize the transport network of the offshore wind turbine generator set according to transport process nodes of various components of the offshore wind turbine generator set, wherein the transport process nodes include the shipping addresses, wind farm addresses and transshipment port addresses of various components; A chromosome encoding unit is configured to generate a plurality of chromosome individuals as an initial population according to the transportation network through chromosome encoding, each chromosome individual including a feasible transportation path for transporting each component of the offshore wind turbine generator set from a shipping address to a wind farm address; A fitness value calculation unit is configured to calculate the fitness value of each chromosome individual in the initial population according to the fitness function; A crossover and mutation unit is configured to select chromosome individuals with fitness values ​​greater than a predetermined threshold in the initial population to perform crossover operations and mutation operations until the number of iterations reaches a maximum evolutionary generation or a set convergence standard; The optimal solution output unit is configured to output the chromosome individual with the maximum fitness value as the optimal solution; The chromosome decoding unit is configured to perform chromosome decoding on the optimal solution to generate an optimized arrangement scheme for the transportation path of the offshore wind turbine generator set. Wherein, the fitness function is: Among them, Z is the fitness value of the feasible transport path corresponding to the chromosome individual, C l is the land transportation cost of the feasible transportation path corresponding to the chromosome individual, C o is the ocean transportation cost of the feasible transportation path corresponding to the chromosome individual, C t is the loading and unloading cost of the feasible transportation path corresponding to the chromosome individual, C s is the storage cost of the feasible transportation path corresponding to the chromosome individual, C to is the delay penalty cost of the feasible transportation path corresponding to the chromosome individual, C z is the demurrage penalty cost of the feasible transportation path corresponding to the chromosome individual, f 1 is a parameter set to ensure the ship transport capacity. If the ship transport capacity meets the ship transport capacity constraint, then f 1 is equal to 1. If the ship transport capacity does not meet the ship transport capacity constraint, then f 1 is equal to 0, Among them, the number of transfers during the transportation of various components of offshore wind turbines is limited to no more than one transfer. Wherein, the transportation network initialization unit is specifically configured as follows: Acquire data associated with the transport process node, wherein the data associated with the transport process node includes at least one of ship data, meteorological data, hydrological data, port data, route data, shipping place data, wind farm data, and data on constraint conditions; inputting data associated with the transport process node into the transport network, The individual chromosome includes gene segments representing the following information: The shipping address of various components of offshore wind turbines, the address of the wind farm, the total volume of a single shipment; and the following: The transportation route from the place of shipment to the wind farm; or The address of the transshipment port, the transportation route from the place of shipment to the transshipment port, and the transportation route from the transshipment port to the wind farm.

7. The arrangement optimization device according to claim 6, It is characterized in that The chromosome encoding unit is further configured as: Select the shipping address for each component to form a shipping address group; Associating a shipping address group with each wind farm address; Selecting a transit port and a transportation route between the shipping address group and the associated wind farm address; Generate multiple chromosome individuals.

8. The arrangement optimization device according to claim 7, It is characterized in that The chromosome encoding unit is further configured as: Eliminate the gene segments that do not meet the constraints, and regenerate chromosome individuals until a chromosome individual with gene segments that have not been eliminated is generated; The chromosome individuals whose gene segments have not been deleted are taken as the initial population.

9. The arrangement optimization device according to claim 8, It is characterized in that The chromosome encoding unit is further configured as: Calculate the transportation time of the feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path; Eliminate the gene segments whose transportation time exceeds the scheduled transportation time and / or the gene segments whose ship transportation capacity exceeds the ship transportation capacity constraint of the corresponding node.

10. The arrangement optimization device according to claim 6, It is characterized in that The ship transport capacity constraint condition includes at least one of the following conditions: The ship size is ≥ 1.2×the size of the unit, the ship load is ≥ 1.2×the weight of the unit components + the weight of the fixture, the minimum water depth of the sea area is ≥ the draft of the ship, the ship size is ≥ the size of the ship that can be berthed in the port, the speed conditions meet the predetermined speed range, the dynamic response of the ship meets the meteorological and hydrological requirements of the sea area, and the ship leasing method and the expenses incurred meet the economic requirements.

11. A computer-readable storage medium storing a computer program, It is characterized in that When the computer program is executed by a processor, the method for optimizing the arrangement of the transport path of offshore wind turbine generator sets as claimed in any one of claims 1 to 5 is implemented.

12. A computing device, include: processor; A memory storing a computer program, which, when executed by a processor, implements the method for optimizing the arrangement of the transport path of offshore wind turbine generator sets as claimed in any one of claims 1 to 5.

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