CN114444784A

CN114444784A - Multi-target arrangement optimization method and system for wind power plant positions

Info

Publication number: CN114444784A
Application number: CN202210015874.9A
Authority: CN
Inventors: 葛铭纬; 曹立超; 李宝良
Original assignee: North China Electric Power University
Current assignee: North China Electric Power University
Priority date: 2022-01-07
Filing date: 2022-01-07
Publication date: 2022-05-06

Abstract

The invention relates to the technical field of micro site selection of wind power plants, and particularly provides a multi-objective arrangement optimization method and system for wind power plant positions, aiming at solving the problems that in the existing wind power plant arrangement optimization process, the optimization objective is single, and the influence of conditions such as wind conditions on the optimization process cannot be fully considered. For the purpose, the method applies a genetic algorithm, takes the target value of the first optimization target as the minimum and the target value of the second optimization target as the maximum as the optimization targets, carries out multi-objective optimization on the arrangement scheme of the wind turbine generators in the wind power plant, and obtains a Pareto front solution of the multi-objective optimization so as to determine the arrangement optimization scheme of the wind power plant. Through the configuration mode, the total output power of the wind power plant and the flow direction turbulence degree in front of the wind turbine generator are comprehensively considered in the optimization process of the arrangement scheme of the wind power plant, the flow direction turbulence degree in front of the wind turbine generator is reduced on the premise of ensuring the total output power of the wind power plant, the operation life of the wind power plant is prolonged, and the generating capacity of the full life cycle is improved.

Description

Multi-target arrangement optimization method and system for wind power plant positions

Technical Field

The invention relates to the technical field of micro site selection of wind power plants, and particularly provides a multi-target configuration optimization method and system for machine positions of a wind power plant.

Background

Wind energy is a pollution-free and renewable clean energy, and in order to increase the utilization of wind energy resources, the construction of onshore and offshore wind farms tends to be large-scale and basic, but at the same time, the wake effect of the wind farms is more serious. In recent years, research on optimization of wind farm layout has been increasing. In the research aiming at the micro site selection of the wind power plant, the maximum output power distribution scheme is obtained by mostly focusing on selecting a high-precision wake flow model or improving the searching precision and the calculating efficiency of an optimization algorithm. The main defects of the researches are that the optimization target is single, and most of the optimization targets are only that the maximum output power of the wind power plant is maximized. In addition, the setting of the conditions such as terrain environment, wind conditions and the like in the research process is also more ideal, so that the wind power plant arrangement optimization problem still has great promotion space.

Accordingly, there is a need in the art for a new wind farm layout optimization scheme that addresses the above-mentioned problems.

Disclosure of Invention

In order to overcome the defects, the invention is provided to solve or at least partially solve the problems that the influence of conditions such as wind conditions and flow direction turbulence on the optimization process cannot be comprehensively considered in the existing wind power plant arrangement optimization process and the optimization target is single.

In a first aspect, the invention provides a multi-objective arrangement optimization method for wind power plant stands, which comprises the following steps:

establishing an initialization population of a genetic algorithm according to a preset wind power plant region and the number of the determined wind power sets in the wind power plant, wherein the initialization population comprises a plurality of individuals, and each individual is a wind power set arrangement scheme in the wind power plant;

performing multi-objective optimization on the arrangement scheme of the wind turbine generators in the wind power plant based on a genetic algorithm and according to the initialized population by taking the minimum target value for controlling the first optimization target and the maximum target value for controlling the second optimization target as optimization targets;

when the first optimization target and the second optimization target reach convergence simultaneously, obtaining a Pareto front solution of the multi-objective optimization, wherein the Pareto front solution comprises the position of each wind generating set in the area of the wind power plant;

determining a configuration optimization scheme of the wind power plant according to the Pareto frontier solution;

the first optimization objective is to calculate the maximum value of the comprehensive flow direction turbulence degree in front of all wind turbines in the wind power plant under the arrangement scheme corresponding to each individual; and the second optimization target is to calculate the total output power of the wind power plant under the arrangement scheme corresponding to each individual.

In a technical solution of the above method for optimizing multi-objective arrangement of wind farm stands, the method further includes calculating a maximum value of flow direction turbulence degrees in flow direction turbulence degrees before all wind turbines in the wind farm under the arrangement scheme corresponding to each individual according to the following steps:

for each wind turbine in the wind power plant, acquiring the flow direction turbulence degree of the wind power plant at any position according to the additional flow direction turbulence degree generated by the upstream wind turbine at any position of the wind power plant and the method described by the following formula:

wherein the content of the first and second substances,

wind direction angle of theta_lFlow direction turbulence at (x, y, z) position in the wind farm; delta Iu_m(x, y, z) is the additional streamwise turbulence generated at (x, y, z) by the mth upstream wind turbine at the (x, y, z) location in the wind farm; n is a radical of_tThe number of wind generating sets in the wind power plant;

obtaining the flow direction turbulence degrees of a plurality of point positions in front of a hub of the current wind turbine generator according to the position of the current wind turbine generator and the flow direction turbulence degrees of any position of the wind power plant and the method of the following formula

And acquiring the maximum value of the flow direction turbulence degree of a plurality of point positions as the flow direction turbulence degree in front of the wind turbine generator i

Wherein the content of the first and second substances,

wind direction angle of theta_lIn time, the flow direction turbulence of the (x, y, z) point location; i is₀The atmospheric turbulence degree in front of a hub of the current wind turbine generator is obtained;

according to preset wind resource data and the flow direction turbulence degree in front of the hub of each wind turbine generator in different wind directions, and according to the method described by the following formula, obtaining the comprehensive flow direction turbulence degree in front of all the wind turbine generators in the individual:

wherein, I_w(i) The comprehensive flow direction turbulence degree before the wind turbine generator set i of the individual is obtained; f (theta)_l) Wind direction angle of theta_lThe wind frequency of the time; n is a radical of_θThe number of the wind direction angles is;

and acquiring the maximum value of the comprehensive flow direction turbulence degree in the comprehensive flow direction turbulence degrees in front of all the wind turbines according to the comprehensive flow direction turbulence degrees in front of all the wind turbines in the individual.

In one technical solution of the above method for optimizing multi-objective arrangement of wind farm stands, the method further includes calculating the total output power of the wind farm under the arrangement scheme corresponding to each individual according to the following steps:

for each wind turbine in the wind power plant, obtaining the average speed loss of the current wind turbine according to the average speed loss of the upstream wind turbine of the current wind turbine at the position of the current wind turbine and the method described by the following formula:

wherein, Delta U_iThe average speed loss of the ith wind turbine generator set is obtained; delta U_ijThe average speed loss of the jth wind turbine generator set at the ith wind turbine generator set is obtained; q. q.s_ijIs a binary variable, q is a binary variable, and q is a binary variable when and only when the current wind turbine i is in the wake generated by the jth upstream wind turbine_ij Other cases q 1_ij＝0；N_tThe number of wind generating sets in the wind power plant;

according to the average speed loss of the current wind turbine generator and the method described by the following formula, the wind speed of the current wind turbine generator in front of the hub under the preset wind condition is obtained:

U_i＝U₀-ΔU_i

wherein, U₀The free inflow wind speed at the height of the hub of the wind turbine generator is obtained; u shape_iThe method comprises the steps that the wind speed of the current wind turbine generator set in front of a hub under a preset wind condition is u_kThe wind direction angle of the wind condition is theta_l；

Acquiring the output power of the current wind turbine generator under the wind condition according to the wind speed of the current wind turbine generator before the hub and the power curve of the current wind turbine generator under the wind condition; the power curve comprises a corresponding relation between the wind speed in front of the hub and the output power of the wind turbine generator;

acquiring the total output power of the wind power plant under the current arrangement scheme of the wind power plant according to the output power and the wind direction and according to the method described by the following formula:

wherein, P_totalThe total output power of the wind power plant under the current arrangement scheme of the wind power plant; power (U)_i) The wind speed is u_kThe wind direction being θ_lThe output power of the ith wind turbine generator set is calculated; f (u)_k,θ_l) The wind speed is u_kWind direction angle of theta_lThe wind frequency of the time; n is a radical of_θThe number of the wind direction angles is; n is a radical of_uIs the number of wind speeds.

In one technical scheme of the multi-target arrangement optimization method for the wind power plant positions, the arrangement scheme of the wind power generation sets in each individual wind power plant meets a constraint condition, the constraint condition is that the distance between any two wind power generation sets in the wind power plant is larger than N times of the diameter of a wind wheel, and N is a positive integer larger than 1.

In one technical solution of the above method for optimizing multi-target arrangement of machine positions of a wind farm, the Pareto front solution further includes a final target value of the first optimization objective and a final target value of the second optimization objective, and the step of obtaining the Pareto front solution of multi-target optimization after the first optimization objective and the second optimization objective reach convergence simultaneously includes:

after the first optimization target and the second optimization target reach convergence at the same time for the first time, performing multi-objective optimization for multiple times, and acquiring a Pareto front solution obtained after the first optimization target and the second optimization target reach convergence at the same time in each multi-objective optimization;

if the Pareto front edge solutions of the multi-time multi-objective optimization are the same as the Pareto front edge solution obtained after the first simultaneous convergence, taking the Pareto front edge solution obtained after the first simultaneous convergence as a final Pareto front edge solution;

if at least one Pareto front solution in the Pareto front solutions of the multiple multi-objective optimization is different from the Pareto front solution obtained after the convergence is simultaneously achieved for the first time, selecting the Pareto front solution corresponding to the minimum final target value of the first optimization target or selecting the Pareto front solution corresponding to the maximum final target value of the second optimization target as the final Pareto front solution according to the final target value of the first optimization target and the final target value of the second optimization target in the Pareto front solutions.

In a second aspect, the present invention provides a system for optimizing multi-objective arrangement of wind farm stands, wherein the system comprises:

the method comprises the following steps that an initialization population establishing module is configured to establish an initialization population of a genetic algorithm according to a preset wind power plant region and the number of determined wind power sets in the wind power plant, wherein the initialization population comprises a plurality of individuals, and each individual is a distribution scheme of the wind power sets in the wind power plant;

a multi-objective optimization module configured to perform multi-objective optimization on the arrangement scheme of the wind turbine generators in the wind farm according to the initialized population based on a genetic algorithm with a target value for controlling a first optimization objective being minimum and a target value for controlling a second optimization objective being maximum as optimization objectives;

a Pareto front solution obtaining module configured to obtain a Pareto front solution of the multi-objective optimization after the first optimization objective and the second optimization objective reach convergence simultaneously, the Pareto front solution including a position of each wind turbine group within a region of the wind farm;

a configuration optimization scheme determination module configured to determine a configuration optimization scheme of the wind farm according to the Pareto frontier solution;

In one technical solution of the above multi-objective arrangement optimization system for wind farm stands, the system further includes a maximum value of flow direction turbulence acquisition module, where the maximum value of flow direction turbulence acquisition module includes:

the method comprises a wind power plant position flow direction turbulence degree obtaining unit, a wind power plant position flow direction turbulence degree obtaining unit and a wind power plant position flow direction turbulence degree obtaining unit, wherein the wind power plant position flow direction turbulence degree obtaining unit is configured to obtain flow direction turbulence degrees of any position of a wind power plant according to additional flow direction turbulence degrees generated by upstream wind power plants at any position of the wind power plant and a method described by the following formula:

wherein the content of the first and second substances,

wind direction angle of theta_lFlow direction turbulence at (x, y, z) position in the wind farm; delta Iu_m(x, y, z) is the additional streamwise turbulence generated at (x, y, z) by the mth upstream wind turbine at the (x, y, z) location in the wind farm; n is the number of the upstream units at the current wind turbine position in the wind power plant;

the device comprises a wind turbine generator hub front flow direction turbulence degree acquisition unit, a wind power plant control unit and a wind power plant control unit, wherein the wind turbine generator hub front flow direction turbulence degree acquisition unit is configured to acquire flow direction turbulence degrees of a plurality of point positions in front of a hub of a current wind turbine generator according to the current position of the wind turbine generator and the flow direction turbulence degrees of any position of the wind power plant and the method of the following formula

And acquiring the maximum value of the flow direction turbulence degrees of a plurality of point positions as the flow direction turbulence degree in front of the wind turbine generator i

Wherein the content of the first and second substances,

wind direction angle of theta_lIn time, the flow direction turbulence of the (x, y, z) point location; i is₀The current atmospheric turbulence in front of the hub of the wind turbine generator;

the wind turbine generator front flow direction turbulence degree obtaining unit is configured to obtain a comprehensive flow direction turbulence degree in front of all wind turbine generators in the individual according to preset wind resource data and flow direction turbulence degrees in front of hubs of the wind turbine generators in different wind directions and according to a method described by the following formula:

a maximum flow direction turbulence level obtaining unit configured to obtain a maximum value of the integrated flow direction turbulence levels among the integrated flow direction turbulence levels of all wind turbines in the individual according to the integrated flow direction turbulence levels of all wind turbines in the individual.

In one technical solution of the above multi-objective arrangement optimization system for wind farm stands, the system further includes a total output power obtaining module, where the total output power obtaining module includes:

an average speed loss obtaining unit, configured to obtain, for each wind turbine in the wind farm, an average speed loss of a current wind turbine according to an average speed loss of an upstream wind turbine of the current wind turbine at a position where the current wind turbine is located, according to a method described by the following formula:

the wind speed obtaining unit is configured to obtain the wind speed of the current wind turbine in front of the hub under the preset wind condition according to the average speed loss of the current wind turbine and the method described by the following formula:

U_i＝U₀-ΔU_i

wherein, U₀The free inflow wind speed at the height of the hub of the wind turbine generator is set; u shape_iThe method comprises the steps that the wind speed of the current wind turbine generator set in front of a hub under a preset wind condition is u_kThe wind direction angle of the wind condition is theta_l；

The wind turbine generator output power acquisition unit is configured to acquire the output power of the current wind turbine generator under the wind condition according to the wind speed of the current wind turbine generator before the hub under the wind condition and the power curve of the current wind turbine generator; the power curve comprises a corresponding relation between the wind speed in front of the hub and the output power of the wind turbine generator;

a wind farm total output power obtaining unit configured to obtain, according to the output power and the wind direction, a total output power of the wind farm in the current wind farm arrangement scheme according to the following method:

In one technical scheme of the multi-target arrangement optimization system of the wind power plant positions, the arrangement scheme of the wind power generation sets in each individual wind power plant meets a constraint condition, the constraint condition is that the distance between any two wind power generation sets in the wind power plant is greater than N times of the diameter of a wind wheel, and N is a positive integer greater than 1.

In an embodiment of the above system for optimizing multi-target arrangement of a wind farm stand, the Pareto front solution further includes a final target value of the first optimization target and a final target value of the second optimization target, and the Pareto front solution obtaining module is further configured to perform the following steps:

One or more technical schemes of the invention at least have one or more of the following beneficial effects:

in the technical scheme, a genetic algorithm is applied according to the established initialization population, the target value of the first optimization target is controlled to be minimum, the target value of the second optimization target is controlled to be maximum, the arrangement scheme of the wind turbine generator in the wind power plant is subjected to multi-objective optimization, when the first optimization target and the second optimization target reach convergence simultaneously, a Pareto front solution of the multi-objective optimization is obtained, and the arrangement optimization scheme of the wind power plant is determined according to the Pareto front solution. Through the configuration mode, in the optimization process of the arrangement scheme of the wind turbine generators in the wind power plant, the requirements of improving the total output power of the wind power plant and reducing the maximum value of the flow direction turbulence degree in front of the wind turbine generators are comprehensively considered, the maximum value of the flow direction turbulence degree in front of the wind turbine generators in the wind power plant can be reduced on the premise of ensuring the total output power of the wind power plant, the operation life of the wind power plant is prolonged, and the power generation capacity of the whole life cycle of the wind power plant is improved.

Drawings

The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are for illustrative purposes only and are not intended to constitute a limitation on the scope of the present invention. Wherein:

FIG. 1 is a flow chart illustrating the main steps of a method for optimizing the multi-objective arrangement of wind farm stands according to an embodiment of the present invention;

FIG. 2 is a schematic representation of probability distributions of wind speed and wind direction according to one implementation of an embodiment of the present invention;

FIG. 3 is a schematic diagram of a Pareto front edge solution according to one implementation of an embodiment of the invention;

FIG. 4 is a schematic diagram illustrating an arrangement scheme of a wind farm corresponding to point A in FIG. 3 according to an embodiment of the present invention;

FIG. 5 is a schematic diagram illustrating an arrangement scheme of a wind farm corresponding to a point B in FIG. 3 according to an embodiment of the present invention;

FIG. 6 is a schematic diagram illustrating a comparison of the overall flow direction turbulence levels of each wind turbine in front of the two wind farm arrangements of FIGS. 4 and 5 according to an embodiment of the present invention;

FIG. 7 is a schematic diagram illustrating the relationship between the front flow turbulence and the time accumulation of the low-efficiency unit in the arrangement of the wind farm of FIGS. 4 and 5 according to an embodiment of the present invention;

FIG. 8 is a main structural block diagram of a multi-objective arrangement optimization method system of a wind farm machine position according to an embodiment of the invention.

Detailed Description

Some embodiments of the invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.

In the description of the present invention, a "module" or "processor" may include hardware, software, or a combination of both. A module may comprise hardware circuitry, various suitable sensors, communication ports, memory, may comprise software components such as program code, or may be a combination of software and hardware. The processor may be a central processing unit, a microprocessor, a digital signal processor, or any other suitable processor. The processor has data and/or signal processing functionality. The processor may be implemented in software, hardware, or a combination thereof. Non-transitory computer readable storage media include any suitable medium that can store program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random-access memory, and the like. The term "a and/or B" denotes all possible combinations of a and B, such as a alone, B alone or a and B. The term "at least one A or B" or "at least one of A and B" means similar to "A and/or B" and may include only A, only B, or both A and B. The singular forms "a", "an" and "the" may include the plural forms as well.

Referring to fig. 1, fig. 1 is a flow chart illustrating main steps of a method for optimizing multi-objective arrangement of wind farm stands according to an embodiment of the invention. As shown in fig. 1, the method for optimizing multi-objective arrangement of machine positions in a wind farm in the embodiment of the present invention mainly includes the following steps S101 to S104.

Step S101: establishing an initialization population of a genetic algorithm according to the number of the wind generation sets determined in the preset wind power plant region and the wind power plant, wherein the initialization population comprises a plurality of individuals, and each individual is a distribution scheme of the wind generation sets in the wind power plant.

In this embodiment, the initialized population of the genetic algorithm may be established according to the geographical location information of the area where the wind farm is to be built and the number Nt of wind turbines determined in the wind farm.

In one embodiment, the evaluation result can be obtained according to the installed capacity requirement of the wind power plant to be built and the wind resource evaluation data, the model of the suitable wind turbine generator is selected, and the number Nt of the wind turbine generator is determined.

In one embodiment, initial coordinates of wind turbines in a wind farm can be randomly generated, an initialization population is established in a real number coding mode, the initialization population comprises a plurality of individuals, each individual is a 2Nt vector, namely the number of the wind turbines in the wind farm is Nt, the sum of horizontal and vertical coordinates of the wind turbines is 2Nt, the individuals are represented by one vector, elements from 1 to Nt in the vector refer to the horizontal coordinate, and elements from (Nt +1) to 2Nt refer to the vertical coordinate. Each individual is a wind power plant arrangement scheme. The real number coding refers to a coding mode that each gene value of an individual is represented by floating point numbers in a certain range, and the coding length of the individual is equal to the number of decision variables.

In one embodiment, the Genetic Algorithm (Genetic Algorithm) may be the NSGA-II Algorithm. The NSGA-II algorithm is a multi-target genetic algorithm.

Step S102: performing multi-objective optimization on the arrangement scheme of the wind turbine generators in the wind power plant based on a genetic algorithm and according to the initialized population by taking the minimum target value for controlling the first optimization target and the maximum target value for controlling the second optimization target as optimization targets; the first optimization objective is to calculate the maximum value of the comprehensive flow direction turbulence degree in front of all wind turbines in the wind power plant under the arrangement scheme corresponding to each individual; the second optimization objective is to calculate the total output power of the wind farm under the arrangement scheme corresponding to each individual.

In this embodiment, the maximum value of the turbulence in the flow direction in front of all the wind turbine generators in the wind farm under the arrangement scheme corresponding to each individual is calculated as a first optimization target, the total output power of the wind farm under the arrangement scheme corresponding to each individual is calculated as a second optimization target, the target value of the first optimization target is controlled to be minimum, the target value of the second optimization target is controlled to be maximum, and the multi-objective optimization is performed on the arrangement scheme of the wind turbine generators in the wind farm based on the genetic algorithm and according to the initialized population.

In one embodiment, constraint conditions may be set for multi-objective optimization of the arrangement schemes of the wind turbine generators in the wind farm, and the arrangement scheme of the wind turbine generators in the wind farm in each individual needs to satisfy the constraint conditions in the process of the multi-objective optimization. The constraint condition may be that the distance between any two wind turbines in the wind farm is greater than N times of the diameter of the wind wheel, where N is a positive integer greater than 1.

In one embodiment, the constraint may be that the distance between any two wind turbines in the wind farm is greater than 5 times the diameter of the wind rotor.

As an example, the wind farm scale is 2000m × 2000m, and the constraints can be expressed by the following equations (1) - (4):

0≤x_i≤2000，0≤y_i≤2000 (2)

constraint:min(g_R(X))＞0 (4)

wherein x is_i、x_kRespectively the abscissa, y, of the ith and kth wind turbine generators in the wind farm_i、y_kRespectively the ordinate of the ith wind turbine generator and the kth wind turbine generator in the wind power plant, D is the diameter of the wind wheel of the wind turbine generator, g_R(X) is the difference value between the square value of the distance between any two wind turbine sets in the wind power plant and the square value of 5D of the diameter of the wind wheel which is 5 times of the wind wheel, and X is the coordinate combination (X) of any two wind turbine sets_i，y_i；x_k，y_k) And R represents the R-th difference.

Step S103: and when the first optimization target and the second optimization target reach convergence simultaneously, obtaining a Pareto front solution of the multi-objective optimization, wherein the Pareto front solution comprises the position of each wind generating set in the region of the wind power plant.

In this embodiment, after the first optimization objective and the second optimization objective reach convergence simultaneously, a Pareto front solution of the multi-objective optimization may be obtained, where the Pareto front solution includes a position of each wind turbine in the wind electric field region. The condition that the first optimization target and the second optimization target simultaneously reach convergence is that a variation of the target value of the first optimization target and the target value of the second optimization target is less than or equal to a convergence threshold, and a person skilled in the art can flexibly set the convergence threshold according to actual application requirements. The Pareto frontier solution refers to an objective function value corresponding to a Pareto optimal solution obtained through multi-objective optimization.

Step S104: and determining a configuration optimization scheme of the wind power plant according to the Pareto frontier solution.

In this embodiment, the arrangement optimization scheme of the wind power plant may be determined according to the position of each wind turbine generator in the wind electric field area included in the Pareto front solution.

Based on the steps S101 to S104, the method can perform multi-objective optimization on the arrangement scheme of wind turbine generators in the wind farm by using a genetic algorithm according to the established initialization population and using the minimum target value of the first optimization objective and the maximum target value of the second optimization objective as optimization objectives, obtain a Pareto front solution of the multi-objective optimization after the first optimization objective and the second optimization objective converge at the same time, and determine the arrangement optimization scheme of the wind farm according to the Pareto front solution. Through the configuration mode, in the optimization process of the arrangement scheme of the wind turbine generators in the wind power plant, the requirements of improving the total output power of the wind power plant and reducing the maximum value of the flow direction turbulence degree in front of the wind turbine generators are comprehensively considered, the maximum value of the flow direction turbulence degree in front of the wind turbine generators in the wind power plant can be reduced on the premise of ensuring the total output power of the wind power plant, the operation life of the wind power plant is prolonged, and the power generation capacity of the whole life cycle of the wind power plant is improved.

Step S102 and step S103 will be further described below.

In an implementation manner of the embodiment of the present invention, step S102 may further include calculating a maximum value of the total flow direction turbulence levels in front of all wind turbines in the wind farm under the arrangement scheme corresponding to each individual according to the following steps:

step S1021: aiming at each wind turbine in the wind power plant, according to the additional flow direction turbulence generated by the upstream wind turbine at any position of the wind power plant, and according to the method described in the formula (5), obtaining the flow direction turbulence at any position of the wind power plant:

wherein, Delta I_n(x, y, z) is the wind direction angle theta_lFlow direction turbulence at (x, y, z) position in the wind farm; delta Iu_m(x, y, z) is the additional streamwise turbulence generated at (x, y, z) by the mth upstream wind turbine at the (x, y, z) location in the wind farm.

In this embodiment, the three-dimensional wake flow additional flow direction turbulence degree model of the wind turbine, disclosed in patent application No. 202011102957.9 and publication No. CN112347611A, entitled far-field wake flow direction turbulence degree calculation method for a wind turbine, may be used to calculate the flow direction turbulence degree of the wind turbine, and the three-dimensional wake flow additional flow direction turbulence degree model of the wind turbine is determined according to the following formulas (6) to (13):

σ_y＝k×x+εD (9)

wherein, Delta I_uFor additional flow direction turbulence;

maximum additional flow direction turbulence; r is the linear distance from any position coordinate in the wake flow cross section to the central line of the hub;

the radial distance from the center line of the hub for the position of the maximum value of the turbulence of the additional flow direction; delta (r) is a correction function, and the asymmetry of the additional flow direction turbulence in the vertical direction is corrected; sigma_TThe standard deviation when the additional flow direction turbulence peak value is in Gaussian distribution; c_TThe thrust coefficient of the wind turbine generator is shown; i is_aThe ambient turbulence level at the hub height of the wind turbine generator; i is₀The atmospheric turbulence degree in front of a hub of the current wind turbine generator is obtained; x is the distance between the upstream and downstream wind turbine generators; d is the diameter of the wind wheel of the wind turbine generator; sigma_yThe standard deviation of the wake velocity loss profile in the horizontal plane; k is a wake expansion coefficient; epsilon is the standard deviation of the wake velocity loss profile at the position of the wind wheel; i is₀ ^hThe intensity of the atmospheric turbulence at the height h of the hub of the wind turbine generator is shown; alpha is an azimuth angle corresponding to the correction amount radial peak position (the y direction represents 0 degree); z is the vertical distance from the ground; z is a radical of_hThe height of the hub of the wind turbine generator is set; k is a radical of₁The weight distribution represents the weight change of the correction function at different positions in the wake section.

Studies have shown that when predicting the streamwise turbulence in a given wind turbine wake, the influence of neighboring upstream wind turbines on streamwise turbulence is greatest. Therefore, in the present embodiment, for the flow direction turbulence at any point in the wind farm, only the influence of the maximum additional flow direction turbulence generated by the latest upstream wind turbine is considered, and the flow direction turbulence at the current position of the wind turbine is obtained by adding the generated maximum additional flow direction turbulence according to the formula (5), which is called as max superposition method.

Step S1022: obtaining the flow direction turbulence degrees of a plurality of point positions in front of a hub of the current wind turbine generator according to the position of the current wind turbine generator and the flow direction turbulence degrees of any position of the wind power plant and the method shown in the formula (14)

Wherein the content of the first and second substances,

wind direction angle of theta_lIn time, the flow direction turbulence of the (x, y, z) point location; i is₀The atmospheric turbulence in front of the hub of the current wind turbine generator is adopted.

In this embodiment, the flow direction turbulence degrees of a plurality of points in front of the hub of the current wind turbine generator can be calculated and obtained according to the formula (14) according to the flow direction turbulence degrees of the position of the wind turbine generator and any position of the wind farm, and the maximum value of the flow direction turbulence degrees is obtained and used as the flow direction turbulence degree in front of the wind turbine generator.

Step S1023: according to preset wind resource data and the flow direction turbulence degree in front of the hub of each wind turbine generator in different wind directions, and the method described by the following formula (15), obtaining the comprehensive flow direction turbulence degree in front of all the wind turbine generators in an individual:

wherein, I_w(i) The flow direction turbulence degree of the wind turbine generator set in front of the individual wind turbine generator set i is obtained; f (theta)_l) Wind direction angle of theta_lThe wind frequency of the time; n is a radical of_θThe number of wind direction angles.

In this embodiment, the flow direction turbulence levels of all wind turbines in an individual can be calculated according to the formula (15) according to preset wind resource data and the flow direction turbulence levels in front of the hubs of each wind turbine in different wind directions. Wherein the wind resource data comprises a wind direction angle.

Step S1024: and acquiring the maximum value of the comprehensive flow direction turbulence degree in the comprehensive flow direction turbulence degrees in front of all the wind power generation sets according to the comprehensive flow direction turbulence degrees in front of all the wind power generation sets in the individual.

In the present embodiment, the maximum value of the flow direction turbulence levels among all the wind turbine generators may be acquired according to the flow direction turbulence levels before all the wind turbine generators obtained in step S1023.

In an implementation manner of the embodiment of the present invention, step S102 may further include the following steps of calculating the total output power of the wind farm under the arrangement scheme corresponding to each individual:

step S1025: for each wind turbine in the wind power plant, obtaining the average speed loss of the current wind turbine according to the average speed loss of the upstream wind turbine of the current wind turbine at the position of the current wind turbine and the method shown in the formula (16):

wherein, Delta U_iThe average speed loss of the ith wind turbine generator set is obtained; delta U_ijThe average speed loss of the jth wind turbine generator set at the ith wind turbine generator set is obtained; q. q.s_ijIn the form of a binary variable, the value of the variable,q if and only if the current wind turbine i is in the wake generated by the jth upstream wind turbine_ij Other cases q 1_ij＝0。

In the embodiment, the average speed loss of the wind turbine generator can be obtained according to the wake flow two-dimensional analytic model of the wind turbine generator. The two-dimensional analysis model of the wind turbine generator is shown as a formula (17):

wherein, Delta U is the average speed loss of the wind turbine generator, U_∞Is free inflow wind speed, k is wake expansion coefficient, x is flow direction distance between upstream and downstream wind turbine generator sets, r₀Is the radius of the wind wheel of the wind turbine generator, r₁The radial distance from any point of the wake area to the center of the wind wheel.

In a wind farm, one wind turbine generator is usually located in the wake generated by a plurality of wind turbine generators on the upstream, the average speed loss of the wind turbine generators on the downstream can be obtained by a speed loss superposition method, and in the embodiment, the speed loss of the ith wind turbine generator can be calculated according to the method shown in the formula (17).

In one embodiment, since the wake expansion rate may be affected by the flow direction turbulence before the wind turbine, the flow direction turbulence before the wind turbine i may be obtained according to step S1023, and the wake expansion coefficient may be corrected according to the following formula (18):

k＝2*(0.3837I_w(i)+0.003678) (18)

wherein k is_wiThe coefficient is expanded for the corrected wake.

Step S1026: according to the average speed loss of the current wind turbine generator and the method shown in the formula (19), the wind speed of the current wind turbine generator in front of the hub under the preset wind condition is obtained:

U_i＝U₀-ΔU_i (19)

wherein, U₀The free inflow wind speed at the height of the hub of the wind turbine generator is obtained; u shape_iThe wind speed of the wind condition is u_kWind direction angle of wind condition is theta_l。

In this embodiment, the wind speed of the current wind turbine in front of the hub under the preset wind condition may be calculated according to the average speed loss of the current wind turbine and the formula (19).

Step S1027: acquiring the output power of the current wind turbine generator under the wind condition according to the wind speed of the current wind turbine generator before the hub and the power curve of the current wind turbine generator under the wind condition; the power curve comprises the corresponding relation between the wind speed in front of the hub and the output power of the wind turbine generator.

In this embodiment, the output power of the current wind turbine generator under the corresponding wind condition can be obtained according to the wind speed before the hub of the current wind turbine generator under the corresponding wind condition and the power curve of the current wind turbine generator.

Step S1028: according to the output power and the wind direction and the method shown in the formula (20), acquiring the total output power of the wind power plant under the arrangement scheme of the current wind power plant:

wherein, P_totalThe total output power of the wind power plant under the current arrangement scheme of the wind power plant; power (U)_i) The wind speed is u_kThe wind direction being θ_lThe output power of the ith wind turbine generator set is calculated; f (u)_k,θ_l) The wind speed is u_kWind direction angle of theta_lThe wind frequency of the time-inflow wind condition; n is a radical of_θThe number of wind direction angles; n is a radical of_uIs the number of wind speeds.

In this embodiment, the possible wind direction angle intervals in the wind farm may be divided to obtain N_θAnd taking the middle value of each wind direction angle sub-interval as the wind direction angle of the wind direction angle sub-interval. Possible wind speed intervals in the wind farm can be divided to obtain N_uEach wind speed sub-interval and each wind speed sub-intervalIs taken as the wind speed of the wind speed sub-interval. The total output power of the wind farm under the current wind farm layout may be calculated according to the method shown in equation (20). The wind frequency is the percentage of the occurrence frequency of a certain wind condition (wind direction angle + wind speed) to the total observation statistics frequency.

In an implementation manner of the embodiment of the present invention, the Pareto front solution may further include a final target value of the first optimization objective and a final target value of the second optimization objective, and the step S103 may further include:

step S1031: when the first optimization target and the second optimization target simultaneously reach convergence for the first time, performing multi-objective optimization for multiple times, and acquiring a Pareto front solution obtained after the first optimization target and the second optimization target simultaneously reach convergence in each multi-objective optimization;

step S1032: if the Pareto front edge solutions of the multi-time multi-objective optimization are the same as the Pareto front edge solutions obtained after the convergence is simultaneously achieved for the first time, the Pareto front edge solutions obtained after the convergence is simultaneously achieved for the first time are used as final Pareto front edge solutions;

step S1033: and if at least one Pareto front solution in the Pareto front solutions of the multi-objective optimization is different from the Pareto front solution obtained after the convergence is simultaneously reached for the first time, selecting the Pareto front solution corresponding to the minimum final target value of the first optimization target or selecting the Pareto front solution corresponding to the maximum final target value of the second optimization target as the final Pareto front solution according to the final target value of the first optimization target and the final target value of the second optimization target in the Pareto front solutions.

In the embodiment, after the first optimization objective and the second optimization objective reach convergence simultaneously, multi-objective optimization can be performed for multiple times, and if Pareto front solutions obtained by the multi-objective optimization for multiple times are the same, the Pareto front solution obtained by the first simultaneous convergence can be used as a final Pareto front solution; if at least one Pareto front solution in the Pareto front solutions obtained by multiple times of multi-objective optimization is different from the Pareto front solution obtained after convergence is reached for the first time, the Pareto front solution corresponding to the minimum first optimization target final target value or the Pareto front solution corresponding to the maximum second optimization target final target value can be selected according to the first optimization target final target value and the second optimization target final target value in the Pareto front solutions to serve as the final Pareto front solution.

In one embodiment, after the first optimization goal and the second optimization goal reach convergence simultaneously, the evolution of the 500 generations of genetic algorithms is maintained to ensure that the first optimization goal and the second optimization goal reach a stable convergence state, and a Pareto front solution at this time is obtained.

In one embodiment, since the genetic algorithm has randomness, the multi-objective optimization may be performed 5 times, and a Pareto front solution corresponding to the final target value of the smallest first optimization objective or a Pareto front solution corresponding to the final target value of the largest second optimization objective is selected as the final Pareto front solution.

For an example, 39 wind turbines are installed in a square area with a side length of 2000m, basic parameters used in the arrangement process of the wind power plant are shown in table 1, and a power curve of the wind turbines can be represented by formula (21):

P＝0.3u³ (21)

wherein, P is the output power of the wind turbine generator, and u is the inflow wind speed.

Table 1: basic parameters used in the wind farm configuration process

Wind conditions of multiple wind directions and multiple wind speeds are selected, and the wind speed in each wind direction can be 8m/s, 12m/s and 17 m/s. Referring to fig. 2, fig. 2 is a schematic diagram of probability distributions of wind speeds and wind directions according to an embodiment of the present invention, the abscissa of fig. 2 represents a wind direction angle, the ordinate represents a wind frequency of wind occurrence, and fig. 2 shows the wind frequency of wind occurrence at each wind speed for each wind direction. For convenience of comparison, the atmospheric turbulence intensity under all wind speeds and wind directions is set to be 10%. The constraint condition is that the safety distance between any two wind generating sets in the wind power plant must be more than 5 times of the diameter of the wind wheel. In this example, flat terrain is selected, without considering the effects of complex terrain. The multi-objective optimization is performed through the methods described in steps S101 to S103 in the foregoing method embodiments, and a Pareto frontier solution is obtained as shown in fig. 3, fig. 3 is a schematic diagram of a Pareto frontier solution according to an embodiment of the present invention, an abscissa of fig. 3 represents a maximum value of a flow direction turbulence degree, and an ordinate represents a total output power, where a point B in fig. 3 is an arrangement scheme in which the total output power is the same as that in the arrangement scheme of the point a and the maximum flow direction turbulence degree before a unit is different. Points a and B with the same total output power of the wind farm may be selected from fig. 3 for comparison. Referring to fig. 4 and 5, fig. 4 is a schematic diagram of an arrangement scheme of a wind farm corresponding to a point a in fig. 3 in an embodiment according to an embodiment of the present invention, fig. 5 is a schematic diagram of an arrangement scheme of a wind farm corresponding to a point B in fig. 3 in an embodiment according to an embodiment of the present invention, abscissa of fig. 4 and abscissa of fig. 5 both represent abscissa of arrangement of the wind farm, and ordinate both represent ordinate of arrangement of the wind farm, and flow direction turbulence degrees of the two arrangement schemes are compared in combination with the arrangement schemes of the wind farm corresponding to the point a and the point B. Referring to fig. 6 and 7, fig. 6 is a schematic diagram illustrating a comparison of the comprehensive flow direction turbulence degree of each wind turbine in front of each wind turbine in the arrangement schemes of the two wind farms of fig. 4 and 5 according to an embodiment of the present invention, where the abscissa of fig. 6 represents the number of the wind turbine, and the ordinate represents the maximum flow direction turbulence degree of the wind turbine in front of the wind turbine; FIG. 7 is a schematic diagram of the relationship between the flow direction turbulence before the low-efficiency unit and the time accumulation in the arrangement of the wind farm shown in FIGS. 4 and 5 according to an embodiment of the present invention, wherein the abscissa of FIG. 7 represents the accumulation time and the ordinate represents the flow direction turbulence. As can be seen from fig. 6, although the total output power of the arrangement scheme at the point a and the total output power of the arrangement scheme at the point B are the same, the turbulence in the flow direction before each wind turbine in the arrangement scheme at the point a is more uniform than that in the arrangement scheme at the point B. Meanwhile, the maximum integrated flow turbulence degree of the arrangement scheme at the point A is lower than 11.68%, and the maximum integrated flow turbulence degree of the arrangement scheme at the point B can reach 12.565%. Therefore, the wind turbine generator under the arrangement scheme of the point A is less subjected to fatigue load, and accordingly, the service life of the wind turbine generator is longer. On the premise that the total output power is the same, the arrangement scheme of the points A can improve the generating capacity of the wind power plant in the whole life cycle. In the arrangement scheme of the points A and B, one wind turbine generator is subjected to the maximum comprehensive flow direction turbulence degree and is called as an inefficient wind turbine generator. The flow direction turbulence levels of the low-efficiency wind turbine in each wind direction under the two arrangement schemes are shown in fig. 7, and the rain flow counting method is used for converting the flow direction turbulence levels of the low-efficiency wind turbine in each wind direction into an accumulated relation between the flow direction turbulence levels and time before the low-efficiency wind turbine. As can be seen from fig. 7, in the time period of 0-0.5T, the flow direction turbulence degree of the low-efficiency wind turbine generator in the arrangement scheme of point a is much lower than that of the low-efficiency wind turbine generator in the arrangement scheme of point B, and the maximum reduction reaches 20%. In other time periods, the flow direction turbulence degree of the low-efficiency wind turbine generators under the two arrangement schemes is basically equal, so that the low-efficiency wind turbine generators under the arrangement scheme of the point A are safer, and the service life is longer.

It should be noted that, although the foregoing embodiments describe each step in a specific sequence, those skilled in the art will understand that, in order to achieve the effect of the present invention, different steps do not necessarily need to be executed in such a sequence, and they may be executed simultaneously (in parallel) or in other sequences, and these changes are all within the protection scope of the present invention.

Further, the invention also provides a multi-target configuration optimization system of the wind power plant machine positions.

Referring to fig. 8, fig. 8 is a main structural block diagram of a multi-objective arrangement optimization system of a wind farm machine position according to an embodiment of the invention. As shown in fig. 8, the multi-objective arrangement optimization system for a wind farm position in the embodiment of the present invention may include an initialized population establishing module, a multi-objective optimization module, a Pareto leading edge solution obtaining module, and an arrangement optimization scheme determining module. In this embodiment, the initialized population establishing module may be configured to establish an initialized population of the genetic algorithm according to a preset region of the wind farm and the number of the wind turbines determined in the wind farm, where the initialized population includes a plurality of individuals, and each individual is a configuration scheme of the wind turbines in the wind farm. The multi-objective optimization module may be configured to perform multi-objective optimization on the arrangement scheme of the wind turbines in the wind farm based on a genetic algorithm and according to the initialized population, with the target value for controlling the first optimization objective being the smallest and the target value for the second optimization objective being the largest as optimization objectives. The Pareto front solution obtaining module may be configured to obtain a multi-objective optimized Pareto front solution after the first optimization objective and the second optimization objective reach convergence simultaneously, the Pareto front solution including a position of each wind turbine group within a region of the wind farm. The configuration optimization scheme determination module may be configured to determine a configuration optimization scheme for the wind farm according to a Pareto frontier solution. The first optimization objective is to calculate the maximum value of the comprehensive flow direction turbulence degree in front of all wind turbines in the wind power plant under the arrangement scheme corresponding to each individual; the second optimization objective is to calculate the total output power of the wind farm under the arrangement scheme corresponding to each individual.

In one embodiment, the multi-objective arrangement optimization system for wind farm machine positions may further include a maximum flow direction turbulence level acquisition module, and the maximum flow direction turbulence level acquisition module may include an additional flow direction turbulence level acquisition unit at the position of the wind turbine, a flow direction turbulence level acquisition unit in front of the hub of the wind turbine, and a flow direction turbulence level acquisition unit in front of all the wind turbines. In this embodiment, the additional flow direction turbulence level obtaining unit at the position of the wind turbine may be configured to obtain, for each wind turbine in the wind farm, the flow direction turbulence level at any position of the wind farm according to the additional flow direction turbulence level generated by the upstream wind turbine at any position of the wind farm and according to the method described in the following formula (5):

wherein the content of the first and second substances,

wind direction angle of theta_lFlow direction turbulence at (x, y, z) position in the wind farm; delta Iu_m(x,y, z) is the additional streamwise turbulence generated at (x, y, z) by the mth upstream wind turbine at the (x, y, z) location in the wind farm; and N is the number of the upstream units at the current wind turbine position in the wind power plant. The obtaining unit for the flow direction turbulence in front of the hub of the wind turbine generator may be configured to obtain the flow direction turbulence of a plurality of points at the hub height of the current wind turbine generator according to the current position of the wind turbine generator and the flow direction turbulence at any position of the wind farm, and according to the method described in the following formula (14)

Wherein the content of the first and second substances,

wind direction angle of theta_lIn time, the flow direction turbulence of the (x, y, z) point location; i is₀The atmospheric turbulence in front of the hub of the current wind turbine generator is adopted. The front flow direction turbulence degree obtaining unit of all the wind turbines may be configured to obtain the comprehensive flow direction turbulence degree of all the wind turbines in an individual according to preset wind resource data and the flow direction turbulence degree in front of the hub of each wind turbine in different wind directions according to the following method (15):

wherein, I_w(i) The flow direction turbulence degree of the wind turbine generator set in front of the individual wind turbine generator set i is obtained; f (theta)_l) Wind direction angle of theta_lThe wind frequency of the time; n is a radical of_θThe number of wind direction angles. The flow direction turbulence maximum value acquiring unit may be configured to acquire the flow direction turbulence maximum value in accordance with the average value of the flow direction turbulenceAnd acquiring the maximum value of the comprehensive flow direction turbulence degree in the comprehensive flow direction turbulence degrees in front of all the wind power units.

In one embodiment, the system for optimizing multi-target arrangement of wind power plant stands may further include a total output power obtaining module, and the total output power obtaining module may include an average speed loss obtaining unit, a hub front wind speed obtaining unit, a wind turbine output power obtaining unit, and a wind power plant total output power obtaining unit. In this embodiment, the average speed loss obtaining unit may be configured to obtain, for each wind turbine in the wind farm, the average speed loss of the current wind turbine according to the average speed loss of an upstream wind turbine of the current wind turbine at the position of the current wind turbine, and according to a method described in the following formula (16):

wherein, Delta U_iThe average speed loss of the ith wind turbine generator set is obtained; delta U_ijThe average speed loss of the jth wind turbine generator set at the ith wind turbine generator set is obtained; q. q.s_ijIs a binary variable, q is a binary variable if and only if the current wind turbine i is in the wake generated by the jth upstream wind turbine_ij Other cases q 1_ij＝0；N_tThe number of wind turbines in the wind farm. The wind speed before hub acquiring unit may be configured to acquire the wind speed before the hub of the current wind turbine under the preset wind condition according to the average speed loss of the current wind turbine and according to the following method (19):

U_i＝U₀-ΔU_i (19)

wherein, U₀The free inflow wind speed at the height of the hub of the wind turbine generator is obtained; u shape_iThe wind speed of the wind condition is u_kWind direction angle of wind condition is theta_l. The wind turbine output power obtaining unit may be configured to obtain the wind speed of the current wind turbine in front of the hub according to the wind conditionsAcquiring the output power of the current wind turbine generator under the wind condition according to the power curve of the current wind turbine generator; the power curve comprises the corresponding relation between the wind speed in front of the hub and the output power of the wind turbine generator. The wind farm total output power obtaining unit may be configured to obtain the total output power of the wind farm under the arrangement scheme of the current wind farm according to the output power and the wind direction and according to a method described in the following formula (20):

wherein, P_totalThe total output power of the wind power plant under the current arrangement scheme of the wind power plant; power (U)_i) The wind speed is u_kThe wind direction being θ_lThe output power of the ith wind turbine generator set is calculated; f (u)_k,θ_l) The wind speed is u_kWind direction angle of theta_lThe wind frequency of the time; n is a radical of_θThe number of wind direction angles; n is a radical of_uIs the number of wind speeds.

In one embodiment, the arrangement scheme of the wind turbine generators in the wind farm of each individual can meet a constraint condition, the constraint condition can be that the distance between any two wind turbine generators in the wind farm is greater than N times of the diameter of the wind wheel, wherein N is a positive integer greater than 1.

In one embodiment, the Pareto front solution further includes a final target value of the first optimization objective and a final target value of the second optimization objective, and the Pareto front solution obtaining module is further configured to perform the steps of: when the first optimization target and the second optimization target simultaneously reach convergence for the first time, performing multi-objective optimization for multiple times, and acquiring a Pareto front solution obtained after the first optimization target and the second optimization target simultaneously reach convergence in each multi-objective optimization; if the Pareto front edge solutions of the multi-time multi-objective optimization are the same as the Pareto front edge solutions obtained after the convergence is simultaneously achieved for the first time, the Pareto front edge solutions obtained after the convergence is simultaneously achieved for the first time are used as final Pareto front edge solutions; and if at least one Pareto front solution in the Pareto front solutions of the multi-objective optimization is different from the Pareto front solution obtained after the convergence is simultaneously reached for the first time, selecting the Pareto front solution corresponding to the minimum final target value of the first optimization target or selecting the Pareto front solution corresponding to the maximum final target value of the second optimization target as the final Pareto front solution according to the final target value of the first optimization target and the final target value of the second optimization target in the Pareto front solutions.

The technical principles, the solved technical problems and the generated technical effects of the above multi-target configuration optimization system for wind farm machine locations shown in fig. 1 are similar, and it can be clearly understood by those skilled in the art that for convenience and simplicity of description, the specific working process and the related description of the multi-target configuration optimization system for wind farm machine locations may refer to the content described in the embodiment of the multi-target configuration optimization method for wind farm machine locations, and the description thereof is omitted here.

It will be understood by those skilled in the art that all or part of the flow of the method according to the above-described embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used to implement the steps of the above-described embodiments of the method when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

Further, it should be understood that, since the configuration of each module is only for explaining the functional units of the apparatus of the present invention, the corresponding physical devices of the modules may be the processor itself, or a part of software, a part of hardware, or a part of a combination of software and hardware in the processor. Thus, the number of individual modules in the figures is merely illustrative.

Those skilled in the art will appreciate that the various modules in the apparatus may be adaptively split or combined. Such splitting or combining of specific modules does not cause the technical solutions to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.

So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims

1. A multi-objective arrangement optimization method for wind power plant stands is characterized by comprising the following steps:

2. The method of optimizing multi-objective placement of wind farm stands according to claim 1, further comprising calculating a maximum value of the integrated flow direction turbulence levels in front of all wind turbines in the wind farm under the placement scheme corresponding to each individual according to the following steps:

wherein the content of the first and second substances,

obtaining the flow direction turbulence degrees of a plurality of point positions in front of a hub of the current wind turbine generator according to the current position of the wind turbine generator and the flow direction turbulence degrees of any position of the wind power plant and the method described by the following formula

Wherein the content of the first and second substances,

according to preset wind resource data and the flow direction turbulence degree of each wind turbine in different wind directions, and according to the method described by the following formula, obtaining the comprehensive flow direction turbulence degree of all wind turbines in the individual:

3. The method for optimizing the multi-objective placement of wind farm stands according to claim 1, further comprising calculating the total output power of the wind farm under the placement scheme corresponding to each individual according to the following steps:

aiming at each wind turbine generator in the wind power plant, obtaining the average speed loss of the current wind turbine generator according to the average speed loss of the upstream wind turbine generator of the current wind turbine generator at the position of the current wind turbine generator and the method described by the following formula:

wherein, Delta U_iThe average speed loss of the ith wind turbine generator set is obtained; delta U_ijThe average speed loss of the jth wind turbine generator set at the ith wind turbine generator set is obtained; q. q.s_ijIs a binary variable, q is a binary variable, and q is a binary variable when and only when the current wind turbine i is in the wake generated by the jth upstream wind turbine_ij1 otherwise q_ij＝0；N_tThe number of wind generating sets in the wind power plant;

U_i＝U₀-ΔU_i

according to the output power and the wind direction, and according to the method described in the following formula, obtaining the total output power of the wind power plant under the current wind power plant arrangement scheme:

wherein, P_totalThe total output power of the wind power plant under the current arrangement scheme of the wind power plant; power (U)_i) The wind speed is u_kThe wind direction being θ_lThe output power of the ith wind turbine generator set is calculated; f (u)_k，θ_l) The wind speed is u_kWind direction angle of theta_lThe wind frequency of the time; n is a radical of_θThe number of the wind direction angles is; n is a radical of_uIs the number of wind speeds.

4. The method for optimizing the multi-objective arrangement of the wind power plant stands according to claim 1, wherein the arrangement scheme of the wind power generation sets in the wind power plant of each individual satisfies a constraint condition that the distance between any two wind power generation sets in the wind power plant is greater than N times of the diameter of the wind wheel, wherein N is a positive integer greater than 1.

5. The method for optimizing the multi-objective arrangement of the wind farm stands according to claim 1, wherein the Pareto front solution further comprises a final target value of the first optimization objective and a final target value of the second optimization objective, and the step of obtaining the multi-objective optimized Pareto front solution after the first optimization objective and the second optimization objective converge simultaneously comprises:

6. A multi-objective arrangement optimization system for wind power plant stands is characterized by comprising the following components:

7. The system for multi-objective placement optimization of wind farm stands as recited in claim 6, further comprising a streamwise turbulence maximum acquisition module comprising:

wherein the content of the first and second substances,

the wind turbine generator hub front flow direction turbulence degree acquisition unit is configured to acquire flow direction turbulence degrees of a plurality of point positions at the front height of the hub of the current wind turbine generator according to the additional flow direction turbulence degree and the atmospheric turbulence degree of the current wind turbine generator position and the method described in the following formula

Wherein the content of the first and second substances,

8. The system for optimizing multi-objective placement of wind farm stands as defined in claim 6, further comprising a total output power harvesting module comprising:

wherein, Delta U_iThe average speed loss of the ith wind turbine generator set is obtained; delta U_ijThe average speed loss of the jth wind turbine generator set at the ith wind turbine generator set is obtained; q. q.s_ijIs a binary variable, q is a binary variable, and q is a binary variable when and only when the current wind turbine i is in the wake generated by the jth upstream wind turbine_ijOther cases q 1_ij＝0；N_tThe number of wind generating sets in the wind power plant;

U_i＝U₀-ΔU_i

9. The system for optimizing multi-objective arrangement of wind power plant stands according to claim 6, wherein the arrangement scheme of wind power generation sets in the wind power plant of each individual satisfies a constraint condition that the distance between any two wind power generation sets in the wind power plant is greater than N times of the diameter of a wind wheel, wherein N is a positive integer greater than 1.

10. The system of claim 6, wherein the Pareto frontier solution further comprises a final target value of the first optimization objective and a final target value of the second optimization objective, the Pareto frontier solution acquisition module being further configured to perform the steps of:

when the first optimization target and the second optimization target simultaneously converge for the first time, performing multi-target optimization for multiple times, and acquiring a Pareto front edge solution obtained when the first optimization target and the second optimization target simultaneously converge in each multi-target optimization;