CN106875068B - optimization method and system for wind driven generator configuration and model selection - Google Patents
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Abstract
the invention particularly relates to an optimization method and system for wind driven generator configuration selection. The method comprises the following steps: acquiring at least one fan arrangement scheme, and taking each fan arrangement scheme as a chromosome of a genetic algorithm; generating an optimal fan model selection scheme corresponding to each chromosome and fitness corresponding to the optimal fan model selection scheme according to a grouped particle swarm algorithm, and taking the fitness as the fitness of the chromosome; according to the fitness of all chromosomes, calculating the first global optimal fitness of the genetic algorithm, acquiring a target chromosome corresponding to the first global optimal fitness, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting an optimal fan type selection scheme corresponding to the target chromosome as a target type selection scheme. The invention fully considers the global property of the type selection algorithm, can effectively avoid the phenomenon that the type selection optimization is trapped in local optimization, and has better global property, better performance index, more accurate type selection scheme and stronger practicability.
Description
Technical Field
the invention relates to the field of micro site selection of wind driven generators, in particular to an optimization method and system for wind driven generator configuration and type selection.
background
Wind energy is a new pollution-free and renewable energy source, and in the modern society of energy shortage and serious environmental pollution caused by traditional energy sources, the wind power industry becomes one of new energy industry which is vigorously developed. The micro site selection of the wind power plant is a necessary step for reasonable planning of the wind power industry. The micro-site selection of the wind power plant before the construction of the wind power plant can effectively improve the utilization efficiency of wind energy, prolong the service life of a fan, and reduce the operation and maintenance cost and the wind power generation cost of the wind power plant, thereby realizing the reasonable decision and scientific development of the wind power industry. The site selection of the wind power plant comprises macro site selection and micro site selection, wherein the macro site selection aims at selecting the site of the wind power plant, and the micro site selection is mainly used for selecting the type and the installation position of a fan. The long-term recording and analysis of local wind resources are the major premise of site selection of a wind power plant, a wind measuring tower is installed after micro site selection is completed in macro site selection, wind conditions at the site are detected and recorded for more than one year, and wind resource analysis and evaluation are comprehensively performed by combining local long-term meteorological recording and the like. On the basis of wind resource evaluation and site landform comprehensive analysis, the number and the types of the fans are selected, and the installation positions of the fans are determined so as to achieve the maximum expected annual output of the wind power plant or the lowest expected wind power generation electricity consumption cost, so that the wind power plant achieves the maximum economic benefit under the condition that social, economic and environmental indexes are met.
the micro site selection optimization of the wind power plant is a nonlinear strong coupling problem, factors such as local meteorological terrain, environmental indexes, land price, road distribution, construction feasibility and the like need to be comprehensively considered, and various factors such as fluid, weather, electromechanics and the like are involved, so that an optimal solution cannot be obtained by using a traditional optimization method. Therefore, currently, the research results in this direction are mostly calculated by using search-based heuristic algorithms to optimize decisions on specific problems worldwide. The optimization method mainly comprises a genetic algorithm, a random algorithm, a particle swarm optimization algorithm and the like. As the wind speed distribution increases with the altitude, each type of fan has advantages and disadvantages under different wind energy distribution conditions. In the micro site selection of the wind power plant, fans with various types and heights are installed in the same wind power plant, so that the wind energy utilization rate and the whole power generation efficiency can be effectively improved, and the cost of wind power generation is further reduced.
Among documents and patents related to the present application, Castro Mora, J, et al, published in the 2007 neuro-typing paper "An atmospheric optimization for wind farm optimal design", propose a problem of multi-model fan configuration optimization and a solution, but do not consider wake effects among fans in the optimization. A genetic algorithm-based wind power plant multi-model fan optimization arrangement scheme (application publication number: CN 103793566A) proposes that the genetic algorithm is used for solving the arrangement problem of the multi-model wind driven generators, but the adopted fan model selection optimization algorithm does not consider the globality of the optimization algorithm, is relatively coarse and is not accurate enough.
disclosure of Invention
The invention provides an optimization method and an optimization system for wind driven generator configuration selection, which solve the technical problems.
the technical scheme for solving the technical problems is as follows:
According to an aspect of the present invention, there is provided a method for optimizing wind turbine layout model selection, comprising the steps of:
Step 1, obtaining at least one fan arrangement scheme, and taking each fan arrangement scheme as a chromosome of a genetic algorithm;
step 2, generating an optimal fan model selection scheme corresponding to each chromosome and fitness corresponding to the optimal fan model selection scheme according to a preset group-divided particle swarm algorithm, and taking the fitness as the fitness of the chromosome;
and 3, calculating a first global optimal fitness of the genetic algorithm according to the fitness of the genetic algorithm and all chromosomes, acquiring a target chromosome corresponding to the first global optimal fitness, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting an optimal fan type selection scheme corresponding to the target chromosome as a target type selection scheme.
The invention has the beneficial effects that: according to the optimization method, the genetic algorithm and the grouping type particle swarm algorithm are used in a nested mode, the genetic algorithm is used for selecting the fan arrangement positions, after each generation of the genetic algorithm is generated, the current generation of fan positions serves as the fan arrangement positions, then the grouping type particle swarm algorithm is used for obtaining the optimal solution of the model selection when the fan arrangement positions are arranged, namely the optimal fan model selection scheme, so that the global property of the model selection algorithm is fully considered on the basis of continuously searching the wind power field region and improving the position arrangement precision, the model selection optimization can be effectively prevented from falling into local optimization, the global property is better, the performance index is better, the model selection scheme is more accurate, and the practicability is stronger.
on the basis of the technical scheme, the invention can be further improved as follows.
further, the step 1 specifically comprises:
S101, acquiring a horizontal and vertical coordinate range and at least one fan arrangement scheme of a wind power plant area;
S102, generating an initial position matrix of the fans in the wind power plant area according to the fan arrangement scheme and the horizontal and vertical coordinate range;
S103, carrying out binary coding on each row of the initial position matrix, and taking the coding result of each row in the initial position matrix as a chromosome of a genetic algorithm;
Each row of the initial position matrix represents one fan arrangement scheme.
the beneficial effect of adopting the further scheme is that: according to the further technical scheme, the position coordinates of the fans are directly coded, and the checkerboards are not selected after the checkerboards are divided into the wind power plant area, so that continuous search can be performed in the range of the wind power plant, and therefore the position of the fans can be effectively selected and optimized for the actual wind power plant area. Meanwhile, the position searching density can be changed through a genetic algorithm coding mode, so that the optimization speed is improved.
further, the step 2 specifically comprises:
s201, acquiring an initial model selection result of the fan;
s202, determining a search space of a grouped particle swarm algorithm according to the initial model selection result, and dividing the search space into at least one independent subspace;
S203, predicting at least one fan model selection scheme corresponding to the chromosome in the initial position matrix according to the initial model selection result, and distributing the fan model selection scheme to a corresponding subspace as the particles of the chromosome, wherein one particle represents one fan model selection scheme;
S204, randomly initializing the speed of the particles in the subspace and the positions of the particles in the subspace, then calculating the fitness of each particle at the current position of the subspace by adopting a corresponding fan arrangement scheme according to a preset fitness calculation function, acquiring the individual optimal fitness of each particle and the second global optimal fitness of all the particles in the subspace, and taking the particle position corresponding to the second global optimal fitness as the current group optimal position of the subspace;
S205, continuously evolving the speed and the position of each particle in the subspace according to the individual optimal fitness, the second global optimal fitness and a preset evolution rule so as to optimize the current population optimal position until a preset evolution termination condition is reached, and then executing S206;
s206, comparing the current population optimal positions of all the subspaces, obtaining the target optimal position of the chromosome in the search space from all the current population optimal positions, and taking the fitness corresponding to the target optimal position as the fitness of the chromosome, wherein the target optimal position is the optimal fan model selection scheme corresponding to the chromosome.
The beneficial effect of adopting the further scheme is that: according to the further technical scheme, the optimal type selection scheme of each chromosome in the genetic algorithm is obtained by using the group-divided particle swarm algorithm, so that the type selection solution can be quickly found under the condition that the parameters of various types of wind driven generators are more, the two algorithms can be nested for use, the calculation time is not too long under the condition that the iteration times are too many, and the better global optimality is realized.
further, the step 3 specifically comprises:
S301, acquiring fitness and target optimal positions of all chromosomes in the initial position matrix;
s302, calculating a first global optimal fitness of the genetic algorithm according to the fitness of all chromosomes, and acquiring iteration times of the genetic algorithm and a target chromosome corresponding to the first global optimal fitness of the genetic algorithm;
S303, judging whether the iteration number reaches a preset iteration number threshold, if so, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting a target optimal position corresponding to the target chromosome as a target type selection scheme, otherwise, executing S304;
S304, taking all chromosomes generated in the step 1 as parent chromosome groups, performing intersection and mutation operations to generate child chromosomes, updating the initial position matrix according to the child chromosomes, and returning to the step S203.
The beneficial effect of adopting the further scheme is that: in the further technical scheme, the genetic algorithm is used for obtaining the optimal arrangement scheme and the corresponding optimal selection scheme, so that the algorithm is advanced, the feasible solution can be still solved aiming at the nonlinear strong coupling optimization problem, and the applicability is strong.
further, in step S203, the fan model selection scheme is predicted according to the fan model and the cabin height of the fan installation, and the schemes that the fan model is the same, the cabin height is different, the fan model is different, the cabin height is the same, the fan model is different, and the cabin height is different are all determined as different fan model selection schemes.
the beneficial effect of adopting the further scheme is that: the further technical scheme fully considers the characteristics of the actual wind field area and the characteristics of using the multi-model fans to utilize wind energy, and can be popularized to the situation of site selection of the three-dimensional fans in complex terrains and mixed loading of the multi-model fans.
further, in step S204, the fitness is a reciprocal of the power consumption cost calculated by the particle using the corresponding fan arrangement scheme and the type selection scheme, and the fitness calculation function is as follows:
wherein CoE is the annual power generation cost, AEP is the annual average power generation amount of the wind power plant, C i is the annual average cost of each fan, C O&M is the annual operation and maintenance cost of the wind power plant, C land is the annual average cost of land occupation of the wind power plant, C other is the annual average value of other expenses of the wind power plant, P i is the annual average power generation amount of each fan, and N is the total number of the fans of the wind power plant.
In order to solve the technical problem of the present invention, the invention further provides an optimization system for wind turbine configuration selection, comprising:
the first generation module is used for acquiring at least one fan arrangement scheme and taking each fan arrangement scheme as a chromosome of a genetic algorithm;
the second generation module is used for generating an optimal fan model selection scheme corresponding to each chromosome and fitness corresponding to the optimal fan model selection scheme according to a preset group-divided particle swarm algorithm, and taking the fitness as the fitness of the chromosome;
And the output module is used for calculating the first global optimal fitness of the genetic algorithm according to the fitness of the genetic algorithm and all chromosomes, acquiring a target chromosome corresponding to the first global optimal fitness, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting an optimal fan type selection scheme corresponding to the target chromosome as a target type selection scheme.
The invention has the beneficial effects that: the optimization system of the invention uses the genetic algorithm and the grouping particle swarm algorithm in a nested manner, firstly uses the genetic algorithm to select the fan arrangement position, uses the current generation fan position as the fan arrangement position after each generation of the population of the genetic algorithm is generated, and then uses the grouping particle swarm algorithm to obtain the optimal solution of the model selection when the fan arrangement position is obtained, namely the optimal fan model selection scheme, thereby effectively avoiding the model selection optimization from falling into local optimization on the basis of continuously searching the wind power field area and improving the position arrangement precision, having better global performance, better performance index, more accurate model selection scheme and stronger practicability.
Further, the first generating module comprises:
The first acquisition unit is used for acquiring the horizontal and vertical coordinate range of the wind power plant area and at least one fan arrangement scheme;
the first generating unit is used for generating an initial position matrix of the fans in the wind power plant area according to the fan arrangement scheme and the horizontal and vertical coordinate range;
a second generating unit, configured to perform binary coding on each row of the initial position matrix, and use a coding result of each row in the initial position matrix as a chromosome of a genetic algorithm; each row of the initial position matrix represents one fan arrangement scheme.
further, the second generating module includes:
The second acquisition unit is used for acquiring an initial model selection result of the fan;
The dividing unit is used for determining a search space of a grouped particle swarm algorithm according to the initial model selection result and dividing the search space into at least one independent subspace;
a third generating unit, configured to predict, according to the initial model selection result, at least one fan model selection scheme corresponding to the chromosome in the initial position matrix, and allocate the fan model selection scheme to a corresponding subspace as a particle of the chromosome, where one particle represents one fan model selection scheme;
the fourth generating unit is used for randomly initializing the speed of the particles in the subspace and the positions of the particles in the subspace, then calculating the fitness of each particle in the current position of the subspace by adopting a corresponding fan arrangement scheme according to a preset fitness calculation function, acquiring the individual optimal fitness of each particle and the second global optimal fitness of all the particles in the subspace, and taking the particle position corresponding to the second global optimal fitness as the current group optimal position of the subspace;
the first evolution unit is used for continuously evolving the speed and the position of each particle in the subspace according to the individual optimal fitness, the second global optimal fitness and a preset evolution rule so as to optimize the current group optimal position until a preset evolution termination condition is reached, and then driving the fifth generation unit;
and the fifth generating unit is used for comparing the current group optimal positions of all the subspaces, acquiring the target optimal position of the chromosome in the search space from all the current group optimal positions, and taking the fitness corresponding to the target optimal position as the fitness of the chromosome, wherein the target optimal position is the optimal fan model selection scheme corresponding to the chromosome.
further, the output module includes:
The third acquisition unit is used for acquiring the fitness and the target optimal position of all chromosomes in the initial position matrix;
a sixth generating unit, configured to calculate a first global optimal fitness of the genetic algorithm according to the fitness of all chromosomes, and obtain iteration times of the genetic algorithm and a target chromosome corresponding to the first global optimal fitness of the genetic algorithm;
the judging unit is used for judging whether the iteration number reaches a preset iteration number threshold value, if so, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting a target optimal position corresponding to the target chromosome as a target type selection scheme, otherwise, driving the second evolution unit;
And the second evolution unit is used for taking all chromosomes generated by the first generation module as parent chromosome groups, generating offspring chromosomes after carrying out crossing and mutation operations, updating the initial position matrix according to the offspring chromosomes and driving the third generation unit.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
drawings
fig. 1 is a schematic flow chart of an optimization method for wind turbine configuration model selection according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an optimization system for wind turbine configuration model selection according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a first generation module in an optimization system for wind turbine configuration model selection according to another embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a second generation module in the optimization system for wind turbine configuration model selection according to another embodiment of the present invention;
Fig. 5 is a schematic structural diagram of an output module in an optimization system for wind turbine configuration selection according to another embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a schematic flow chart of an optimization method for wind turbine configuration model selection according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
step 1, obtaining at least one fan arrangement scheme, and taking each fan arrangement scheme as a chromosome of a genetic algorithm;
step 2, generating an optimal fan model selection scheme corresponding to each chromosome and fitness corresponding to the optimal fan model selection scheme according to a preset group-divided particle swarm algorithm, and taking the fitness as the fitness of the chromosome;
and 3, calculating a first global optimal fitness of the genetic algorithm according to the fitness of the genetic algorithm and all chromosomes, acquiring a target chromosome corresponding to the first global optimal fitness, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting an optimal fan type selection scheme corresponding to the target chromosome as a target type selection scheme.
according to the optimization method, the genetic algorithm and the grouping type particle swarm algorithm are used in a nested mode, the fan arrangement position is selected through the genetic algorithm, after each generation of the population of the genetic algorithm is generated, the current generation of the fan position serves as the fan arrangement position, then the grouping type particle swarm algorithm is used for obtaining the optimal solution of the type selection when the fan arrangement position is obtained, namely the optimal fan type selection scheme, and therefore the situation that the type selection optimization falls into local optimization can be effectively avoided on the basis that the wind power field area is searched continuously and the position arrangement precision is improved, the overall performance is better, the performance index is better, the type selection scheme is more accurate, and the practicability is higher.
in a preferred embodiment, the step 1 specifically includes:
s101, acquiring a horizontal and vertical coordinate range and at least one fan arrangement scheme of a wind power plant area;
S102, generating an initial position matrix of the fans in the wind power plant area according to the fan arrangement scheme and the horizontal and vertical coordinate range;
s103, carrying out binary coding on each row of the initial position matrix, and taking the coding result of each row in the initial position matrix as a chromosome of a genetic algorithm; each row of the initial position matrix represents one fan arrangement scheme.
the wind power plant area grid optimization method and device directly encode the position coordinates of the wind turbines, and the wind power plant area grid is not selected after the wind power plant area grid is divided, so that continuous searching can be conducted in the range of the wind power plant, and therefore the position of the wind turbines can be effectively selected and optimized for the actual wind power plant area. Meanwhile, the position searching density can be changed through a genetic algorithm coding mode, so that the optimization speed is improved.
in another preferred embodiment, the step 2 specifically includes:
S201, obtaining an initial model selection result of the wind turbine, specifically performing initial model selection on the wind turbine according to a wind resource evaluation result and the topographic meteorological characteristics of a wind farm, and determining a plurality of alternative models for model selection optimization;
s202, determining a search space of a grouped particle swarm algorithm according to the initial model selection result, and dividing the search space into at least one independent subspace;
s203, predicting at least one fan model selection scheme corresponding to the chromosome in the initial position matrix according to the initial model selection result, and distributing the fan model selection scheme to a corresponding subspace as the particles of the chromosome, wherein one particle represents one fan model selection scheme; in a specific embodiment, the number of particles per subspace is greater than 3;
s204, randomly initializing the speed of the particles in the subspace and the positions of the particles in the subspace, then calculating the fitness of each particle at the current position of the subspace by adopting a corresponding fan arrangement scheme according to a preset fitness calculation function, acquiring the individual optimal fitness of each particle and the second global optimal fitness of all the particles in the subspace, and taking the particle position corresponding to the second global optimal fitness as the current group optimal position of the subspace;
s205, continuously evolving the speed and the position of each particle in the subspace according to the individual optimal fitness, the second global optimal fitness and a preset evolution rule so as to optimize the current population optimal position until a preset evolution termination condition is reached, and then executing S206;
S206, comparing the current population optimal positions of all the subspaces, obtaining the target optimal position of the chromosome in the search space from all the current population optimal positions, and taking the fitness corresponding to the target optimal position as the fitness of the chromosome, wherein the target optimal position is the optimal fan model selection scheme corresponding to the chromosome.
In the step S205, the evolution termination condition is that the iteration number of the clustered particle swarm algorithm reaches a preset iteration number threshold, and certainly, in other embodiments, other evolution termination conditions may be adopted, for example, the evolution is terminated when the increment of the second global optimal fitness is smaller than the preset increment threshold, and these schemes are all within the protection scope of the present invention.
The optimal type selection scheme of each chromosome in the genetic algorithm is obtained by using the group-based particle swarm optimization in the preferred embodiment, so that the type selection solution can be quickly found under the condition that the parameters of various types of wind driven generators are more, the two algorithms can be nested for use, the calculation time is not too long under the condition that the iteration times are too many, and the optimal type selection scheme has better global optimality.
in another preferred embodiment, the step 3 specifically includes:
s301, acquiring fitness and target optimal positions of all chromosomes in the initial position matrix;
s302, calculating a first global optimal fitness of the genetic algorithm according to the fitness of all chromosomes, and acquiring iteration times of the genetic algorithm and a target chromosome corresponding to the first global optimal fitness of the genetic algorithm;
S303, judging whether the iteration number reaches a preset iteration number threshold, if so, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting a target optimal position corresponding to the target chromosome as a target type selection scheme, otherwise, executing S304;
s304, taking all chromosomes generated in the step 1 as parent chromosome groups, performing intersection and mutation operations to generate child chromosomes, updating the initial position matrix according to the child chromosomes, and returning to the step S203.
The optimal configuration scheme and the corresponding optimal selection scheme are obtained by using the genetic algorithm, so that the optimal configuration scheme and the corresponding optimal selection scheme are advanced, the feasible solution can be still solved aiming at the nonlinear strong coupling optimization problem, and the applicability is strong.
In another embodiment, the model selection scheme of the fan is predicted according to the model of the fan and the height of the cabin where the fan is installed, and the schemes with the same model of the fan, different heights of the cabins, different models of the fan, the same height of the cabin, different models of the fan and different heights of the cabin are all determined as different model selection schemes of the fan. For example, the alternative fans are two factory models a (rated power is 1.5MW) and B (rated power is 2MW), and the heights of the cabins installed on the fans of each factory model are two (1.5MW has two heights of 65 meters and 80 meters, and 2MW has two heights of 80 meters and 90 meters), that is, there are 4 fan models. The optimal embodiment fully considers the characteristics of the actual wind field area and the characteristics of using multi-model fans to utilize wind energy, and can be popularized to the situation of site selection of three-dimensional fans in complex terrains and mixed loading of the multi-model fans.
Preferably, in step S204, the fitness is a reciprocal of the power consumption cost calculated by the particle using the corresponding fan arrangement scheme and the model selection scheme, and the fitness calculation function is as follows:
wherein CoE is annual power generation cost, AEP is annual average power generation amount of a wind power plant, C i is annual average cost of each fan, C O&M is annual operation and maintenance cost of the wind power plant, C land is annual average occupation cost of land of the wind power plant, C other is annual average value of other expenses of the wind power plant, P i is annual average power generation amount of each fan, and N is total number of the fans of the wind power plant.
fig. 2 is a schematic structural diagram of an optimization system for wind turbine configuration selection according to another embodiment, as shown in fig. 2, including:
the first generation module is used for acquiring at least one fan arrangement scheme and taking each fan arrangement scheme as a chromosome of a genetic algorithm;
The second generation module is used for generating an optimal fan model selection scheme corresponding to each chromosome and fitness corresponding to the optimal fan model selection scheme according to a preset group-divided particle swarm algorithm, and taking the fitness as the fitness of the chromosome;
And the output module is used for calculating the first global optimal fitness of the genetic algorithm according to the fitness of the genetic algorithm and all chromosomes, acquiring a target chromosome corresponding to the first global optimal fitness, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting an optimal fan type selection scheme corresponding to the target chromosome as a target type selection scheme.
the optimization system of the invention uses the genetic algorithm and the grouping particle swarm algorithm in a nested manner, firstly uses the genetic algorithm to select the fan arrangement position, uses the current generation fan position as the fan arrangement position after each generation of the population of the genetic algorithm is generated, and then uses the grouping particle swarm algorithm to obtain the optimal solution of the model selection when the fan arrangement position is obtained, namely the optimal fan model selection scheme, thereby effectively avoiding the model selection optimization from falling into local optimization on the basis of continuously searching the wind power field area and improving the position arrangement precision, having better global performance, better performance index, more accurate model selection scheme and stronger practicability.
fig. 3 is a schematic structural diagram of the first generating module in a preferred embodiment, and as shown in fig. 3, the first generating module includes:
The first acquisition unit is used for acquiring the horizontal and vertical coordinate range of the wind power plant area and at least one fan arrangement scheme;
the first generating unit is used for generating an initial position matrix of the fans in the wind power plant area according to the fan arrangement scheme and the horizontal and vertical coordinate range;
a second generating unit, configured to perform binary coding on each row of the initial position matrix, and use a coding result of each row in the initial position matrix as a chromosome of a genetic algorithm; each row of the initial position matrix represents one fan arrangement scheme.
The first generation module of the preferred embodiment directly encodes the position coordinates of the wind turbine, rather than selecting the checkerboards after the checkerboards are divided into the wind farm areas, so that continuous search can be performed within the range of the wind farm, and the position of the wind turbine can be effectively selected and optimized for the actual wind farm areas. Meanwhile, the position searching density can be changed through a genetic algorithm coding mode, so that the optimization speed is improved.
Fig. 4 is a schematic structural diagram of the second generating module in another preferred embodiment, and as shown in fig. 4, the second generating module includes:
the second acquisition unit is used for acquiring an initial model selection result of the fan;
the dividing unit is used for determining a search space of a grouped particle swarm algorithm according to the initial model selection result and dividing the search space into at least one independent subspace;
A third generating unit, configured to predict, according to the initial model selection result, at least one fan model selection scheme corresponding to the chromosome in the initial position matrix, and allocate the fan model selection scheme to a corresponding subspace as a particle of the chromosome, where one particle represents one fan model selection scheme;
the fourth generating unit is used for randomly initializing the speed of the particles in the subspace and the positions of the particles in the subspace, then calculating the fitness of each particle in the current position of the subspace by adopting a corresponding fan arrangement scheme according to a preset fitness calculation function, acquiring the individual optimal fitness of each particle and the second global optimal fitness of all the particles in the subspace, and taking the particle position corresponding to the second global optimal fitness as the current group optimal position of the subspace;
The first evolution unit is used for continuously evolving the speed and the position of each particle in the subspace according to the individual optimal fitness, the second global optimal fitness and a preset evolution rule so as to optimize the current group optimal position until a preset evolution termination condition is reached, and then driving the fifth generation unit;
And the fifth generating unit is used for comparing the current group optimal positions of all the subspaces, acquiring the target optimal position of the chromosome in the search space from all the current group optimal positions, and taking the fitness corresponding to the target optimal position as the fitness of the chromosome, wherein the target optimal position is the optimal fan model selection scheme corresponding to the chromosome.
the second generation module of the preferred embodiment uses the clustering particle swarm algorithm to obtain the optimal model selection scheme of each chromosome in the genetic algorithm, so that not only can the model selection solution be quickly found under the condition that the parameters of various types of wind driven generators are more, but also the two algorithms can be nested for use, and when the iteration times are too many, the calculation time is not too long, and meanwhile, the global optimality is better.
fig. 5 is a schematic structural diagram of the output module in another preferred embodiment, and as shown in fig. 5, the output module includes:
The third acquisition unit is used for acquiring the fitness and the target optimal position of all chromosomes in the initial position matrix;
a sixth generating unit, configured to calculate a first global optimal fitness of the genetic algorithm according to the fitness of all chromosomes, and obtain iteration times of the genetic algorithm and a target chromosome corresponding to the first global optimal fitness of the genetic algorithm;
the judging unit is used for judging whether the iteration number reaches a preset iteration number threshold value, if so, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting a target optimal position corresponding to the target chromosome as a target type selection scheme, otherwise, driving the second evolution unit;
and the second evolution unit is used for taking all chromosomes generated by the first generation module as parent chromosome groups, generating offspring chromosomes after carrying out crossing and mutation operations, updating the initial position matrix according to the offspring chromosomes and driving the third generation unit.
The output module of the preferred embodiment uses the genetic algorithm to obtain the optimal arrangement scheme and the corresponding optimal selection scheme, so that the algorithm is advanced, the feasible solution can be still solved aiming at the nonlinear strong coupling optimization problem, and the applicability is strong.
in another preferred embodiment, the third generating unit predicts the fan model selection scheme according to the fan model and the cabin height of the fan installation, and determines the schemes with the same fan model and different cabin heights, different fan models and the same cabin height and different fan models and different cabin heights as different fan model selection schemes. Therefore, the characteristics of the actual wind field area and the characteristics of using the multi-model fans to utilize wind energy are fully considered, and the method can be popularized to the conditions of site selection of the three-dimensional fans in complex terrains and mixed loading of the multi-model fans.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
in the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. An optimization method for wind driven generator configuration selection is characterized by comprising the following steps:
step 1, obtaining at least one fan arrangement scheme, and taking each fan arrangement scheme as a chromosome of a genetic algorithm;
step 2, generating an optimal fan model selection scheme corresponding to each chromosome and fitness corresponding to the optimal fan model selection scheme according to a preset group-divided particle swarm algorithm, and taking the fitness as the fitness of the chromosome;
Step 3, calculating a first global optimal fitness of the genetic algorithm according to the fitness of the genetic algorithm and all chromosomes, acquiring a target chromosome corresponding to the first global optimal fitness, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting an optimal fan type selection scheme corresponding to the target chromosome as a target type selection scheme;
The step 1 specifically comprises the following steps:
s101, acquiring a horizontal and vertical coordinate range and at least one fan arrangement scheme of a wind power plant area;
S102, generating an initial position matrix of the fans in the wind power plant area according to the fan arrangement scheme and the horizontal and vertical coordinate range;
s103, carrying out binary coding on each row of the initial position matrix, and taking the coding result of each row in the initial position matrix as a chromosome of a genetic algorithm;
Each row of the initial position matrix represents a fan arrangement scheme;
the step 2 specifically comprises the following steps:
S201, acquiring an initial model selection result of the fan;
s202, determining a search space of a grouped particle swarm algorithm according to the initial model selection result, and dividing the search space into at least one independent subspace;
s203, predicting at least one fan model selection scheme corresponding to the chromosome in the initial position matrix according to the initial model selection result, and distributing the fan model selection scheme to a corresponding subspace as the particles of the chromosome, wherein one particle represents one fan model selection scheme;
s204, randomly initializing the speed of the particles in the subspace and the positions of the particles in the subspace, then calculating the fitness of each particle at the current position of the subspace by adopting a corresponding fan arrangement scheme according to a preset fitness calculation function, acquiring the individual optimal fitness of each particle and the second global optimal fitness of all the particles in the subspace, and taking the particle position corresponding to the second global optimal fitness as the current group optimal position of the subspace;
S205, continuously evolving the speed and the position of each particle in the subspace according to the individual optimal fitness, the second global optimal fitness and a preset evolution rule so as to optimize the current population optimal position until a preset evolution termination condition is reached, and then executing S206;
s206, comparing the current population optimal positions of all the subspaces, obtaining the target optimal position of the chromosome in the search space from all the current population optimal positions, and taking the fitness corresponding to the target optimal position as the fitness of the chromosome, wherein the target optimal position is the optimal fan model selection scheme corresponding to the chromosome.
2. the method for optimizing wind turbine generator configuration model selection according to claim 1, wherein the step 3 is specifically:
s301, acquiring fitness and target optimal positions of all chromosomes in the initial position matrix;
S302, calculating a first global optimal fitness of the genetic algorithm according to the fitness of all chromosomes, and acquiring iteration times of the genetic algorithm and a target chromosome corresponding to the first global optimal fitness of the genetic algorithm;
S303, judging whether the iteration number reaches a preset iteration number threshold, if so, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting a target optimal position corresponding to the target chromosome as a target type selection scheme, otherwise, executing S304;
s304, taking all chromosomes generated in the step 1 as parent chromosome groups, performing intersection and mutation operations to generate child chromosomes, updating the initial position matrix according to the child chromosomes, and returning to the step S203.
3. the method for optimizing the wind turbine generator configuration model selection according to claim 1, wherein in step S203, the fan model selection scheme is predicted according to the fan model and the cabin height of the installed fan, and the schemes with the same fan model and cabin height, different fan model and cabin height, and different fan model and cabin height are determined as different fan model selection schemes.
4. The method for optimizing wind turbine generator configuration and model selection according to any one of claims 1 to 3, wherein in step S204, the fitness is a reciprocal of a power consumption cost calculated by the particles using the corresponding fan configuration scheme and model selection scheme, and the fitness calculation function is as follows:
wherein CoE is the annual power generation cost, AEP is the annual average power generation amount of the wind power plant, C i is the annual average cost of each fan, C O&M is the annual operation and maintenance cost of the wind power plant, C land is the annual average cost of land occupation of the wind power plant, C other is the annual average value of other expenses of the wind power plant, P i is the annual average power generation amount of each fan, and N is the total number of the fans of the wind power plant.
5. an optimization system for wind turbine configuration model selection, comprising:
the first generation module is used for acquiring at least one fan arrangement scheme and taking each fan arrangement scheme as a chromosome of a genetic algorithm;
The second generation module is used for generating an optimal fan model selection scheme corresponding to each chromosome and fitness corresponding to the optimal fan model selection scheme according to a preset group-divided particle swarm algorithm, and taking the fitness as the fitness of the chromosome;
the output module is used for calculating a first global optimal fitness of the genetic algorithm according to the fitness of the genetic algorithm and all chromosomes, acquiring a target chromosome corresponding to the first global optimal fitness, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting an optimal fan type selection scheme corresponding to the target chromosome as a target type selection scheme;
the first generation module comprises:
The first acquisition unit is used for acquiring the horizontal and vertical coordinate range of the wind power plant area and at least one fan arrangement scheme;
The first generating unit is used for generating an initial position matrix of the fans in the wind power plant area according to the fan arrangement scheme and the horizontal and vertical coordinate range;
a second generating unit, configured to perform binary coding on each row of the initial position matrix, and use a coding result of each row in the initial position matrix as a chromosome of a genetic algorithm; each row of the initial position matrix represents a fan arrangement scheme;
The second generation module comprises:
The second acquisition unit is used for acquiring an initial model selection result of the fan;
the dividing unit is used for determining a search space of a grouped particle swarm algorithm according to the initial model selection result and dividing the search space into at least one independent subspace;
A third generating unit, configured to predict, according to the initial model selection result, at least one fan model selection scheme corresponding to the chromosome in the initial position matrix, and allocate the fan model selection scheme to a corresponding subspace as a particle of the chromosome, where one particle represents one fan model selection scheme;
the fourth generating unit is used for randomly initializing the speed of the particles in the subspace and the positions of the particles in the subspace, then calculating the fitness of each particle in the current position of the subspace by adopting a corresponding fan arrangement scheme according to a preset fitness calculation function, acquiring the individual optimal fitness of each particle and the second global optimal fitness of all the particles in the subspace, and taking the particle position corresponding to the second global optimal fitness as the current group optimal position of the subspace;
the first evolution unit is used for continuously evolving the speed and the position of each particle in the subspace according to the individual optimal fitness, the second global optimal fitness and a preset evolution rule so as to optimize the current group optimal position until a preset evolution termination condition is reached, and then driving the fifth generation unit;
and the fifth generating unit is used for comparing the current group optimal positions of all the subspaces, acquiring the target optimal position of the chromosome in the search space from all the current group optimal positions, and taking the fitness corresponding to the target optimal position as the fitness of the chromosome, wherein the target optimal position is the optimal fan model selection scheme corresponding to the chromosome.
6. the wind turbine generator configuration selection optimization system according to claim 5, wherein the output module comprises:
the third acquisition unit is used for acquiring the fitness and the target optimal position of all chromosomes in the initial position matrix;
a sixth generating unit, configured to calculate a first global optimal fitness of the genetic algorithm according to the fitness of all chromosomes, and obtain iteration times of the genetic algorithm and a target chromosome corresponding to the first global optimal fitness of the genetic algorithm;
The judging unit is used for judging whether the iteration number reaches a preset iteration number threshold value, if so, outputting a fan arrangement scheme corresponding to the target chromosome as a target arrangement scheme, and outputting a target optimal position corresponding to the target chromosome as a target type selection scheme, otherwise, driving the second evolution unit;
and the second evolution unit is used for taking all chromosomes generated by the first generation module as parent chromosome groups, generating offspring chromosomes after carrying out crossing and mutation operations, updating the initial position matrix according to the offspring chromosomes and driving the third generation unit.
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