CN106886833B - Site selection and type selection optimization method of wind driven generator suitable for complex constraint conditions - Google Patents

Site selection and type selection optimization method of wind driven generator suitable for complex constraint conditions Download PDF

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CN106886833B
CN106886833B CN201710021306.9A CN201710021306A CN106886833B CN 106886833 B CN106886833 B CN 106886833B CN 201710021306 A CN201710021306 A CN 201710021306A CN 106886833 B CN106886833 B CN 106886833B
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唐晓宇
杨秦敏
陈积明
孙优贤
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Abstract

The invention discloses a site selection and type selection optimization method of a wind driven generator, which is suitable for complex constraint conditions. Aiming at the optimization problem of site selection of the wind driven generator, when nonlinear constraint conditions such as the minimum annual energy production of the whole field and the shortest distance between fans exist, the fan position is selected by using a genetic algorithm, and the optimal scheme of the type selection at the fan position is solved by using a particle swarm algorithm. The method effectively sets a target function of the particle swarm algorithm, adds a penalty function and adds a minimum power generation limiting condition into the target function, and the target function is used as a wind driven generator type selection optimization algorithm and is nested with a genetic algorithm. In the genetic algorithm, a target function of the genetic algorithm is effectively set, a penalty function is added, the shortest fan distance, namely a safe distance limiting condition is added, and the multi-model mixed position model optimization of the wind power plant generator is carried out. The method can solve the optimization problem of complex nonlinear constraint conditions, and has the advantages of better performance index, more accurate model selection scheme and stronger practicability.

Description

Site selection and type selection optimization method of wind driven generator suitable for complex constraint conditions
Technical Field
The invention relates to a method for optimizing the arrangement of multiple types of wind driven generators in a wind power plant, in particular to a method for optimizing the site selection and the type selection of the wind driven generators suitable for complex constraint conditions.
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. Meanwhile, in the process of micro-site selection of the wind power plant, various limiting conditions exist, such as the requirement of meeting the safety distance and the minimum annual energy production of the wind power plant in normal times.
Among documents and patents related to this patent, Castro Mora, J, etc. published in the 2007 neuro-typing paper "An atmospheric optimization for wind farm optimal design", the problem of multi-model fan configuration optimization and a solution are proposed, but the wake effect among fans is not considered in the optimization. A genetic algorithm-based wind power plant multi-model fan optimization arrangement scheme (application publication number: CN103793566A) proposes that a genetic algorithm is used for solving the arrangement problem of 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. None of these studies take into account complex constraints or assume to be quite simple and are not suitable for use in micro-siting practice.
Disclosure of Invention
The invention aims to overcome the problems and defects of the existing research and technology and provides a wind driven generator site selection and type selection optimization method suitable for complex constraint conditions. According to the method, on the basis of continuously searching the wind power plant region and meeting the safety distance and improving the position arrangement precision, the sum of a model selection optimization algorithm is fully considered, the wind resources in the vertical direction can be fully utilized, and in addition, the limiting condition of the annual minimum generated energy of the wind power plant is considered. The method has the advantages of practicability and high expansibility.
The purpose of the invention is realized by the following technical scheme: a wind driven generator site selection and type selection optimization method suitable for complex constraint conditions comprises the following steps:
1) according to the wind resource evaluation result and the topographic meteorological characteristics of the wind farm, carrying out initial model selection on the wind driven generator, determining a plurality of alternative models for model selection optimization, and reading in parameters such as relevant topographic terrain, meteorological phenomena and the like of the wind farm;
2) abstracting a complex constraint condition of an optimization problem according to site selection requirements, and relaxing the constraint condition into a penalty function item in a target function to enable an optimization result to meet safety distance constraints among fans and annual minimum power generation amount constraints of a wind field;
3) randomly generating an initial position matrix of the fans in the horizontal and vertical coordinate range of the wind power plant region, wherein each row of the matrix represents a fan position arrangement scheme, namely a chromosome, the row represents the number of chromosomes of a genetic algorithm, and each row of the matrix is subjected to binary coding;
4) generating an initial model matrix in a given alternative fan model and coding, wherein the number of matrix rows represents the particle number of a particle swarm algorithm, each row of the matrix represents a particle (a fan model selection scheme), and the speed and the position of the particle in a search domain are initialized randomly as an initial solution for selecting the fan model of the current chromosome;
5) calculating the fitness of each current particle, namely adopting the current fan position and the power consumption cost of the model selection scheme, and solving the individual optimal fitness of each particle and the global optimal fitness of all particles;
6) evolving the position and the speed of each particle according to the particle speed and the position evolution rule set in the particle swarm algorithm;
7) judging whether the set maximum algebra of the particle swarm algorithm is reached, if the set maximum algebra is reached, stopping fan model optimization, selecting the global optimal fitness of the particle swarm algorithm as the fitness of the current chromosome, and if not, returning to the step 5);
8) according to the fitness of each chromosome, the global optimal fitness of a genetic algorithm, namely the global optimal value of fan position model selection, is solved;
9) judging whether the maximum iteration times of the genetic algorithm is reached, if so, outputting a chromosome corresponding to the global optimal fitness of the genetic algorithm as a fan position scheme, and taking the global optimal fitness of the particle swarm algorithm corresponding to the chromosome as a model selection scheme, so as to complete the configuration optimization of the multi-model wind driven generator, otherwise, performing the step 10);
10) and (4) taking all chromosomes as parent chromosome groups, carrying out intersection and mutation operations, calculating selection probability according to the fitness of the chromosomes, selecting to generate child chromosome groups, and returning to the step 4).
Further, a genetic algorithm is used for selecting the position of the fan, a particle swarm algorithm is nested for optimizing the model of the fan, namely, after the position of the fan is selected each time, the particle swarm algorithm is used for obtaining the optimal solution of the model selection of the position of the fan, and the optimal solution is used as the fitness of the position of the fan. The optimization problem has nonlinear constraints, and the nonlinear constraints comprise the minimum distance between fans and the annual minimum power generation amount of a wind power plant. The nonlinear constraint conditions cause the problems of fan model selection and site selection optimization to be very complex and difficult to obtain the optimal solution.
Further, the individual fitness calculation value is calculated as follows: the individual fitness is embodied by calculating the reciprocal of the electricity consumption cost, the individual with the highest fitness is the maximum value of the reciprocal of the electricity consumption cost, namely the individual with the minimum value of the electricity consumption cost, and the calculation formula of the electricity consumption cost CoP is as follows:
Figure BDA0001208313030000031
wherein: CoE is annual power generation cost, AEP is annual average power generation amount of wind power plant, CiIs the purchase year of each fan
Average cost, CO&MAnnual operation and maintenance costs of wind farms, ClandIs the annual average land occupation cost of the wind power plant CotherIs wind
Annual average of other costs of the electric field, PiThe annual average generated energy of each fan, and N is the total number of the fans in the wind power plant.
Further, the optimization problem P1 is relaxed to be an optimization problem P2, a nested optimization problem P3 in P2;
p1 represents the following:
min CoP
wherein: e0Is the annual minimum power generation amount, DsIs the safety distance between the fans di,jIs the distance between the fans, xi，yiIs a two-dimensional coordinate of the position of the fan,
Figure BDA0001208313030000033
and
Figure BDA0001208313030000034
respectively the upper and lower bounds of the two-dimensional coordinate of the fan.
P2 represents the following:
adding a nonlinear constraint condition which must meet a safety distance between fans as a penalty function item into an objective function of the genetic algorithm, and expressing the objective function of the genetic algorithm as follows:
Figure BDA0001208313030000035
Figure BDA0001208313030000036
wherein:
Figure BDA0001208313030000041
C1is a penalty factor.
P3 represents the following:
and adding the constraint condition of the minimum annual power generation amount of the wind field as a penalty function term into the objective function of the particle swarm algorithm, wherein the objective function of the particle swarm algorithm is expressed as follows:
min(CoP+C2e)
Figure BDA0001208313030000042
wherein:
Figure BDA0001208313030000043
C2is a penalty factor, E0Is the lowest annual energy production of the whole wind field.
Further, the particle swarm algorithm is used for selecting fan model combinations of the specific position scheme, wherein the fan model combinations comprise fans of the same model fan cabin and different cabin heights or fans of different models of the same cabin height or fans of different cabin heights, namely, if the fans of the same factory model are installed at different cabin heights, the fans are also considered to be of different models.
Furthermore, constraint conditions are added and solved according to the requirements of the actual wind power plant, and the micro-site selection result is higher in wind energy utilization efficiency and better in practicability.
Compared with the prior art, the invention has the following advantages:
1. the method can search the position of the fan in the wind power plant area in a feasible area in detail and continuously. Because the position coordinates of the wind turbine are directly coded, the checkerboards are not selected after the checkerboards are divided into the wind power plant areas, and continuous search can be carried out in the range of the wind power plant. The position of the fan can be effectively selected and optimized aiming at the actual wind power field area. If the search speed is improved, the position search density can be changed by a genetic algorithm coding mode.
2. The algorithm is advanced, and the feasibility of solving is guaranteed. The use of the genetic algorithm fully ensures that a feasible solution can be solved aiming at the nonlinear strong coupling optimization problem, the use of the grouped particle swarm algorithm ensures that a model selection solution can be quickly found under the condition of more parameters of various types of wind driven generators, the two algorithms can be quickly used in a nested manner, the calculation time cannot be overlong under the condition of excessive iteration times, and the global optimality is better.
3. The practicability is strong. The method 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; the coding mode is easy to realize under the conditions that the wind power plant area has the limiting conditions of roads, maintenance and the like and the non-fan-building sub-area exists.
4. Adding the safety distance between the fans into the objective function by using a method of adding a penalty function, and obtaining a solution meeting the nonlinear constraint condition by properly configuring a penalty factor; the continuity of the address selection search domain is ensured, and the safety distance limiting condition is met.
5. Adding the annual minimum power generation amount of the whole wind field into an objective function of a model selection optimization algorithm by using a method of adding a penalty function, and obtaining a solution meeting a nonlinear constraint condition by properly configuring a penalty factor; and the annual energy production of the wind power plant is ensured to be larger than the set minimum value.
6. The research method and the result can be effectively popularized and expanded to similar problem solving to solve corresponding problems.
Drawings
FIG. 1 is a flow chart of an arrangement optimization method of a wind power plant multi-model wind driven generator.
FIG. 2 is the result of a calculation applied to an embodiment by the optimization method of the present invention.
Detailed Description
The following detailed description of the invention is provided in connection with the accompanying drawings:
examples
In the embodiment, the fan arrangement and selection optimization before the generator is built is carried out on 8 wind powers of a certain wind power plant. The alternative fans are of two factory models A (rated power is 1.5MW) and B (rated power is 2MW), and the fan installation height of each factory model is two (1.5MW is 65 meters and 80 meters in height, and 2MW is 80 meters and 90 meters in height), namely 4 fan models. The wind farm area is in the range of the abscissa [0,2000] (meter) and the ordinate [0,2000] (meter). The complex terrain is not considered in this embodiment. The number of the fans is 7, the safety distance between the fans is 5 times of the diameter of the wind wheel, namely 550 meters, and the annual minimum generated energy of the wind power plant is 8 MW. The optimization target is that the power consumption cost of the wind power plant is the lowest. The implementation steps are as follows:
1) according to the wind resource evaluation result and the topographic meteorological characteristics of the wind farm, carrying out initial model selection on the wind driven generator, determining a plurality of alternative models for model selection optimization, and reading in parameters such as relevant topographic terrain, meteorological phenomena and the like of the wind farm;
2) abstracting a complex constraint condition of an optimization problem according to site selection requirements, and relaxing the constraint condition into a penalty function item in a target function to enable an optimization result to meet safety distance constraints among fans and annual minimum power generation amount constraints of a wind field;
3) randomly generating an initial position matrix of the fans in the horizontal and vertical coordinate range of the wind power plant region, wherein each row of the matrix represents a fan position arrangement scheme, namely a chromosome, the row represents the number of chromosomes of a genetic algorithm, and each row of the matrix is subjected to binary coding;
4) determining a search space of a particle swarm algorithm according to a given alternative fan model, dividing all particles into M independent particle swarm subspaces according to a search area, wherein the number of the particles in each subspace is more than 3;
5) generating an initial model matrix and encoding, wherein the number of rows of the matrix represents the number of particles of the particle swarm algorithm, and each row of the matrix represents one particle (a fan model selection scheme);
6) in each subspace, randomly initializing the speed of all particles and the positions of all the particles in a search domain, calculating the fitness of each current particle, namely the power consumption cost of the current fan position and the type selection scheme, solving the individual optimal fitness of each particle and the global optimal fitness of all the particles, and taking the global optimal position in the subspace as the current group optimal position.
7) In each subspace, evolving the position and the speed of each particle according to the particle speed and the position evolution rule set in the particle swarm algorithm;
8) judging whether the maximum algebra set by the group-divided particle swarm algorithm is reached, if the maximum algebra set by the group-divided particle swarm algorithm is reached, stopping fan model optimization, comparing the optimal positions of the population in each subspace, selecting the optimal positions in the whole search space, taking the fitness of the optimal positions as the global optimal fitness of the particle swarm algorithm, and taking the fitness as the fitness of the current chromosome, otherwise, returning to the step 6);
9) according to the fitness of each chromosome, the global optimal fitness of a genetic algorithm, namely the global optimal value of fan position model selection, is solved;
10) judging whether the maximum iteration times of the genetic algorithm is reached, if so, outputting a chromosome corresponding to the global optimal fitness of the genetic algorithm as a fan position scheme, and taking the global optimal fitness of the particle swarm algorithm corresponding to the chromosome as a model selection scheme, so as to complete the configuration optimization of the multi-model wind driven generator, otherwise, performing the step 11);
11) and (5) taking all chromosomes as parent chromosome groups, carrying out intersection and mutation operations, calculating selection probability according to the fitness of the chromosomes, selecting to generate child chromosome groups, and returning to the step 5).
Calculating the fitness value of the optimal solution of the current generation fan model selection scheme, and calculating the individual fitness value of the current generation position, wherein the individual fitness calculation formula is as follows:
Figure BDA0001208313030000061
wherein: CoE is annual power generation cost, AEP is annual average power generation amount of wind power plant, CiIs the annual average cost of purchase of each fan, CO&MAnnual operation and maintenance costs of wind farms, ClandIs the annual average land occupation cost of the wind power plant CotherIs the annual average of other costs of the wind farm, PiIs the annual average power generation of each fan, and N is the total number of fans in the wind farm, which is 7 in this embodiment.
Further, the method for optimizing the site selection and the type selection of the wind driven generator suitable for the complex constraint conditions solves the optimization problem expressed as follows:
min CoP
Figure BDA0001208313030000062
wherein: e0Is the annual minimum power generation amount, DsIs the safety distance between the fans di,jIs the distance between the fans, xi，yiIs a two-dimensional coordinate of the position of the fan,and
Figure BDA0001208313030000064
respectively the upper and lower bounds of the two-dimensional coordinate of the fan.
And adding safety distance which must be satisfied between the fans under the nonlinear constraint condition as a penalty function item into an objective function of the genetic algorithm, wherein the objective function of the genetic algorithm is expressed as follows:
Figure BDA0001208313030000071
Figure BDA0001208313030000072
wherein:
Figure BDA0001208313030000073
Dsin this example 550 meters.
And (3) adding the minimum annual power generation amount of the wind field under the nonlinear constraint condition as a penalty function term into the objective function of the particle swarm algorithm, wherein the objective function of the particle swarm algorithm is expressed as:
min(CoP+C2e)
wherein:
Figure BDA0001208313030000075
E0is the annual minimum power generation of the whole wind farm, which in this example is 8 MW.
The invention relates to a site selection and type selection optimization method for a wind driven generator under complex constraint conditions, which mainly comprises the steps of initialization (including coding), calculation of the fitness of individuals in the current generation, generation (cross variation) of filial generations and the like. In each generation of fitness calculation of the genetic algorithm, an optimization algorithm process for the fan model is nested, and the optimization algorithm for the fan model is a grouped particle swarm algorithm. Fig. 1 is a specific flow of a wind turbine site selection and type selection optimization method suitable for complex constraint conditions. In the whole embodiment, the configuration optimization calculation of the multi-model wind driven generator is carried out according to the flow shown in fig. 1. FIG. 2 is a result of the configuration using the site selection and type selection optimization method of the wind turbine generator suitable for complex constraint conditions of the present invention. Assuming that the service life of the fan is 20 years, the calculation result of the kilowatt-hour cost is 0.5519 yuan/kilowatt hour, the generating efficiency of the fan under the influence of the wake flow is 0.9940, and the annual expected generating capacity of the wind power plant is 8.49 MW. The result of calculation by using the site selection and type selection optimization method of the wind driven generator suitable for complex constraint conditions shows that the wind turbine arrangement position fully utilizes the area of the wind power plant, the wind energy utilization rate is effectively improved, and the method is suitable for micro site selection of the wind power plant.

Claims (6)

1. A wind driven generator site selection and type selection optimization method suitable for complex constraint conditions is characterized by comprising the following steps:
1) according to the wind resource evaluation result and the topographic meteorological characteristics of the wind farm, carrying out initial model selection on the wind driven generator, determining a plurality of alternative models for model selection optimization, and reading in relevant topographic and meteorological parameters of the wind farm;
2) abstracting a complex constraint condition of an optimization problem according to site selection requirements, and relaxing the constraint condition into a penalty function item in a target function to enable an optimization result to meet safety distance constraints among fans and annual minimum power generation amount constraints of a wind field;
3) randomly generating an initial position matrix of the fans in the horizontal and vertical coordinate range of the wind power plant region, wherein each row of the matrix represents a fan position arrangement scheme, namely a chromosome, the row represents the number of chromosomes of a genetic algorithm, and each row of the matrix is subjected to binary coding;
4) generating an initial model matrix in a given alternative fan model and coding, wherein the number of matrix rows represents the particle number of a particle swarm algorithm, each row of the matrix represents a particle, namely a fan model selection scheme, and the speed and the position of the particle in a search domain are initialized randomly as an initial solution for fan model selection of a current chromosome;
5) calculating the fitness of each current particle, namely adopting the current fan position and the power consumption cost of the model selection scheme, and solving the individual optimal fitness of each particle and the global optimal fitness of all particles;
6) evolving the position and the speed of each particle according to the particle speed and the position evolution rule set in the particle swarm algorithm;
7) judging whether the set maximum algebra of the particle swarm algorithm is reached, if the set maximum algebra is reached, stopping fan model optimization, selecting the global optimal fitness of the particle swarm algorithm as the fitness of the current chromosome, and if not, returning to the step 5);
8) according to the fitness of each chromosome, the global optimal fitness of a genetic algorithm, namely the global optimal value of fan position model selection, is solved;
9) judging whether the maximum iteration times of the genetic algorithm is reached, if so, outputting a chromosome corresponding to the global optimal fitness of the genetic algorithm as a fan position scheme, and taking the global optimal fitness of the particle swarm algorithm corresponding to the chromosome as a model selection scheme, so as to complete the configuration optimization of the multi-model wind driven generator, otherwise, performing the step 10);
10) and (4) taking all chromosomes as parent chromosome groups, carrying out intersection and mutation operations, calculating selection probability according to the fitness of the chromosomes, selecting to generate child chromosome groups, and returning to the step 4).
2. The wind driven generator site selection and optimization method suitable for complex constraint conditions, according to claim 1, is characterized in that a genetic algorithm is used for selecting a fan position, a particle swarm algorithm is nested for fan model optimization, namely after the fan position is selected, the particle swarm algorithm is used for obtaining the optimal solution of the fan position in the mode selection as the fitness of the fan position; the optimization problem has nonlinear constraints, wherein the nonlinear constraints comprise the minimum distance between fans and the annual minimum power generation amount of a wind power plant; the nonlinear constraint conditions cause the problems of fan model selection and site selection optimization to be very complex and difficult to obtain the optimal solution.
3. The wind turbine generator site selection optimization method suitable for complex constraint conditions according to claim 1, wherein the individual fitness calculation value is calculated as follows: the individual fitness is embodied by calculating the reciprocal of the electricity consumption cost, the individual with the highest fitness is the maximum value of the reciprocal of the electricity consumption cost, namely the individual with the minimum value of the electricity consumption cost, and the calculation formula of the electricity consumption cost CoP is as follows:
Figure FDA0002224209260000021
wherein: CoE is annual power generation cost, AEP is annual average power generation amount of wind power plant, CiIs the annual average cost of purchase of each fan, CO&MAnnual operation and maintenance costs of wind farms, ClandIs the annual average land occupation cost of the wind power plant CotherIs the annual average of other costs of the wind farm, PiThe annual average generated energy of each fan, and N is the total number of the fans in the wind power plant.
4. The wind turbine site selection optimization method suitable for complex constraint conditions according to any one of claims 1, 2 or 3, characterized by relaxing an optimization problem P1 into an optimization problem P2, a nested optimization problem P3 in P2;
p1 represents the following:
min CoP
Figure FDA0002224209260000022
wherein: e0Is the annual minimum power generation amount, DsIs the safety distance between the fans di,jIs the distance between the fans, xi，yiIs a two-dimensional coordinate of the position of the fan,
Figure FDA0002224209260000023
and
Figure FDA0002224209260000024
respectively is the upper and lower boundaries of the two-dimensional coordinate of the fan;
p2 represents the following:
adding a nonlinear constraint condition which must meet a safety distance between fans as a penalty function item into an objective function of the genetic algorithm, and expressing the objective function of the genetic algorithm as follows:
Figure FDA0002224209260000025
wherein:
Figure FDA0002224209260000027
C1is a penalty factor;
p3 represents the following:
and adding the constraint condition of the minimum annual power generation amount of the wind field as a penalty function term into the objective function of the particle swarm algorithm, wherein the objective function of the particle swarm algorithm is expressed as follows:
min(CoP+C2e)
Figure FDA0002224209260000031
wherein:
Figure FDA0002224209260000032
C2is a penalty factor, E0Is the lowest annual energy production of the whole wind field.
5. The method for optimizing the site selection and the model selection of the wind driven generator suitable for the complex constraint conditions as claimed in claim 1, wherein the particle swarm algorithm is used for selecting the fan model combination of the specific position scheme, wherein the fan model combination comprises fans of the same model, of different cabin heights, of different models, of different cabin heights, or of different models, of different cabin heights, that is, if the fan of the same factory model is installed at different cabin heights, the fan is also considered to be of different models.
6. The wind driven generator site selection optimization method suitable for complex constraint conditions according to claim 1, characterized in that constraint conditions are added and solved according to requirements of an actual wind power plant, and the micro site selection result has higher wind energy utilization efficiency and better practicability.
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