CN111242803A - Fan arrangement method and device based on multi-population genetic algorithm - Google Patents
Fan arrangement method and device based on multi-population genetic algorithm Download PDFInfo
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- CN111242803A CN111242803A CN202010006002.7A CN202010006002A CN111242803A CN 111242803 A CN111242803 A CN 111242803A CN 202010006002 A CN202010006002 A CN 202010006002A CN 111242803 A CN111242803 A CN 111242803A
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- G06N3/12—Computing arrangements based on biological models using genetic models
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Abstract
The invention provides a method and a device for arranging fans based on multi-population genetic algorithm. The method comprises the following steps: randomly generating a plurality of initial populations, wherein chromosomes in each population use machine position as chromosomes and electric energy production as fitness function; optimizing each population by adopting a genetic algorithm; completing one-time optimization on each population, and transferring chromosomes among the populations; selecting the optimal chromosome from each population, and adding the essence population; and taking the position represented by the chromosome in the elite population as the optimal machine position. The fan arrangement method and device based on the multi-population genetic algorithm can solve the problem of local convergence of the original fan arrangement mode and achieve global optimization.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a method and a device for arranging fans based on multi-population genetic algorithm.
Background
With the advance of bidding online policies in the wind power industry, the demand of increasing income and reducing cost is more and more urgent, and in the past, manual arrangement is mostly adopted in fan arrangement, so that subjective factors have great influence, and real optimization of machine position arrangement is difficult to realize. The intelligent algorithm is introduced into the fan arrangement, the optimal fan layout is automatically searched, the wind power plant resources can be more fully utilized, and the economic benefit is improved.
The intelligent optimization algorithm, especially the genetic algorithm, is widely applied in various industries. Some articles for fan arrangement based on genetic algorithms have appeared. However, the method is easy to be trapped in local convergence by simply applying a genetic algorithm, for example, the algorithm automatically searches several loom positions with higher power generation amount in a certain high-point dense area in a wind power plant, and stops searching when the arrangement scheme is found to be optimal through comparison with the surrounding positions. However, there are better locations than a certain distance apart, which is so-called local convergence.
Disclosure of Invention
The invention aims to solve the technical problem of providing a fan arrangement method and device based on multi-population genetic algorithm, so that the problem of local convergence of the original fan arrangement mode is solved, and global optimization is achieved.
In order to solve the technical problem, the invention provides a fan arrangement method based on multi-population genetic algorithm, which comprises the following steps: randomly generating a plurality of initial populations, wherein chromosomes in each population are encoded by taking the position of a machine position as a chromosome, and the generated energy is taken as an fitness function; optimizing each population by adopting a genetic algorithm; completing one-time optimization on each population, and transferring chromosomes among the populations; selecting the optimal chromosome from each population, and adding the essence population; and taking the position represented by the chromosome in the elite population as the optimal machine position.
In some embodiments, further comprising: and after the optimal chromosomes are selected from each population and the elite population is added, repeating the operations of population optimization, chromosome transfer, optimal chromosome selection and elite population addition until the number of the chromosomes in the elite population meets the number requirement.
In some embodiments, the optimal chromosome is selected from each population and a elite population is added, comprising: selecting an optimal chromosome from each population; and selecting an optimal chromosome from the optimal chromosomes to place the optimal chromosome into the elite population.
In some embodiments, the optimization is performed using a genetic algorithm for each population, comprising: and optimizing each population by adopting a genetic algorithm, wherein the traditional genetic algorithm comprises the following steps: simple genetic algorithm, niche genetic algorithm, immune genetic algorithm.
In some embodiments, the optimizing is performed once for each population, and the transferring of chromosomes between each population comprises: and after one-time genetic algorithm optimization is completed, selecting the optimal machine position of each population to replace the worst machine position in the next population.
In some embodiments, the optimal and worst machine positions are determined with reference to a fitness function.
In some embodiments, the genetic algorithm employs an objective function that is a target power generation.
In some embodiments, further comprising: before a plurality of populations are randomly generated, a data matrix of machine position positions is established, and grid positions are stored in the matrix.
In addition, the invention also provides a multi-population genetic algorithm-based fan arrangement device, which comprises: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for fan configuration based on a multi-population genetic algorithm as described above.
After adopting such design, the invention has at least the following advantages:
at present, the arrangement of the machine positions in the industry is usually completed manually, but the arrangement mode of the fan is difficult to realize the maximization of the economic benefit under the large background of bidding surfing the internet. The application of the genetic algorithm in fan arrangement can greatly improve the accuracy and efficiency of arrangement, but the traditional genetic algorithm is easy to have the problem of local convergence. According to the invention, various possible machine position arrangement schemes in the wind power plant can be intelligently preferred by using a multi-population genetic algorithm, the automation of machine position arrangement is realized, the rationality of machine position arrangement can be greatly improved, the economic benefit of the wind power plant is improved, and local convergence is avoided to a certain extent.
Drawings
The foregoing is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description.
FIG. 1 is a flow chart of a multi-population genetic algorithm based fan configuration method provided by the present invention;
FIG. 2 is a schematic diagram of a wind farm machine location grid provided by the present invention;
FIG. 3 is a flow chart of a multi-population genetic algorithm based fan configuration method provided by the present invention;
FIG. 4 is a block diagram of a multi-population genetic algorithm based wind turbine arrangement according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
FIG. 1 is a flow chart of a multi-population genetic algorithm-based wind turbine configuration method provided by the invention. Referring to fig. 1, the method for arranging fans based on the multi-population genetic algorithm includes:
and S1, randomly generating a plurality of initial populations, wherein chromosomes in each population are encoded by using the position of the machine position as chromosomes, and the power generation amount is used as an adaptive function.
And S2, optimizing each population by adopting a genetic algorithm.
And S3, completing one-time optimization on each population, and transferring chromosomes among the populations.
And S4, selecting the optimal chromosome from each population, and adding the essence population.
And S5, taking the position represented by the chromosome in the elite population as the optimal machine position.
The method comprises the steps of firstly operating an optimization process in each population, then transferring chromosomes among each population after one-time population optimization is completed, preferentially selecting optimal chromosomes from the optimized populations respectively, and adding the selected optimal chromosomes into the elite population, so that the optimal machine position solution represented by the chromosomes in the elite population is not only a local optimal solution of the machine position in the wind power plant, but also a global machine position optimal solution.
Preferably, before executing the steps of the method shown in fig. 1, for the convenience of representation and storage of the respective machine position, it is also necessary to grid the entire field area of the wind farm, i.e. to establish a data matrix of machine position. After the data matrix is established, the storage of the machine position is embodied as the storage of the data matrix.
Fig. 2 shows the data matrix described above. Referring to fig. 2, the whole wind farm is divided into several grids of equal size, and the positions of the stands are stored in grid units. That is, in the subsequent genetic algorithm optimization process, the codes of the chromosomes participating in the optimization are represented in the form of a grid.
The process shown in fig. 1 needs to be repeatedly performed in practical applications. Specifically, once the initial population is randomly generated, the subsequent operations of S2 and S3 need to be repeatedly performed. This is mainly because the number of optimal stains to be added to the elite population per selection is limited, and performing the operations of S2 and S3 only once often fails to meet the number of chromosomes in the elite population. Therefore, the number of the machine positions obtained by the machine position arrangement method of the wind power plant cannot meet the requirement of the number of the machine positions of the whole wind power plant.
Specifically, the operations of S2 and S3 generally need to be repeated 2 to 5 times to complete the selection of the elite population.
Further, the optimization is carried out by adopting a genetic algorithm on each population, which comprises the following steps: optimizing each population by adopting a genetic algorithm; and after one-time genetic algorithm optimization is completed, selecting the optimal machine position of each population to replace the worst machine position in the next population. Through the optimization process, the optimization of each chromosome in each population and the transfer of the chromosomes between different populations are completed.
The fitness function and the objective function adopted in the population optimization process are the generated energy. Specifically, the fitness function is the real-time power generation amount of the wind power plant. The objective function is the target power generation amount of the wind power plant.
In a preferred embodiment of the present invention, the wind turbine configuration method based on multi-population genetic algorithm comprises the following steps:
1) and establishing a data matrix of the machine position, and storing each grid position and whether the position is suitable for machine position arrangement in the matrix.
2) A plurality of initial populations are randomly generated. Each population is composed of a certain number of randomly generated machine positions, the position data of each machine position is regarded as a chromosome, the power generation amount of each machine position is used as a fitness function, and the group of machine positions is used as an initial population. As a solution to the problem of power generation amount maximization, the algorithm determines both the objective function and the fitness function as power generation amounts.
3) And respectively optimizing various groups by adopting a traditional genetic algorithm.
4) After one-time genetic algorithm optimization is completed, for each population, the optimal machine position is selected to replace the worst machine position in the next population (the optimal worst machine position is measured by an fitness function).
5) And selecting an optimal chromosome from each population, and selecting an optimal chromosome from the optimal chromosomes to place the optimal chromosome into the elite population.
6) And (5) repeating the steps 2) -5) for a plurality of times until the number of chromosomes in the essence population meets the requirement, and the integral iteration number reaches the set requirement.
7) And at the moment, the essence population represents an optimal machine position arrangement position scheme.
FIG. 3 also shows the flow of the wind turbine arrangement method based on the multi-population genetic algorithm provided by the present invention. Referring to fig. 3, the method for arranging fans based on the multi-population genetic algorithm includes: establishing multiple populations, optimizing various populations, transferring chromosome populations, selecting various populations preferentially, adding essence populations and judging whether the essence populations are converged. Through the operation of each step, the method for arranging the fans based on the multi-population genetic algorithm can achieve automation of machine position arrangement, can greatly improve reasonability of machine position arrangement, improves economic benefits of the wind power plant and avoids local convergence to a certain extent.
FIG. 4 is a block diagram of a multi-population genetic algorithm based fan array of the present invention. Referring to fig. 4, the multi-population genetic algorithm-based fan arranging apparatus includes: a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for system operation are also stored. The CPU 401, ROM402, and RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program performs the above-described functions defined in the method of the present invention when executed by a Central Processing Unit (CPU) 401. Note that the computer-readable medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention in any way, and it will be apparent to those skilled in the art that the above description of the present invention can be applied to various modifications, equivalent variations or modifications without departing from the spirit and scope of the present invention.
Claims (9)
1. A fan arrangement method based on multi-population genetic algorithm is characterized by comprising the following steps:
randomly generating a plurality of initial populations, wherein chromosomes in each population are encoded by taking the position of a machine position as a chromosome, and the generated energy is taken as an fitness function;
optimizing each population by adopting a genetic algorithm;
completing one-time optimization on each population, and transferring chromosomes among the populations;
selecting the optimal chromosome from each population, and adding the essence population;
and taking the position represented by the chromosome in the elite population as the optimal machine position.
2. The multi-population genetic algorithm based fan configuration method of claim 1, further comprising:
and after the optimal chromosomes are selected from each population and the elite population is added, repeating the operations of population optimization, chromosome transfer, optimal chromosome selection and elite population addition until the number of the chromosomes in the elite population meets the number requirement.
3. The multi-population genetic algorithm-based fan arrangement method according to claim 2, wherein the selection of the optimal chromosome from each population and the addition of the elite population comprises:
selecting an optimal chromosome from each population;
and selecting an optimal chromosome from the optimal chromosomes to place the optimal chromosome into the elite population.
4. The multi-population genetic algorithm-based wind turbine configuration method according to claim 1, wherein the genetic algorithm is adopted for optimization of each population, and comprises the following steps:
optimizing each population using a genetic algorithm, the genetic algorithm comprising: simple genetic algorithm, niche genetic algorithm, immune genetic algorithm.
5. The fan arrangement method based on the multi-population genetic algorithm according to claim 1, wherein the step of performing one optimization on each population and transferring chromosomes among each population comprises the steps of:
and after one-time genetic algorithm optimization is completed, selecting the optimal machine position of each population to replace the worst machine position in the next population.
6. The multi-population genetic algorithm-based wind turbine configuration method according to claim 5, wherein said optimal and worst machine positions are determined with reference to a fitness function.
7. The multi-population genetic algorithm based fan array method of claim 6, wherein the genetic algorithm employs an objective function that is a target power generation.
8. The multi-population genetic algorithm based fan configuration method of claim 1, further comprising:
before a plurality of populations are randomly generated, a data matrix of machine position positions is established, and grid positions are stored in the matrix.
9. A fan arrangement device based on multi-population genetic algorithm is characterized by comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of multi-population genetic algorithm based wind turbine arrangement according to any one of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112131795A (en) * | 2020-09-25 | 2020-12-25 | 国电联合动力技术有限公司 | Fan arrangement method and device based on annealing simulation and genetic algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106792451A (en) * | 2016-12-17 | 2017-05-31 | 河海大学 | A kind of D2D communication resource optimization methods based on Multiple-population Genetic Algorithm |
| CN106875068A (en) * | 2017-03-03 | 2017-06-20 | 风脉能源（武汉）股份有限公司 | The optimization method and system of a kind of wind-driven generator arrangement type selecting |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106792451A (en) * | 2016-12-17 | 2017-05-31 | 河海大学 | A kind of D2D communication resource optimization methods based on Multiple-population Genetic Algorithm |
| CN106875068A (en) * | 2017-03-03 | 2017-06-20 | 风脉能源（武汉）股份有限公司 | The optimization method and system of a kind of wind-driven generator arrangement type selecting |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112131795A (en) * | 2020-09-25 | 2020-12-25 | 国电联合动力技术有限公司 | Fan arrangement method and device based on annealing simulation and genetic algorithm |
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