CN111325394A - Optimal design method and system for wind power plant, electronic device and storage medium - Google Patents
Optimal design method and system for wind power plant, electronic device and storage medium Download PDFInfo
- Publication number
- CN111325394A CN111325394A CN202010099581.4A CN202010099581A CN111325394A CN 111325394 A CN111325394 A CN 111325394A CN 202010099581 A CN202010099581 A CN 202010099581A CN 111325394 A CN111325394 A CN 111325394A
- Authority
- CN
- China
- Prior art keywords
- wind
- optimization
- loom
- algorithm
- power plant
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013461 design Methods 0.000 title claims abstract description 89
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000005457 optimization Methods 0.000 claims abstract description 112
- 238000004422 calculation algorithm Methods 0.000 claims description 62
- 230000006870 function Effects 0.000 claims description 22
- 238000013473 artificial intelligence Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 9
- 238000007635 classification algorithm Methods 0.000 claims description 8
- 238000012706 support-vector machine Methods 0.000 claims description 8
- 230000002068 genetic effect Effects 0.000 claims description 7
- 239000002245 particle Substances 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000003066 decision tree Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000007935 neutral effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- General Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Databases & Information Systems (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Primary Health Care (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Public Health (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- Remote Sensing (AREA)
- Looms (AREA)
Abstract
The invention discloses an optimal design method, an optimal design system, electronic equipment and a storage medium for a wind power plant, wherein the optimal design method comprises the following steps: acquiring attribute information of a plurality of wind resource data points in a wind resource map of a wind power plant; acquiring a target category corresponding to the wind resource data point according to the attribute information; determining a plurality of machine distribution sub-areas corresponding to the wind power plant area according to the target category; wherein the same target class corresponds to the same loom subarea; obtaining an optimized design scheme corresponding to the loom subarea; and laying out the loom subareas according to the optimized design scheme. By combining classification and optimization, the optimization range and parameter dimension are reduced, and required computing resources are reduced, so that the optimization process is simplified, the optimization speed is greatly increased, and the optimization speed of the optimization design of the whole wind power plant is obviously increased.
Description
Technical Field
The invention relates to the technical field of wind power plants, in particular to an optimal design method, an optimal design system, electronic equipment and a storage medium for a wind power plant.
Background
At present, an optimal solution is obtained mainly by adopting an optimization algorithm (such as random optimization, a genetic algorithm, a particle swarm algorithm, an ant colony algorithm and the like) according to a wind resource map during optimization design of a wind power plant, inappropriate machine sites (such as low power generation) are gradually eliminated by the algorithms in a certain mode in the optimization process, and finally the optimal solution (local or global) is obtained, and whether part of algorithms can obtain the optimal solution depends on an initial machine site layout scheme.
However, the existing optimization design of the wind power plant is generally processed for the whole wind power plant area, and the problems of large required computing resource, slow optimization speed and incapability of meeting the actual requirement exist.
Disclosure of Invention
The invention aims to overcome the defects that in the prior art, the optimization design scheme of a wind power plant has large calculation resources and slow optimization speed and cannot meet the actual requirement, and provides an optimization design method, a system, electronic equipment and a storage medium of the wind power plant.
The invention solves the technical problems through the following technical scheme:
the invention provides an optimal design method of a wind power plant, which comprises the following steps:
acquiring attribute information of a plurality of wind resource data points in a wind resource map of a wind power plant;
acquiring a target category corresponding to the wind resource data point according to the attribute information;
determining a plurality of machine distribution sub-areas corresponding to the wind power plant area according to the target category;
wherein the same target class corresponds to the same loom subarea;
obtaining an optimized design scheme corresponding to the loom subarea;
and laying out the loom subareas according to the optimized design scheme.
Preferably, the step of obtaining the target category corresponding to the wind resource data point according to the attribute information includes:
and acquiring the target category corresponding to the wind resource data point according to the attribute information by adopting an artificial intelligence algorithm.
Preferably, the step of obtaining the optimized design scheme corresponding to the loom subarea includes:
determining an optimization condition corresponding to the loom subarea;
and obtaining the optimal design scheme of the machine sub-area according to the optimal condition and an objective function corresponding to the wind power plant.
Preferably, the step of obtaining the optimal design scheme of the machine sub-area according to the optimization condition and the objective function corresponding to the wind farm includes:
obtaining layout parameter information corresponding to the loom subareas by adopting an optimization algorithm according to the optimization conditions and the objective function;
the step of laying out the loom sub-regions according to the optimal design scheme comprises:
and laying out the loom subareas according to the layout parameter information.
Preferably, the optimization algorithm comprises a random optimization algorithm, a genetic algorithm, an ant colony algorithm or a particle swarm algorithm; and/or the presence of a gas in the gas,
the layout parameter information comprises at least one of the number of units, the positions of the units, the types of the units, the height of the hubs and the basic type.
Preferably, the attribute information includes at least one of atmospheric stability, wind shear, turbulence intensity, and wind power density; and/or the presence of a gas in the gas,
the artificial intelligence algorithm comprises a decision tree classification method, a naive Bayes classification algorithm, a support vector machine, a neural network algorithm, a k-nearest neighbor method or a fuzzy classification method.
The invention also provides an optimal design system of the wind power plant, which comprises an attribute information acquisition module, a target category acquisition module, a sub-region determination module, a scheme acquisition module and a layout module;
the attribute information acquisition module is used for acquiring attribute information of a plurality of wind resource data points in a wind resource map of the wind power plant;
the target category acquisition module is used for acquiring a target category corresponding to the wind resource data point according to the attribute information;
the sub-region determining module is used for determining a plurality of sub-regions corresponding to the wind power plant region according to the target category;
wherein the same target class corresponds to the same loom subarea;
the scheme acquisition module is used for acquiring an optimized design scheme corresponding to the loom subarea;
the layout module is used for laying out the loom subareas according to the optimized design scheme.
Preferably, the target category obtaining module is configured to obtain the target category corresponding to the wind resource data point according to the attribute information by using an artificial intelligence algorithm.
Preferably, the scheme acquisition module comprises a condition determination unit and a scheme acquisition unit;
the condition determining unit is used for determining an optimization condition corresponding to the loom subarea;
the scheme obtaining unit is used for obtaining the optimized design scheme of the sub-wind power plant area according to the optimized condition and the objective function corresponding to the wind power plant.
Preferably, the scheme obtaining module is configured to obtain layout parameter information corresponding to the loom subarea according to the optimization condition and the objective function by using an optimization algorithm;
the layout module is used for laying out the loom subareas according to the layout parameter information.
Preferably, the optimization algorithm comprises a random optimization algorithm, a genetic algorithm, an ant colony algorithm or a particle swarm algorithm; and/or the presence of a gas in the gas,
the layout parameter information comprises at least one of the number of units, the positions of the units, the types of the units, the height of the hubs and the basic type.
Preferably, the attribute information includes at least one of atmospheric stability, wind shear, turbulence intensity, and wind power density; and/or the presence of a gas in the gas,
the artificial intelligence algorithm comprises a decision tree classification method, a naive Bayes classification algorithm, a support vector machine, a neural network algorithm, a k-nearest neighbor method or a fuzzy classification method.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the optimal design method of the wind power plant.
The invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method for optimal design of a wind farm.
The positive progress effects of the invention are as follows:
according to the optimization method, the categories corresponding to the attribute information of the wind resource data points in the wind resource map are obtained, the whole wind power plant is divided into a plurality of different machine distribution sub-regions according to different categories, the optimization design schemes of the different machine distribution sub-regions are respectively obtained, and each machine distribution sub-region is distributed according to the optimization design schemes, so that the optimization range and the parameter dimension of each sub-region are reduced, the required computing resources are reduced, the optimization process is simplified, the optimization speed is greatly increased, and the optimization speed of the optimization design of the whole wind power plant is remarkably increased.
Drawings
Fig. 1 is a flowchart of an optimal design method for a wind farm according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of an optimal design method for a wind farm according to embodiment 2 of the present invention.
Fig. 3 is a schematic structural diagram of a determination system for designing a wind farm according to embodiment 3 of the present invention.
Fig. 4 is a schematic structural diagram of an optimal design method for a wind farm according to embodiment 4 of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device for implementing the optimal design method for a wind farm in embodiment 5 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, the optimal design method for the wind farm of the present embodiment includes:
s101, acquiring attribute information of a plurality of wind resource data points in a wind resource map of a wind power plant;
the attribute information includes atmospheric stability, wind shear, turbulence intensity, wind power density, and the like.
Preferably, the present embodiment is directed to each wind resource data point in the wind resource map of the entire wind farm.
S102, acquiring a target category corresponding to the wind resource data point according to the attribute information;
for example, for atmospheric stability, its corresponding categories may include: stable, neutral and unstable; wind shears, the corresponding categories of which may include high wind shears and low wind shears; the turbulence intensity can be classified into A, B, C types according to set standards; wind power density can be classified into a plurality of categories according to an artificially set rule.
One or more attributes of the attribute information of the wind resource data point may be selected for classification according to their value. When the optimization speed is reduced by adopting a complex classification algorithm, classification can be performed based on main attributes, if wind power density is selected as the main attribute, a region lower than a certain numerical value can be set as a non-loom region, a region higher than the certain numerical value is set as a loom region, the loom region is divided into a plurality of loom regions according to the numerical value, and the loom is started from the loom region corresponding to the maximum wind power density in the optimization process. A category may also be determined based on multiple attributes such as atmospheric stability, wind shear, turbulence intensity, wind power density, and the like.
S103, determining a plurality of machine distribution sub-areas corresponding to the wind power plant area according to the target category;
wherein the same target class corresponds to the same loom subarea;
s104, obtaining an optimized design scheme corresponding to the loom subarea;
and S105, laying out the loom subareas according to the optimized design scheme.
In the embodiment, the wind resource data are classified, then a plurality of machine sub-regions in the whole wind farm are obtained according to each category, and each sub-region is optimized, so that an optimization design scheme is rapidly obtained and layout is performed.
In the embodiment, the categories corresponding to the attribute information of the wind resource data points in the wind resource map are obtained, the whole wind power plant is divided into a plurality of different machine distribution sub-regions according to different categories, the optimization design schemes of the different machine distribution sub-regions are respectively obtained, and each machine distribution sub-region is distributed according to the optimization design schemes, namely for each sub-region, the optimization searching range and the parameter dimension are reduced, the required computing resources are reduced, so that the optimization process is simplified, the optimization searching speed is greatly increased, and the optimization searching speed of the optimization design of the whole wind power plant is obviously increased.
Example 2
As shown in fig. 2, the optimal design method for the wind farm of the present embodiment is a further improvement of embodiment 1, specifically:
step S102 includes:
and S1021, acquiring a target category corresponding to the wind resource data point according to the attribute information by adopting an artificial intelligence algorithm.
The artificial intelligence algorithm comprises a decision tree classification method, a naive Bayes classification algorithm, a support vector machine, a neural network algorithm, a k-nearest neighbor method or a fuzzy classification method and the like.
Step S104 includes:
s1041, determining an optimization condition corresponding to a loom subregion;
and determining the optimization conditions corresponding to each loom subarea according to the attribute information of each wind resource data point in each loom subarea. Of course, the optimization conditions can be adjusted and corrected according to actual requirements.
S1042, obtaining an optimized design scheme of the loom subarea according to the optimized conditions and the objective function corresponding to the wind power field.
The objective function corresponding to the wind farm is a single objective function or a multi-objective function known to those skilled in the art, and therefore, the details are not described here.
Specifically, an optimization algorithm is adopted to obtain layout parameter information corresponding to the loom subareas according to the optimization conditions and the objective function.
Wherein, the optimization algorithm includes but is not limited to a random optimization algorithm, a genetic algorithm, an ant colony algorithm or a particle swarm algorithm.
The layout parameter information comprises the number of units, the positions of the units, the types of the units, the height of the hubs, the basic type and the like.
For example, the loom subareas are divided into three regions with turbulence degrees, at the moment, the model of each region can be determined and the optimization region is reduced, for the subareas, the optimization range is reduced and the design variable is reduced by one dimension, namely, the setting parameters of the optimization algorithm are adjusted, the optimization problem is simplified, and the optimization speed is increased.
Step S105 includes:
and laying out the loom subareas according to the layout parameter information.
The following is a detailed description with reference to examples:
(1) classifying attribute information of a plurality of wind resource data points in a wind resource map of a wind power plant by using a support vector machine to obtain three types of I, II and III;
(2) respectively determining three corresponding loom subareas A, B, C of I, II and III;
(3) determining A, B, C optimization conditions (or optimization parameters) corresponding to the three areas;
assuming that the I area is an area with high wind shear, the height of the hub is set to be 100 m; and the areas II and III are areas with lower wind shear, the hub height is set to 90 m. Meanwhile, if the area II is the class A turbulent zone, the machine type is determined to be the IEC class A fan, and if the area III is assumed to be the class B turbulent zone, the machine type is determined to be the IEC class B fan;
(4) after parameter setting of each machine distribution subarea is completed, optimization design can be simultaneously carried out on each area, finally, the number of fans, machine types, machine position coordinates and the like corresponding to each machine distribution subarea are obtained, and finally, optimization design and layout of the whole wind power plant are completed.
In the embodiment, the categories corresponding to the attribute information of the wind resource data points in the wind resource map are obtained, the whole wind power plant is divided into a plurality of different machine distribution sub-regions according to different categories, the optimization design schemes of the different machine distribution sub-regions are respectively obtained, and each machine distribution sub-region is distributed according to the optimization design schemes, namely for each sub-region, the optimization searching range and the parameter dimension are reduced, the required computing resources are reduced, so that the optimization process is simplified, the optimization searching speed is greatly increased, and the optimization searching speed of the optimization design of the whole wind power plant is obviously increased.
Example 3
As shown in fig. 3, the optimal design method for the wind farm in this embodiment includes an attribute information obtaining module 1, a target class obtaining module 2, a sub-region determining module 3, a scheme obtaining module 4, and a layout module 5.
The attribute information acquisition module 1 is used for acquiring attribute information of a plurality of wind resource data points in a wind resource map of a wind farm.
The attribute information includes atmospheric stability, wind shear, turbulence intensity, wind power density, and the like.
Preferably, the present embodiment is directed to each wind resource data point in the wind resource map of the entire wind farm.
The target category acquisition module 2 is used for acquiring a target category corresponding to the wind resource data point according to the attribute information;
for example, for atmospheric stability, its corresponding categories may include: stable, neutral and unstable; wind shears, the corresponding categories of which may include high wind shears and low wind shears; the turbulence intensity can be classified into A, B, C types according to set standards; wind power density can be classified into a plurality of categories according to an artificially set rule.
One or more attributes of the attribute information of the wind resource data point may be selected for classification according to their value. When the optimization speed is reduced by adopting a complex classification algorithm, classification can be performed based on main attributes, if wind power density is selected as the main attribute, a region lower than a certain numerical value can be set as a non-loom region, a region higher than the certain numerical value is set as a loom region, the loom region is divided into a plurality of loom regions according to the numerical value, and the loom is started from the loom region corresponding to the maximum wind power density in the optimization process. A category may also be determined based on multiple attributes such as atmospheric stability, wind shear, turbulence intensity, wind power density, and the like.
The sub-region determining module 3 is used for determining a plurality of machine sub-regions corresponding to the wind power plant region according to the target category;
wherein the same target class corresponds to the same loom subarea;
the scheme acquisition module 4 is used for acquiring an optimized design scheme corresponding to a loom subregion;
the layout module 5 is used for laying out the loom subareas according to the optimized design scheme.
In the embodiment, the wind resource data are classified, then a plurality of machine sub-regions in the whole wind farm are obtained according to each category, and each sub-region is optimized, so that an optimization design scheme is rapidly obtained and layout is performed.
In the embodiment, the categories corresponding to the attribute information of the wind resource data points in the wind resource map are obtained, the whole wind power plant is divided into a plurality of different machine distribution sub-regions according to different categories, the optimization design schemes of the different machine distribution sub-regions are respectively obtained, and each machine distribution sub-region is distributed according to the optimization design schemes, namely for each sub-region, the optimization searching range and the parameter dimension are reduced, the required computing resources are reduced, so that the optimization process is simplified, the optimization searching speed is greatly increased, and the optimization searching speed of the optimization design of the whole wind power plant is obviously increased.
Example 4
As shown in fig. 4, the optimal design system of the wind farm of the present embodiment is a further improvement of embodiment 3, specifically:
and the target category acquisition module 2 is used for acquiring a target category corresponding to the wind resource data point according to the attribute information by adopting an artificial intelligence algorithm.
The artificial intelligence algorithm comprises a decision tree classification method, a naive Bayes classification algorithm, a support vector machine, a neural network algorithm, a k-nearest neighbor method or a fuzzy classification method and the like.
The scheme acquisition module 4 comprises a condition determination unit 6 and a scheme acquisition unit 7;
the condition determining unit 6 is used for determining an optimization condition corresponding to the loom subarea;
and determining the optimization conditions corresponding to each loom subarea according to the attribute information of each wind resource data point in each loom subarea. Of course, the optimization conditions can be adjusted and corrected according to actual requirements.
The scheme obtaining unit 7 is configured to obtain an optimal design scheme of the loom subarea according to an objective function corresponding to the optimization condition and the wind farm.
The objective function corresponding to the wind farm is a single objective function or a multi-objective function known to those skilled in the art, and therefore, the details are not described here.
Specifically, the scheme obtaining module 4 is configured to obtain layout parameter information corresponding to the sub-regions of the loom by using an optimization algorithm according to the optimization condition and the objective function;
wherein, the optimization algorithm includes but is not limited to a random optimization algorithm, a genetic algorithm, an ant colony algorithm or a particle swarm algorithm.
The layout parameter information comprises the number of units, the positions of the units, the types of the units, the height of the hubs, the basic type and the like.
For example, the loom subareas are divided into three regions with turbulence degrees, at the moment, the model of each region can be determined and the optimization region is reduced, for the subareas, the optimization range is reduced and the design variable is reduced by one dimension, namely, the setting parameters of the optimization algorithm are adjusted, the optimization problem is simplified, and the optimization speed is increased.
The layout module 5 is used for laying out the loom subareas according to the layout parameter information.
The following is a detailed description with reference to examples:
(1) classifying attribute information of a plurality of wind resource data points in a wind resource map of a wind power plant by using a support vector machine to obtain three types of I, II and III;
(2) respectively determining three corresponding loom subareas A, B, C of I, II and III;
(3) determining A, B, C optimization conditions (or optimization parameters) corresponding to the three areas;
assuming that the I area is an area with high wind shear, the height of the hub is set to be 100 m; and the areas II and III are areas with lower wind shear, the hub height is set to 90 m. Meanwhile, if the area II is the class A turbulent zone, the machine type is determined to be the IEC class A fan, and if the area III is assumed to be the class B turbulent zone, the machine type is determined to be the IEC class B fan;
(4) after parameter setting of each machine distribution subarea is completed, optimization design can be simultaneously carried out on each area, finally, the number of fans, machine types, machine position coordinates and the like corresponding to each machine distribution subarea are obtained, and finally, optimization design and layout of the whole wind power plant are completed.
In the embodiment, the categories corresponding to the attribute information of the wind resource data points in the wind resource map are obtained, the whole wind power plant is divided into a plurality of different machine distribution sub-regions according to different categories, the optimization design schemes of the different machine distribution sub-regions are respectively obtained, and each machine distribution sub-region is distributed according to the optimization design schemes, namely for each sub-region, the optimization searching range and the parameter dimension are reduced, the required computing resources are reduced, so that the optimization process is simplified, the optimization searching speed is greatly increased, and the optimization searching speed of the optimization design of the whole wind power plant is obviously increased.
Example 5
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention. The electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, and when the processor executes the program, the optimal design method of the wind farm in any one of the embodiments 1 or 2 is realized. The electronic device 30 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM)321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
The processor 31 executes various functional applications and data processing, such as a method for optimally designing a wind farm in any one of the embodiments 1 or 2 of the present invention, by running a computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via network adapter 36. As shown in FIG. 5, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 6
The present embodiment provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps in the method for optimal design of a wind farm in any of embodiments 1 or 2.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention can also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of a method for optimal design of a wind farm as described in any of embodiments 1 or 2, when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (14)
1. An optimal design method for a wind power plant is characterized by comprising the following steps:
acquiring attribute information of a plurality of wind resource data points in a wind resource map of a wind power plant;
acquiring a target category corresponding to the wind resource data point according to the attribute information;
determining a plurality of machine distribution sub-areas corresponding to the wind power plant area according to the target category;
wherein the same target class corresponds to the same loom subarea;
obtaining an optimized design scheme corresponding to the loom subarea;
and laying out the loom subareas according to the optimized design scheme.
2. The method for optimizing design of a wind farm according to claim 1, wherein the step of obtaining the target category corresponding to the wind resource data point according to the attribute information comprises:
and acquiring the target category corresponding to the wind resource data point according to the attribute information by adopting an artificial intelligence algorithm.
3. The method for optimally designing a wind farm according to claim 1 or 2, wherein the step of obtaining the optimal design scheme corresponding to the distributed sub-area comprises the following steps:
determining an optimization condition corresponding to the loom subarea;
and obtaining the optimal design scheme of the machine sub-area according to the optimal condition and an objective function corresponding to the wind power plant.
4. The optimal design method for the wind farm according to claim 3, wherein the step of obtaining the optimal design scheme for the grid-connected sub-area according to the optimization condition and the objective function corresponding to the wind farm comprises:
obtaining layout parameter information corresponding to the loom subareas by adopting an optimization algorithm according to the optimization conditions and the objective function;
the step of laying out the loom sub-regions according to the optimal design scheme comprises:
and laying out the loom subareas according to the layout parameter information.
5. The method for optimal design of a wind farm according to claim 4, wherein the optimization algorithm comprises a random optimization algorithm, a genetic algorithm, an ant colony algorithm or a particle swarm algorithm; and/or the presence of a gas in the gas,
the layout parameter information comprises at least one of the number of units, the positions of the units, the types of the units, the height of the hubs and the basic type.
6. The method of optimizing design for a wind farm according to claim 2, wherein the attribute information comprises at least one of atmospheric stability, wind shear, turbulence intensity, and wind power density; and/or the presence of a gas in the gas,
the artificial intelligence algorithm comprises a decision tree classification method, a naive Bayes classification algorithm, a support vector machine, a neural network algorithm, a k-nearest neighbor method or a fuzzy classification method.
7. The optimal design system of the wind power plant is characterized by comprising an attribute information acquisition module, a target category acquisition module, a sub-region determination module, a scheme acquisition module and a layout module;
the attribute information acquisition module is used for acquiring attribute information of a plurality of wind resource data points in a wind resource map of the wind power plant;
the target category acquisition module is used for acquiring a target category corresponding to the wind resource data point according to the attribute information;
the sub-region determining module is used for determining a plurality of sub-regions corresponding to the wind power plant region according to the target category;
wherein the same target class corresponds to the same loom subarea;
the scheme acquisition module is used for acquiring an optimized design scheme corresponding to the loom subarea;
the layout module is used for laying out the loom subareas according to the optimized design scheme.
8. The optimal design system for wind farms according to claim 7, wherein the target class obtaining module is configured to obtain the target class corresponding to the wind resource data point according to the attribute information by using an artificial intelligence algorithm.
9. The optimal design system for a wind farm according to claim 7 or 8, characterized in that the solution acquisition module comprises a condition determination unit and a solution acquisition unit;
the condition determining unit is used for determining an optimization condition corresponding to the loom subarea;
the scheme obtaining unit is used for obtaining the optimized design scheme of the sub-wind power plant area according to the optimized condition and the objective function corresponding to the wind power plant.
10. The optimal design system of the wind farm according to claim 9, wherein the scheme acquisition module is configured to acquire layout parameter information corresponding to the sub-regions of the wind farm by using an optimization algorithm according to the optimization condition and the objective function;
the layout module is used for laying out the loom subareas according to the layout parameter information.
11. The optimal design system for a wind farm according to claim 10, wherein the optimization algorithm comprises a random optimization algorithm, a genetic algorithm, an ant colony algorithm or a particle swarm algorithm; and/or the presence of a gas in the gas,
the layout parameter information comprises at least one of the number of units, the positions of the units, the types of the units, the height of the hubs and the basic type.
12. The optimal design system for a wind farm according to claim 8, wherein the attribute information comprises at least one of atmospheric stability, wind shear, turbulence intensity, and wind power density; and/or the presence of a gas in the gas,
the artificial intelligence algorithm comprises a decision tree classification method, a naive Bayes classification algorithm, a support vector machine, a neural network algorithm, a k-nearest neighbor method or a fuzzy classification method.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the method for optimal design of a wind farm according to any of claims 1-6.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for optimal design of a wind farm according to any of the claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010099581.4A CN111325394A (en) | 2020-02-18 | 2020-02-18 | Optimal design method and system for wind power plant, electronic device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010099581.4A CN111325394A (en) | 2020-02-18 | 2020-02-18 | Optimal design method and system for wind power plant, electronic device and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111325394A true CN111325394A (en) | 2020-06-23 |
Family
ID=71168831
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010099581.4A Pending CN111325394A (en) | 2020-02-18 | 2020-02-18 | Optimal design method and system for wind power plant, electronic device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111325394A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106682282A (en) * | 2016-12-08 | 2017-05-17 | 浙江大学 | Wind farm multi-type wind driven generator arrangement optimization method based on genetic algorithm nested in particle swarm optimization |
| CN106886833A (en) * | 2017-01-12 | 2017-06-23 | 浙江大学 | A kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition |
| CN106897793A (en) * | 2017-01-12 | 2017-06-27 | 浙江大学 | A kind of arrangement optimization method of the wind power plant wind-driven generator based on genetic algorithm for ensureing safe distance |
| CN109299495A (en) * | 2018-07-26 | 2019-02-01 | 河海大学 | A kind of microcosmic intelligent cloth machine method of wind power plant |
| CN109871972A (en) * | 2017-12-01 | 2019-06-11 | 北京普华亿能风电技术有限公司 | A kind of optimization placement method and system of wind field inner blower |
-
2020
- 2020-02-18 CN CN202010099581.4A patent/CN111325394A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106682282A (en) * | 2016-12-08 | 2017-05-17 | 浙江大学 | Wind farm multi-type wind driven generator arrangement optimization method based on genetic algorithm nested in particle swarm optimization |
| CN106886833A (en) * | 2017-01-12 | 2017-06-23 | 浙江大学 | A kind of wind-driven generator addressing Lectotype Optimization method suitable for Complex Constraints condition |
| CN106897793A (en) * | 2017-01-12 | 2017-06-27 | 浙江大学 | A kind of arrangement optimization method of the wind power plant wind-driven generator based on genetic algorithm for ensureing safe distance |
| CN109871972A (en) * | 2017-12-01 | 2019-06-11 | 北京普华亿能风电技术有限公司 | A kind of optimization placement method and system of wind field inner blower |
| CN109299495A (en) * | 2018-07-26 | 2019-02-01 | 河海大学 | A kind of microcosmic intelligent cloth machine method of wind power plant |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | A decomposition-based archiving approach for multi-objective evolutionary optimization | |
| CN109670213B (en) | Multi-machine equivalent modeling method and device for wind power plant | |
| EP4068172A1 (en) | Planning method and system for cable path of wind power plant, medium, and electronic device | |
| CN108647820A (en) | Based on the distributed generation resource addressing constant volume optimization method and system for improving particle cluster algorithm | |
| CN111339713A (en) | Optimal design method and system for wind power plant, electronic device and storage medium | |
| Shetty et al. | An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting | |
| CN113236487B (en) | Wind power plant noise control method, system, device and readable storage medium | |
| CN112803434A (en) | Reactive power optimization method, device, equipment and storage medium for active power distribution network | |
| CN115730755A (en) | Method, device, terminal equipment and medium for realizing path planning of double-carbon target | |
| Makhadmeh et al. | A novel link-based multi-objective grey wolf optimizer for appliances energy scheduling problem | |
| CN116822672A (en) | Air conditioner cold load prediction optimization method and system | |
| CN112668650A (en) | Industrial data model generation method, system, device and medium | |
| CN111325394A (en) | Optimal design method and system for wind power plant, electronic device and storage medium | |
| CN111488712B (en) | Wind power generator power curve modeling method based on transfer learning | |
| CN112464545A (en) | Layout method, system, equipment and medium for cables and transformer substation of offshore wind farm | |
| CN116896093A (en) | Online analysis and optimization method for grid-connected oscillation stability of wind farm | |
| Rashedi et al. | A multi-objective approach for solving transmission expansion planning problem considering wind power uncertainty | |
| de Moura Oliveira et al. | Evolutionary and bio-inspired algorithms in greenhouse control: introduction, review and trends | |
| Ignatyev et al. | Development of a method for automatic generation and optimization of fuzzy controller parameters using genetic algorithm | |
| Li et al. | An Improved Cuckoo Search Algorithm Using Elite Opposition-Based Learning and Golden Sine Operator | |
| Bashista et al. | Single-Sensor based CSPO Algorithm for Maximum Power Point Tracking under Dynamic Shading Conditions | |
| Harrison-Atlas et al. | Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering | |
| CN117713213A (en) | Photovoltaic cluster control method and device based on improved artificial fish school and storage medium | |
| Mazumdar et al. | Optimizing MPPT Control for Enhanced Efficiency in Sustainable Photovoltaic Microgrids: A DSO‐Based Approach | |
| CN113642766B (en) | Method, device, equipment and medium for predicting power outage number of power system station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200623 |