CN114077911A - Method and device for optimizing arrangement of transport paths of wind generating set - Google Patents
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Abstract
The disclosure provides a method and a device for optimizing arrangement of transportation paths of a wind generating set. The method comprises the following steps: initializing a transportation network according to transportation process nodes of various components, wherein the transportation process nodes comprise shipping addresses of the various components, addresses of wind power plants and addresses of transfer ports; generating a plurality of chromosome individuals as an initial population according to a transportation network through chromosome coding, wherein each chromosome individual comprises a feasible transportation path for transporting each component from a delivery address to a wind power plant address; calculating the fitness value of each chromosome individual in the initial population according to the fitness function; selecting chromosome individuals with fitness values larger than a preset threshold value in the initial population to perform cross operation and mutation operation until the iteration times reach the maximum evolution algebra or a set convergence standard; outputting the chromosome individual with the maximum fitness value as an optimal solution; and carrying out chromosome decoding on the optimal solution to generate an arrangement optimization scheme.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a method and a device for optimizing the arrangement of transportation paths of a wind generating set.
Background
On the one hand, with the explosion of wind power generation projects, there are multiple wind power generation projects being started at the same time, and one factory producing wind power generation unit components is generally required to supply multiple wind power generation projects.
On the other hand, components (e.g., blades, generators, etc.) of a wind turbine generator set are typically produced by factories in different areas, and there is a situation where the same transport vessel transports a variety of components. Meanwhile, the transportation of components of the offshore wind power generation project relates to a plurality of links such as land transportation, port transfer, offshore transportation and the like, and each link is influenced by a plurality of factors such as cost factors, meteorological factors and ship resource factors.
Disclosure of Invention
An object of an exemplary embodiment of the present invention is to provide a method and an apparatus for optimizing arrangement of a transportation path of a wind turbine generator system, so as to overcome the disadvantages in the prior art.
According to an exemplary embodiment of the invention, a method for optimizing the layout of a wind turbine generator system transport path is provided, the method comprising: initializing a transportation network of the wind generating set according to transportation process nodes of various components of the wind generating set, wherein the transportation process nodes comprise delivery addresses of the various components, wind power plant addresses and transit port addresses; generating a plurality of chromosome individuals as an initial population according to the transportation network through chromosome coding, wherein each chromosome individual comprises a feasible transportation path for transporting each component of the wind generating set from a delivery address to a wind power plant address; calculating the fitness value of each chromosome individual in the initial population according to the fitness function; selecting chromosome individuals with fitness values larger than a preset threshold value in the initial population to perform cross operation and mutation operation until the iteration times reach the maximum evolution algebra or a set convergence standard; outputting the chromosome individual with the maximum fitness value as an optimal solution; and carrying out chromosome decoding on the optimal solution to generate an arrangement optimization scheme of the transport path of the wind generating set.
Optionally, the step of initializing the transport network of the wind park according to the transport process nodes of the various components of the wind park comprises: acquiring data associated with the transport process node, the data associated with the transport process node including at least one of ship data, meteorological data, hydrological data, port data, airline data, ship location data, wind farm data, and data regarding constraints; inputting data associated with the transportation process node into the transportation network.
Optionally, the chromosomal individual comprises gene segments representing the following information: shipping addresses of various components of the wind turbine generator set, wind farm addresses, total volume of single shipments, and the following: a transportation route from a delivery location to a wind farm; and/or, a port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm; and/or a port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm, a transport route between a plurality of ports of transfer.
Optionally, the step of generating a plurality of chromosome individuals as the initial population via chromosome coding according to the transportation network comprises: selecting a delivery address of each component to form a delivery address group; associating a shipping address set with each wind farm address; selecting a port of transfer and a haul route between the shipping address set and the associated wind farm address; a plurality of chromosome individuals are generated.
Optionally, the step of generating a plurality of chromosome individuals as the initial population by chromosome coding according to the transportation network further comprises: eliminating gene segments which do not satisfy the constraint condition, and regenerating chromosome individuals until chromosome individuals without the eliminated gene segments are generated; chromosome individuals without the knocked out gene segments are used as an initial population.
Optionally, the step of rejecting gene segments that do not satisfy the constraint comprises: calculating the transportation time of a feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path; and eliminating the gene segments with the transport time exceeding the preset transport time and/or the gene segments with the transport capacity exceeding the ship transport capacity constraint condition of the corresponding nodes.
Optionally, the fitness function is:
wherein Z is the fitness value of the feasible transportation path corresponding to the chromosome individual, ClCost of land transportation for feasible transportation path corresponding to chromosome individual, CoShipping costs for feasible transportation routes corresponding to individual chromosomes CtLoading and unloading costs for feasible transport paths for individual chromosomes CsStockpiling costs of feasible transportation routes for individual chromosomes CtoDeferred penalty cost for feasible transportation path corresponding to chromosome individual, CzLag penalty cost, f, for feasible transportation path corresponding to chromosome individual1Is a parameter set for ensuring the shipping capacity, if the shipping capacity satisfies the shipping capacity constraint condition, f1Equal to 1, if the shipping capacity does not satisfy the shipping capacity constraint, f1Equal to 0.
Optionally, the vessel transport capacity constraint comprises at least one of the following conditions: the ship size is more than or equal to 1.2 times of the unit size, the ship load is more than or equal to 1.2 times of the unit component weight plus the clamp weight, the minimum water depth of the sea area is more than or equal to the ship draught, the ship size is more than or equal to the size of a ship which can be parked at a port, the speed condition meets the preset speed range, the dynamic response of the ship meets the meteorological hydrological requirements of the sea area, and the ship leasing mode and the generated cost meet the economic requirements.
According to an exemplary embodiment of the present invention, there is provided an arrangement optimizing device for a wind turbine generator system transportation path, the arrangement optimizing device including: a transportation network initialization unit configured to initialize a transportation network of the wind turbine generator set according to transportation process nodes of various components of the wind turbine generator set, wherein the transportation process nodes comprise shipping addresses, wind farm addresses and transfer port addresses of the various components; a chromosome coding unit configured to generate a plurality of chromosome individuals as an initial population through chromosome coding according to the transportation network, each chromosome individual including one feasible transportation path for transporting each component of the wind turbine generator system from a shipping address to a wind farm address; a fitness value calculation unit configured to calculate a fitness value of each chromosome individual in the initial population according to a fitness function; the crossing and mutation unit is configured to select chromosome individuals with fitness values larger than a preset threshold value in the initial population to carry out crossing operation and mutation operation until the iteration number reaches the maximum evolution algebra or a set convergence standard; an optimal solution output unit configured to output the chromosome individual having the largest fitness value as an optimal solution; and the chromosome decoding unit is configured to perform chromosome decoding on the optimal solution so as to generate an arrangement optimization scheme of the transportation path of the wind generating set.
Optionally, the transport network initialization unit is further configured to: acquiring data associated with the transport process node, the data associated with the transport process node including at least one of ship data, meteorological data, hydrological data, port data, airline data, ship location data, wind farm data, and data regarding constraints; inputting data associated with the transportation process node into the transportation network.
Optionally, the chromosomal individual comprises gene segments representing the following information: the shipping address, the wind power plant address and the total transportation volume of single transportation of various components of the wind generating set; and the following: a transportation route from a delivery location to a wind farm; and/or, a port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm; and/or a port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm, a transport route between a plurality of ports of transfer.
Optionally, the chromosome coding unit is further configured to: selecting a delivery address of each component to form a delivery address group; associating a shipping address set with each wind farm address; selecting a port of transfer and a haul route between the shipping address set and the associated wind farm address; a plurality of chromosome individuals are generated.
Optionally, the chromosome coding unit is further configured to: eliminating gene segments which do not satisfy the constraint condition, and regenerating chromosome individuals until chromosome individuals without the eliminated gene segments are generated; chromosome individuals without the knocked out gene segments are used as an initial population.
Optionally, the chromosome coding unit is further configured to: calculating the transportation time of a feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path; and eliminating the gene segments with the transport time exceeding the preset transport time and/or the gene segments with the transport capacity exceeding the ship transport capacity constraint condition of the corresponding nodes.
Optionally, the fitness function is:
wherein Z is the fitness value of the feasible transportation path corresponding to the chromosome individual, ClCost of land transportation for feasible transportation path corresponding to chromosome individual, CoShipping costs for feasible transportation routes corresponding to individual chromosomes CtLoading and unloading costs for feasible transport paths for individual chromosomes CsStockpiling costs of feasible transportation routes for individual chromosomes CtoDeferred penalty cost for feasible transportation path corresponding to chromosome individual, CzLag penalty cost, f, for feasible transportation path corresponding to chromosome individual1Is a parameter set for ensuring the shipping capacity, if the shipping capacity satisfies the shipping capacity constraint condition, f1Equal to 1 if the shipping capacity does not satisfy the shipping capacity constraintCondition, then f1Equal to 0.
Optionally, the vessel transport capacity constraint comprises at least one of the following conditions: the ship size is more than or equal to 1.2 times of the unit size, the ship load is more than or equal to 1.2 times of the unit component weight plus the clamp weight, the minimum water depth of the sea area is more than or equal to the ship draught, the ship size is more than or equal to the size of a ship which can be parked at a port, the speed condition meets the preset speed range, the dynamic response of the ship meets the meteorological hydrological requirements of the sea area, and the ship leasing mode and the generated cost meet the economic requirements.
According to an exemplary embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, carries out the method for optimizing an arrangement of a transport path of a wind park as described above.
According to an exemplary embodiment of the present disclosure, there is provided a computing device including: a processor; a memory storing a computer program which, when executed by the processor, implements the method for optimizing the layout of a transport path of a wind turbine generator set as described above.
By adopting the arrangement optimization method, the device and the storage medium of the transport path of the wind generating set according to the exemplary embodiment of the invention, scientific decision guidance can be provided for transport strategies and plans; the method has the advantages that various factors influencing the arrangement planning of the transport path of the wind generating set are comprehensively considered, the transport flow of the wind generating set influenced by complex variable factors is modeled and digitized, the labor cost is saved, the accuracy and precision of the arrangement planning of the transport path are improved, and a transport strategy more conforming to engineering practice is provided; and considering all links of wind generating set transportation, and realizing the configuration optimization of the transportation path of the wind generating set and the optimization of the transportation strategy based on a genetic algorithm.
Drawings
The above and other objects and features of the present invention will become more apparent from the following description when taken in conjunction with the accompanying drawings.
Fig. 1 is a flowchart of a method for optimizing the layout of a wind park transport path according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart of a process of generating an initial population in a layout optimization method according to an exemplary embodiment of the present disclosure.
Fig. 3 is a schematic view of an arrangement optimizing device of a wind turbine generator set transportation path according to an exemplary embodiment of the present disclosure.
Fig. 4 is a schematic diagram of a computing device according to an example embodiment of the present disclosure.
Detailed Description
Today, wind power generation is developed vigorously, theories are summarized by combining practices, and the theories are applied to the practices and are proved by the practices to be necessary requirements of the spanning development of the wind power generation. With the vigorous development of wind power generation projects, the construction of a plurality of wind power generation projects can be carried out at the same time, so that the problem that one factory for producing wind power generation unit components is required to supply a plurality of wind power generation projects occurs.
On the other hand, components of a wind turbine generator system (e.g., blades, generators, towers, etc.) are typically produced by different regional factories, and there are situations where the same transport vessel transports multiple components. For example, the transportation of components in an offshore wind power generation project involves a plurality of links such as land transportation, port transportation, marine transportation and the like, and each link is influenced by a plurality of factors such as cost factors, meteorological factors, ship resource factors and the like.
With the large-scale development of wind power generation projects, the factors needing to be analyzed in a transportation plan are exponentially increased, and the problem that how to synthesize the ship and capacity condition control principle and consider external influence factors reflects the artificial judgment principle and arranges the optimal transportation strategy of the wind power generation projects becomes the current urgent need to be solved. The existing wind generating set transportation path planning is still in a stage of manual judgment according to experience and related data, along with the increase of wind power generation construction projects and the increase of transportation distance, the calculation amount is bound to be exponentially increased, and the arrangement optimization of the wind generating set transportation path is difficult to obtain through a manual technology.
The invention provides an arrangement optimization method and an arrangement optimization device for a wind generating set transportation path, which can utilize a genetic algorithm to generate an arrangement optimization scheme for the wind generating set transportation path, and the arrangement optimization scheme for the wind generating set transportation path can be used for transporting various components of the wind generating set from a delivery address to each wind power plant address so as to complete construction of each wind power plant project. The arrangement optimization method and the arrangement optimization device can comprehensively consider various factors influencing the arrangement planning of the transport path of the wind generating set based on the genetic algorithm, simultaneously save labor cost, improve the accuracy and precision of the arrangement planning of the transport path, and realize the arrangement optimization of the transport path of the wind generating set or the optimization of the transport strategy.
Exemplary embodiments of the present disclosure will now be described more fully with reference to the accompanying drawings. Examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. Although some exemplary embodiments are shown in the drawings, the present invention is not limited thereto.
Fig. 1 is a flowchart of a method for optimizing the layout of a wind park transport path according to an exemplary embodiment of the present disclosure.
As shown in FIG. 1, at operation S11, a transport network of the wind turbine generator set may be initialized based on transport flow nodes of various components of the wind turbine generator set. For example, the transport flow nodes may include, but are not limited to, shipping addresses, wind farm addresses, and port of transfer addresses for various components. The transportation network may include all combinations of all feasible transportation paths generated based on the transportation flow nodes of the various components.
Optionally, in operation S11, data associated with the transportation process node may also be acquired, and the data associated with the transportation process node may be input into the transportation network. The data associated with the transportation process node may include, but is not limited to, at least one of ship data, meteorological data, hydrological data, port data, airline data, ship from data, wind farm data, and data regarding constraints. According to an exemplary embodiment of the disclosure, the data associated with the transportation process node may be requested and obtained from a cloud server or a database through wired communication or wireless communication.
The ship data may include ship size, ship weight, ship dynamic response (e.g., pitch, roll, heave) associated with the transportation process node, ship lease duration (e.g., day lease, month lease, year lease) and their corresponding lease fees, etc. Weather data may include rainfall, typhoon paths and ratings, weather forecast data, etc. associated with the transportation process nodes. The hydrological data may include the water level of the sea area associated with the transportation process node, etc. Port data may include the number and size of ships that the port may berth, the throughput of the port, the cargo class of the port, the open time of the port, etc., associated with the transportation process node. The route data may include ship density, round trip time, total average ship loading and unloading quota, total ship flow, and cargo flow between two ports, etc. associated with the transportation process node. The delivery location data may include geographic coordinates of the delivery location, etc. The wind farm data may include wind farm geographical coordinates, and the like. The data regarding the constraints may include transit time limits for various components of the wind turbine during transport (e.g., a transit time limit is one time, i.e., at most one transit is allowed), transit time constraints associated with the transport flow nodes (e.g., a transit time constraint may include a land transit time constraint, a port operating time constraint, a sea transit time constraint, a total transit time constraint, etc.), capacity constraints for the vessel or vehicle (e.g., the vessel and vehicle are subject to meteorological conditions, hydrological conditions, port transfer capacity, etc.), capacity constraints for the vessel (e.g., the load of wind turbine components in a transport route must not exceed the capacity of the ship for the route), and the like.
In operation S12, a plurality of chromosome individuals, each of which may include a feasible transportation path for transporting each component of the wind turbine generator set from a shipping address to a wind farm address, is generated as an initial population through chromosome coding according to the transportation network. Each individual chromosome may correspond to a set of feasible transport paths for transporting each component of the wind turbine generator system from a shipping address to a wind farm address.
In the exemplary embodiment of the present disclosure, the chromosome coding may be performed using a coding method such as a binary coding method, a floating point coding method, a sign coding method, or the like.
According to an exemplary embodiment of the present disclosure, a chromosomal individual may include gene segments representing the following information: shipping addresses of various components of the wind turbine generator set, wind farm addresses, total volume of single shipments, and the following: a shipping route from the shipping location to the wind farm (i.e., the parts are shipped directly from the shipping location to the wind farm location without transit); and/or, a port of transfer address, a shipping route from a shipping location to a port of transfer, a shipping route from a port of transfer to a wind farm (i.e., the part is transferred once during shipping); and/or, a port of transfer address, a shipping route from a shipping location to a port of transfer, a shipping route from a port of transfer to a wind farm, a shipping route between multiple ports of transfer (i.e., a component is transferred multiple times during shipping). The total transport volume of the single transportation can represent the total number of the wind generating sets transported to the address of the wind power plant after the feasible transportation path corresponding to the chromosome individual is completely transported once.
The feasible transportation path corresponding to the chromosome individual can comprise various transportation routes, such as no transfer, one transfer and/or multiple transfers during transportation.
According to an exemplary embodiment of the present disclosure, the operation of generating a plurality of chromosome individuals as the initial population may include a plurality of operations. A process of generating an initial population according to an exemplary embodiment of the present disclosure is described herein with reference to fig. 2.
Fig. 2 is a flowchart of a process of generating an initial population in a layout optimization method according to an exemplary embodiment of the present disclosure.
In operation S21, a shipping address for each component of the wind turbine generator system may be selected to form a shipping address group. In an exemplary embodiment of the present disclosure, the transport network of the wind turbine generator set may include M1 shipping addresses for the blades, M2 shipping addresses for the nacelle generators, M3 shipping addresses for the towers, and N wind farm addresses. Therefore, M1 shipping addresses of the blades, M2 shipping addresses of the nacelle generator, and M3 shipping addresses of the tower can be selected to form a shipping address group of the wind turbine generator system. Wherein, M1, M2 and M3 are all natural numbers which are more than or equal to 1. For example, if only one shipping address is selected from among the shipping addresses included in the transport network for each component, the group of shipping addresses consisting of the selected shipping addresses may include combinations of M1M 2M 3 shipping addresses. However, the present disclosure is not limited thereto, and M1, M2, M3 may be a natural number greater than or equal to 2, and the number of shipping addresses selected for each component may be two or more.
In operation S22, a shipping address set may be associated with each wind farm address. Since each component is shipped to each wind farm address, a shipping address set needs to be associated with each wind farm address. In an exemplary embodiment of the present disclosure, combinations of M1 × M2 × M3 shipping addresses described above may be associated with N wind farm addresses, forming combinations of M1 × M2 × M3 × N shipping addresses to wind farm addresses.
At operation S23, a transit port and a haul route may be selected between the shipping address group and the associated wind farm address. For example, a transit port between the shipping address set and the associated wind farm address may be selected in the transport network, and a transport route associated with the transit port may be selected.
In operation S24, a plurality of chromosomal individuals is generated, each chromosomal individual corresponding to a set of feasible transportation paths. Then, the individual chromosome can be further screened.
In operation S25, it may be determined whether the gene segments of the individual chromosomes all satisfy the constraint condition. For example, whether or not the gene segments of the individual chromosomes all satisfy the constraint condition may be determined from the data on the constraint condition as described above.
In an exemplary embodiment of the present disclosure, the constraint condition may include a ship transport capacity constraint condition that a transport time does not exceed a predetermined transport time and a ship transport capacity does not exceed a corresponding node.
If it is determined in operation S25 that the chromosomal individual includes a gene segment that does not satisfy the constraint condition, operation S26 is performed. In operation S26, gene segments that do not satisfy the constraints may be deleted. 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 can be calculated, and the gene segments with the transportation time exceeding the preset transportation time and/or the gene segments with the ship transportation capacity exceeding the ship transportation capacity constraint condition of the corresponding nodes are/is rejected.
After eliminating gene segments that do not satisfy the constraint condition, operation S24 may be returned to regenerate the chromosomal individual until a chromosomal individual without the gene segments eliminated is generated.
If it is determined in operation S25 that the gene segments of the chromosomal individual all satisfy the constraint condition, operation S27 is performed. In operation S27, the chromosome individuals without the knocked-out gene segments can be used as the initial population, i.e., the initial population is generated.
In another exemplary embodiment of the present disclosure, operations S25 through S27 may be omitted.
Referring back to fig. 1, in operation S13, a 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:
wherein Z is the fitness value of the feasible transportation path corresponding to the chromosome individual, ClA cost of land transportation for a feasible transportation path corresponding to the individual chromosome; coA shipping cost for a feasible transportation route corresponding to the chromosome individual; ctLoading and unloading costs for feasible transportation paths for individual chromosomes; csStockpiling costs for feasible transportation paths corresponding to individual chromosomes; ctoA deferred penalty cost for a feasible transportation path corresponding to the chromosome individual; czA lag penalty cost for a feasible transportation path corresponding to the chromosome individual; f. of1Is a parameter set for ensuring the shipping capacity, if the shipping capacity satisfies the shipping capacity constraint condition, f1Equal to 1 if shippedIf the capacity does not satisfy the constraint condition of the ship transportation capacity, f1Equal to 0.
For example, the transport costs C of the road sections included in the feasible transport route can be calculated from the land transport data entered into the transport networkl(ii) a The transportation cost C of the marine section included in the feasible transportation path can be calculated according to the marine transportation data input into the transportation networko(ii) a The total handling costs C of the feasible transport routes can be calculated from the handling cost data at the shipping address, the wind farm address and/or the transit port address entered into the transport networkt(ii) a The total stacking cost C of the feasible transport routes can be calculated from the stacking cost data at the shipping address, the wind farm address and/or the transit port address entered into the transport networks(ii) a The total deferred penalty cost C for a feasible transport path may be calculated from deferred penalty cost data entered at the shipping address, wind farm address and/or transit port address in the transport networkto(ii) a The total lag penalty cost C for a feasible transport path may be calculated from lag penalty cost data at the shipping address, wind farm address and/or transit port address entered into the transport networkz。
According to an exemplary embodiment of the present disclosure, the ship transportability constraint may include at least one of the following conditions: the ship size is more than or equal to 1.2 times of the unit size, the ship load is more than or equal to 1.2 times of the unit component weight plus the clamp weight, the minimum water depth of the sea area is more than or equal to the ship draught, the ship size is more than or equal to the size of a ship which can be parked at a port, the speed condition meets the preset speed range, the dynamic response of the ship meets the meteorological hydrological requirements of the sea area, and the ship leasing mode and the generated cost meet the economic requirements. In an exemplary embodiment of the present disclosure, the unit size may be an overall size of the wind turbine generator unit.
The fitness function is only used as an example to comprehensively measure various costs and constraints influenced by various factors, but the disclosure is not limited thereto, and the fitness function can be set according to other variable factors to model and digitize the transportation process of the wind generating set influenced by the complex variable factors.
In operation S14, chromosome individuals having fitness values greater than a predetermined threshold value in the initial population are selected. For example, the predetermined threshold value may be set according to a budget value for the total transportation cost of the wind turbine generator system, such that the total transportation cost of the feasible transportation path corresponding to the individual chromosome selected from the initial population does not exceed the budget value.
In operation S15, a crossover operation and a mutation operation are performed.
In an exemplary embodiment of the present disclosure, partial gene segments of two chromosome individuals paired with each other may be replaced and recombined with each other to form two new chromosome individuals, i.e., a crossover operation is performed using a crossover operator. Specifically, the crossover operation may be performed using crossover operators such as single-point crossover, two-point crossover, multi-point crossover, uniform crossover, arithmetic crossover, and the like.
The mutation operation may be performed after the crossover operation is completed. In exemplary embodiments of the present disclosure, gene values at certain loci in a chromosomal individual's encoded string may be replaced with other alleles at that locus, thereby forming a new individual. Specifically, mutation operations can be performed by using mutation operators such as base bit mutation, uniform mutation, boundary mutation, non-uniform mutation, gaussian approximation mutation, and the like.
In operation S16, whether to stop the iteration may be determined according to whether the number of iterations reaches a maximum evolutionary algebra or a set convergence criterion. In an exemplary embodiment of the present disclosure, the set convergence criterion may be that the fitness value of the chromosome individual having the largest fitness value is no longer increased or that the fitness value of the chromosome population is no longer increased.
If the iteration number does not reach the maximum evolution algebra or the set convergence criterion, the operation S14 is returned to and the execution is continued until the iteration number reaches the maximum evolution algebra or the set convergence criterion.
If it is determined in operation S16 that the number of iterations reaches the maximum evolutionary algebra or the set convergence criterion, operation S17 is performed. In operation S17, the chromosome individual having the greatest fitness value may be output as the optimal solution.
In an exemplary embodiment of the present disclosure, the fitness value of each individual chromosome may be calculated according to a fitness function, and the fitness values of all individual chromosomes are compared to screen out the individual chromosome having the largest fitness value.
In operation S18, the optimal solution is chromosome-decoded to generate an arrangement optimization plan of the wind turbine generator system transportation path. In an exemplary embodiment of the present disclosure, the chromosome decoding may be performed according to a decoding method corresponding to the chromosome encoding method.
In an exemplary embodiment of the disclosure, since the larger the fitness value is, the lower the overall transportation cost of the corresponding feasible transportation path is, the feasible transportation path obtained by performing chromosome decoding on the optimal solution may be used as an arrangement optimization scheme of the transportation path of the wind turbine generator set.
The arrangement optimization method of the wind turbine generator system transportation path according to the exemplary embodiment of the present disclosure has been described above with reference to fig. 1 and 2. Hereinafter, an arrangement optimizing device of a wind turbine generator system transportation path and a unit thereof according to an exemplary embodiment of the present disclosure will be described with reference to fig. 3.
Fig. 3 is a schematic view of the arrangement optimizing device 3 of the wind turbine generator set transportation path according to an exemplary embodiment of the present disclosure. It should be understood that the specific processing performed by the arrangement optimizing device 3 and each unit thereof according to the exemplary embodiment of the present invention has been described in detail with reference to fig. 1 and 2, and the details thereof will not be described herein.
The arrangement optimization apparatus 3 may include a transport network initialization unit 31, a chromosome coding unit 32, an fitness value calculation unit 33, a crossing and mutation unit 34, an optimal solution output unit 35, and a chromosome decoding unit 36.
The transport network initialization unit 31 may be configured to initialize the transport network of the wind park according to transport process nodes of the various components of the wind park, the transport process nodes comprising shipping addresses, wind farm addresses and transit port addresses of the various components.
In an exemplary embodiment of the present disclosure, the transport network initialization unit 31 may be configured to: acquiring data associated with the transport process node, the data associated with the transport process node including at least one of ship data, meteorological data, hydrological data, port data, airline data, ship location data, wind farm data, and data regarding constraints; inputting data associated with the transportation process node into the transportation network.
In exemplary embodiments of the present disclosure, a chromosomal individual may include gene segments representing the following information:
the shipping address, the wind power plant address and the total transportation volume of single transportation of various components of the wind generating set; and the following: a transportation route from a delivery location to a wind farm; and/or, a port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm; and/or a port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm, a transport route between a plurality of ports of transfer.
The chromosome coding unit 32 may be configured to generate a plurality of chromosome individuals as an initial population via chromosome coding according to the transportation network, each chromosome individual including one feasible transportation path for transporting each component of the wind turbine generator set from a shipping address to a wind farm address.
In an exemplary embodiment of the present disclosure, the chromosome coding unit 32 may be configured to select a shipping address of each component, constituting a shipping address group; associating a shipping address set with each wind farm address; selecting a port of transfer and a haul route between the shipping address set and the associated wind farm address; a plurality of chromosome individuals are generated.
In an exemplary embodiment of the present disclosure, the chromosome encoding unit 32 may be configured to delete a gene segment that does not satisfy the constraint condition, and regenerate a chromosomal individual until a chromosomal individual in which the gene segment is not deleted is generated; chromosome individuals without the knocked out gene segments are used as an initial population.
In an exemplary embodiment of the present disclosure, the constraint condition may include a ship transport capacity constraint condition that a transport time does not exceed a predetermined transport time and a ship transport capacity does not exceed a corresponding node. The chromosome encoding unit 32 may be configured to: calculating the transportation time of a feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path; and eliminating the gene segments with the transport time exceeding the preset transport time and/or the gene segments with the transport capacity exceeding the ship transport capacity constraint condition of the corresponding nodes.
The fitness value calculating unit 33 may be configured to calculate a fitness value for each individual chromosome in the initial population according to a fitness function. In an exemplary embodiment of the present disclosure, the fitness function may be equation (1) described above.
In an exemplary embodiment of the present disclosure, the ship transportability constraint may include at least one of the following conditions: the ship size is more than or equal to 1.2 times of the unit size, the ship load is more than or equal to 1.2 times of the unit component weight plus the clamp weight, the minimum water depth of the sea area is more than or equal to the ship draught, the ship size is more than or equal to the size of a ship which can be parked at a port, the speed condition meets the preset speed range, the dynamic response of the ship meets the meteorological hydrological requirements of the sea area, and the ship leasing mode and the generated cost meet the economic requirements.
The crossover and mutation unit 34 may be configured to select chromosome individuals with fitness values greater than a predetermined threshold value in the initial population for crossover and mutation operations until the number of iterations reaches a maximum number of evolutionary generations or a set convergence criterion.
The optimal solution output unit 35 may be configured to output the chromosome individual having the largest fitness value as the optimal solution.
The chromosome decoding unit 36 may be configured to perform chromosome decoding on the optimal solution to generate an arrangement optimization scheme of the wind turbine generator set transportation path.
The specific details of the arrangement optimization apparatus 3 and the corresponding processing performed by each unit thereof can be understood with reference to the description of fig. 1 and 2, and will not be described herein again.
Further, according to an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed, implements a wind turbine generator set transport path arrangement optimization method according to an exemplary embodiment of the present disclosure.
In an exemplary embodiment of the disclosure, the computer readable storage medium may carry one or more programs which, when executed, implement the steps of: initializing a transportation network of the wind generating set according to transportation process nodes of various components of the wind generating set, wherein the transportation process nodes comprise delivery addresses of the various components, wind power plant addresses and transit port addresses; generating a plurality of chromosome individuals as an initial population according to the transportation network through chromosome coding, wherein each chromosome individual comprises a feasible transportation path for transporting each component of the wind generating set from a delivery address to a wind power plant address; calculating the fitness value of each chromosome individual in the initial population according to the fitness function; selecting chromosome individuals with fitness values larger than a preset threshold value in the initial population to perform cross operation and mutation operation until the iteration times reach the maximum evolution algebra or a set convergence standard; outputting the chromosome individual with the maximum fitness value as an optimal solution; and carrying out chromosome decoding on the optimal solution to generate an arrangement optimization scheme of the transport path of the wind generating set.
A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing. The computer readable storage medium may be embodied in any device; it may also be present separately and not assembled into the device.
The arrangement optimizing device of the transportation path of the wind turbine generator set according to the exemplary embodiment of the present disclosure has been described above with reference to fig. 3. Next, a computing device according to an exemplary embodiment of the present disclosure is described with reference to fig. 4.
Fig. 4 is a schematic diagram of a computing device according to an example embodiment of the present disclosure.
Referring to fig. 4, the computing device 4 according to an exemplary embodiment of the present disclosure may include a memory 41 and a processor 42, a computer program 43 is stored on the memory 41, and when the computer program 43 is executed by the processor 42, the arrangement optimization method of the wind turbine generator set transportation path according to an exemplary embodiment of the present disclosure is implemented.
In an exemplary embodiment of the present disclosure, the steps of the method described with reference to fig. 1 may be implemented when the computer program 43 is executed by the processor 42: initializing a transportation network of the wind generating set according to transportation process nodes of various components of the wind generating set, wherein the transportation process nodes comprise delivery addresses of the various components, wind power plant addresses and transit port addresses; generating a plurality of chromosome individuals as an initial population according to the transportation network through chromosome coding, wherein each chromosome individual comprises a feasible transportation path for transporting each component of the wind generating set from a delivery address to a wind power plant address; calculating the fitness value of each chromosome individual in the initial population according to the fitness function; selecting chromosome individuals with fitness values larger than a preset threshold value in the initial population to perform cross operation and mutation operation until the iteration times reach the maximum evolution algebra or a set convergence standard; outputting the chromosome individual with the maximum fitness value as an optimal solution; and carrying out chromosome decoding on the optimal solution to generate an arrangement optimization scheme of the transport path of the wind generating set.
The computing device illustrated in fig. 4 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the disclosure.
The layout optimization method and the layout optimization device of the transportation path of the wind turbine generator set according to the exemplary embodiment of the present disclosure have been described above with reference to fig. 1 to 3. However, it should be understood that: the arrangement optimizing device of the wind turbine generator system transportation path and the units thereof shown in fig. 3 may be respectively configured as software, hardware, firmware or any combination thereof for performing specific functions, the computing device shown in fig. 4 is not limited to include the components shown above, but some components may be added or deleted as needed, and the above components may also be combined.
By adopting the arrangement optimization method, the arrangement optimization device, the computer readable storage medium and the computing device of the wind generating set transportation path according to the exemplary embodiment of the invention, scientific decision guidance can be provided for transportation strategies and plans at least; the method has the advantages that various factors influencing the arrangement planning of the transport path of the wind generating set are comprehensively considered, the transport flow of the wind generating set influenced by complex variable factors is modeled and digitized, the labor cost is saved, the accuracy and precision of the arrangement planning of the transport path are improved, and a transport strategy more conforming to engineering practice is provided; and considering all links of wind generating set transportation, and realizing the configuration optimization of the transportation path of the wind generating set or the optimization of the transportation strategy based on the genetic algorithm model.
The control logic or functions performed by the various components or controllers in the control system may be represented by flowcharts or the like in one or more of the figures. These figures provide representative control strategies and/or logic that may be implemented using one or more processing strategies (e.g., event-driven, interrupt-driven, multi-tasking, multi-threading, and so forth). As such, various steps or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted. Although not always explicitly illustrated, one of ordinary skill in the art will recognize that one or more of the illustrated steps or functions may be repeatedly performed depending on the particular processing strategy being used.
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 changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (18)
1. A method for optimizing the arrangement of a transportation path of a wind generating set is characterized by comprising the following steps:
initializing a transportation network of the wind generating set according to transportation process nodes of various components of the wind generating set, wherein the transportation process nodes comprise delivery addresses of the various components, wind power plant addresses and transit port addresses;
generating a plurality of chromosome individuals as an initial population according to the transportation network through chromosome coding, wherein each chromosome individual comprises a feasible transportation path for transporting each component of the wind generating set from a delivery address to a wind power plant address;
calculating the fitness value of each chromosome individual in the initial population according to the fitness function;
selecting chromosome individuals with fitness values larger than a preset threshold value in the initial population to perform cross operation and mutation operation until the iteration times reach the maximum evolution algebra or a set convergence standard;
outputting the chromosome individual with the maximum fitness value as an optimal solution;
and carrying out chromosome decoding on the optimal solution to generate an arrangement optimization scheme of the transport path of the wind generating set.
2. The method of claim 1, wherein the step of initializing a transport network of the wind power plant based on the transport process nodes of the various components of the wind power plant comprises:
acquiring data associated with the transport process node, the data associated with the transport process node including at least one of ship data, meteorological data, hydrological data, port data, airline data, ship location data, wind farm data, and data regarding constraints;
inputting data associated with the transportation process node into the transportation network.
3. The method of claim 1, wherein the individual chromosome includes gene segments representing:
shipping addresses of various components of the wind turbine generator set, wind farm addresses, total volume of single shipments, and the following:
a transportation route from a delivery location to a wind farm; and/or
A port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm; and/or
A port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm, a transport route between a plurality of ports of transfer.
4. The method of claim 1, wherein the step of generating a plurality of chromosome individuals as an initial population via chromosome coding according to the transportation network comprises:
selecting a delivery address of each component to form a delivery address group;
associating a shipping address set with each wind farm address;
selecting a port of transfer and a haul route between the shipping address set and the associated wind farm address;
a plurality of chromosome individuals are generated.
5. The method of claim 4, wherein the step of generating a plurality of chromosome individuals as an initial population via chromosome coding according to the transportation network further comprises:
eliminating gene segments which do not satisfy the constraint condition, and regenerating chromosome individuals until chromosome individuals without the eliminated gene segments are generated;
chromosome individuals without the knocked out gene segments are used as an initial population.
6. The method of claim 5, wherein the step of eliminating gene segments that do not satisfy the constraint comprises:
calculating the transportation time of a feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path;
and eliminating the gene segments with the transport time exceeding the preset transport time and/or the gene segments with the transport capacity exceeding the ship transport capacity constraint condition of the corresponding nodes.
7. The method of claim 1, wherein the fitness function is:
wherein Z is the fitness value of the feasible transportation path corresponding to the chromosome individual, ClCost of land transportation for feasible transportation path corresponding to chromosome individual, CoShipping costs for feasible transportation routes corresponding to individual chromosomes CtLoading and unloading costs for feasible transport paths for individual chromosomes CsStockpiling costs of feasible transportation routes for individual chromosomes CtoDeferred penalty cost for feasible transportation path corresponding to chromosome individual, CzLag penalty cost, f, for feasible transportation path corresponding to chromosome individual1Is a parameter set for ensuring the shipping capacity, if the shipping capacity satisfies the shipping capacity constraint condition, f1Equal to 1, if the shipping capacity does not satisfy the shipping capacity constraint, f1Equal to 0.
8. The method of claim 7, wherein the vessel transport capacity constraints comprise at least one of:
the ship size is more than or equal to 1.2 times of the unit size, the ship load is more than or equal to 1.2 times of the unit component weight plus the clamp weight, the minimum water depth of the sea area is more than or equal to the ship draught, the ship size is more than or equal to the size of a ship which can be parked at a port, the speed condition meets the preset speed range, the dynamic response of the ship meets the meteorological hydrological requirements of the sea area, and the ship leasing mode and the generated cost meet the economic requirements.
9. An arrangement optimization device for a wind generating set transportation path is characterized by comprising:
a transportation network initialization unit configured to initialize a transportation network of the wind turbine generator set according to transportation process nodes of various components of the wind turbine generator set, wherein the transportation process nodes comprise shipping addresses, wind farm addresses and transfer port addresses of the various components;
a chromosome coding unit configured to generate a plurality of chromosome individuals as an initial population through chromosome coding according to the transportation network, each chromosome individual including one feasible transportation path for transporting each component of the wind turbine generator system from a shipping address to a wind farm address;
a fitness value calculation unit configured to calculate a fitness value of each chromosome individual in the initial population according to a fitness function;
the crossing and mutation unit is configured to select chromosome individuals with fitness values larger than a preset threshold value in the initial population to carry out crossing operation and mutation operation until the iteration number reaches the maximum evolution algebra or a set convergence standard;
an optimal solution output unit configured to output the chromosome individual having the largest fitness value as an optimal solution;
and the chromosome decoding unit is configured to perform chromosome decoding on the optimal solution so as to generate an arrangement optimization scheme of the transportation path of the wind generating set.
10. The arrangement optimization device according to claim 9, wherein the transportation network initialization unit is further configured to:
acquiring data associated with the transport process node, the data associated with the transport process node including at least one of ship data, meteorological data, hydrological data, port data, airline data, ship location data, wind farm data, and data regarding constraints;
inputting data associated with the transportation process node into the transportation network.
11. The layout optimization apparatus of claim 9, wherein the individual chromosome includes segments of genes representing:
the shipping address, the wind power plant address and the total transportation volume of single transportation of various components of the wind generating set; and the following:
a transportation route from a delivery location to a wind farm; and/or
A port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm; and/or
A port of transfer address, a transport route from a shipping location to a port of transfer, a transport route from a port of transfer to a wind farm, a transport route between a plurality of ports of transfer.
12. The layout optimization device of claim 9, wherein the chromosome coding unit is further configured to:
selecting a delivery address of each component to form a delivery address group;
associating a shipping address set with each wind farm address;
selecting a port of transfer and a haul route between the shipping address set and the associated wind farm address;
a plurality of chromosome individuals are generated.
13. The layout optimization device of claim 12, wherein the chromosome coding unit is further configured to:
eliminating gene segments which do not satisfy the constraint condition, and regenerating chromosome individuals until chromosome individuals without the eliminated gene segments are generated;
chromosome individuals without the knocked out gene segments are used as an initial population.
14. The layout optimization device of claim 13, wherein the chromosome coding unit is further configured to:
calculating the transportation time of a feasible transportation path corresponding to each chromosome individual and the ship transportation capacity of each node on the feasible transportation path;
and eliminating the gene segments with the transport time exceeding the preset transport time and/or the gene segments with the transport capacity exceeding the ship transport capacity constraint condition of the corresponding nodes.
15. The spread optimization device of claim 9, wherein the fitness function is:
wherein Z is the fitness value of the feasible transportation path corresponding to the chromosome individual, ClCost of land transportation for feasible transportation path corresponding to chromosome individual, CoShipping costs for feasible transportation routes corresponding to individual chromosomes CtLoading and unloading costs for feasible transport paths for individual chromosomes CsStockpiling costs of feasible transportation routes for individual chromosomes CtoDeferred penalty cost for feasible transportation path corresponding to chromosome individual, CzLag penalty cost, f, for feasible transportation path corresponding to chromosome individual1Is a parameter set for ensuring the shipping capacity, if the shipping capacity satisfies the shipping capacity constraint condition, f1Equal to 1, if the shipping capacity does not satisfy the shipping capacity constraint, f1Equal to 0.
16. The spread optimization apparatus of claim 15, wherein the vessel transport capacity constraints include at least one of:
the ship size is more than or equal to 1.2 times of the unit size, the ship load is more than or equal to 1.2 times of the unit component weight plus the clamp weight, the minimum water depth of the sea area is more than or equal to the ship draught, the ship size is more than or equal to the size of a ship which can be parked at a port, the speed condition meets the preset speed range, the dynamic response of the ship meets the meteorological hydrological requirements of the sea area, and the ship leasing mode and the generated cost meet the economic requirements.
17. A computer-readable storage medium storing a computer program, characterized in that the computer program, when being executed by a processor, implements the method for optimizing the layout of a transport path of a wind park according to any one of claims 1 to 8.
18. A computing device, comprising:
a processor;
a memory storing a computer program which, when executed by the processor, implements the method of wind turbine generator system transport path layout optimization according to any one of claims 1 to 8.
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