CN104268635A - Anemometry network layout optimization method based on reanalysis data - Google Patents
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Abstract
The invention provides an anemometry network layout optimization method based on reanalysis data. The method includes the following steps that the reanalysis data are selected; an anemometry network layout is optimized through a genetic algorithm; whether the effective coverage area of the anemometry network layout meets an end condition or not is judged, if yes, an optimized anemometer tower layout is output, and if not, the last step is continuously executed. Based on an existing anemometer tower, the correlation between different grids of the reanalysis data serves as evaluation indexes for region wind regime representation, the genetic algorithm is adopted, and then the anemometry network layout is optimized. Along with differences of problem varieties and enlargement of problem scales, search and optimization can be well achieved with limited price, and the method has good robustness and engineering practicality.
Description
Technical field
The present invention relates to a kind of optimization method, be specifically related to a kind of wind measurement network layout optimization method based on analysis of data again.
Background technology
The fast development of wind-powered electricity generation is very favourable for making full use of local wind energy resources, realizing energy-saving and cost-reducing target.Wind energy is the motive power of wind-power electricity generation, and wind energy resources characteristic determines the characteristic of wind power output power.By setting up rational wind measurement network, wind energy Real-Time Monitoring can be realized, concentrate the wind energy resources of developing zone to carry out Real-Time Monitoring to wind-powered electricity generation, understand and grasp wind energy resources situation of change and wind energy resources characteristic in time, and provide basic data for wind power prediction model exploitation and optimization etc.; Can be ultra-short term power prediction and wind energy turbine set theoretical power and generated energy to calculate and provide basic data, for wind-powered electricity generation management and running provide support; Also wind energy resources distribution situation and the feature of understanding Wind Power Generation region is conducive to.
In the development and utilization process of wind energy resources, anemometer tower is in very important position, is mainly manifested in the wind-resources assessment in wind energy turbine set early stage, wind field microcosmic structure, wind energy turbine set planning and design, wind energy turbine set wind regime Real-Time Monitoring, ultra-short term prediction, Numerical Prediction Models, the forecast output aspect such as comparing and numerical model parameter correction.Anemometer tower addressing point should be representative, can reflect the wind-resources situation of overlay area.
Summary of the invention
In order to overcome above-mentioned the deficiencies in the prior art, the invention provides a kind of wind measurement network layout optimization method based on analysis of data again, the principle of wind measurement network addressing is the maximal cover realizing region area with minimum anemometer tower quantity.Weather data adopts analysis of data again, considers to have the constraint conditions such as anemometer tower coverage in region, adopts genetic algorithm, is optimized the layout of anemometer tower.
In order to realize foregoing invention object, the present invention takes following technical scheme:
The invention provides a kind of wind measurement network layout optimization method based on analysis of data again, said method comprising the steps of:
Step 1: choose analysis of data again;
Step 2: adopt genetic algorithm optimization wind measurement network layout;
Step 3: judge whether wind measurement network layout area of effective coverage meets end condition, if meet, then exports the anemometer tower layout through optimizing; Otherwise, return step 2 and continue to perform.
In described step 1, from global atmosphere data again plan of analysis, choose analysis of data again; The analysis of data again chosen is NCEP/NCAR reanalysis datasets.
In described step 2, wind measurement network layout optimization target is under the condition of setting quantity anemometer tower, realizes the maximization of coverage.Step 2 specifically comprises the following steps:
Step 2-1: carry out integer coding to anemometer tower position, determine chromosome, generates initial population;
Step 2-2: definition fitness function, and K the individuality that the individuality selecting K fitness larger replaces fitness in initial population less;
Step 2-3: individual crossover and mutation;
Step 2-4: the renewal of population and termination.
In described step 2-2, if grid adds up to M in wind measurement network, the quantity of newly-increased anemometer tower is N, certain legal chromosome Q={q of grid arrangement
1, q
2..., q
n..., q
m, fitness function E is expressed as:
Wherein, A
ifor q
1the area of effective coverage that place's grid is corresponding.
In described step 2-3, adopt the method for semi-match to carry out individual interlace operation, from parent, select the sequence of certain group order set up filial generation and preserve the order of parent as far as possible; Quote polynomial expression and carry out individual variation operation, individual X in parent
ivariation is individual X in filial generation
i+1detailed process as follows:
(1) selected random number R
i∈ [0,1);
(2) the individual X set
iprobability density function P (δ
i) be expressed as:
Wherein,
for the profile exponent of user's setting, δ
ifor threshold parameter, be expressed as:
(3) through the offspring individual X obtained that makes a variation
i+1be expressed as:
Wherein,
with
be respectively subsidiary individual X
ibound.
In described step 2-4, parent individuality is added in population through the offspring individual that obtains of making a variation, and after variation produce random individual and also add population, to ensure population invariable number, complete the renewal of population;
By arranging greatest iteration step number N, and record often walks the offspring individual of renewal, when the offspring individual of continuous three times is constant, exports iteration result, population iteration ends.
In described step 3, the wind measurement network layout area of effective coverage through optimizing is calculated according to the fitness function of formula (1), if this area of effective coverage reaches 90% of the area coverage of anemometer tower region or more, then export this wind measurement network layout; Otherwise return step 2 to continue to perform.
Compared with prior art, beneficial effect of the present invention is:
Wind measurement network layout optimization method based on analysis of data again provided by the invention, on the basis of existing anemometer tower, using the correlativity again between the different grid of analysis of data as the representational evaluation index of region wind regime, adopt genetic algorithm, carry out the optimization of wind measurement network layout.Along with the difference of Questions types and the expansion of problem scale, this method can well solve search with limited cost and optimize, and has fabulous robustness and engineering practicability.
Accompanying drawing explanation
Fig. 1 is the wind measurement network layout optimization method process flow diagram based on analysis of data again in the embodiment of the present invention;
Fig. 2 carries out integer coding schematic diagram to anemometer tower position in the embodiment of the present invention;
Fig. 3 adopts genetic algorithm optimization wind measurement network layout process flow diagram in the embodiment of the present invention.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in further detail.
In the development and utilization process of wind energy resources, anemometer tower is in very important position, is mainly manifested in the wind-resources assessment in wind energy turbine set early stage, wind field microcosmic structure, wind energy turbine set planning and design, wind energy turbine set wind regime Real-Time Monitoring, ultra-short term prediction, Numerical Prediction Models, the forecast output aspect such as comparing and numerical model parameter correction.Anemometer tower addressing point should be representative, can reflect the wind-resources situation of overlay area.When region does not possess anemometer tower, in order to assessment area surveys the representativeness of wind, using between different grid again the correlativity of analysis of data as the representational evaluation index of region wind regime, and carry out the Optimizing Site Selection technical research of wind power resource observation network network on this basis.
As Fig. 1, the invention provides a kind of wind measurement network layout optimization method based on analysis of data again, said method comprising the steps of:
Step 1: choose analysis of data again from global atmosphere data again plan of analysis;
In order to make up the unequal defect of weather observation data spatial and temporal distributions, propose and utilize the Data Assimilation technology in numerical weather forecast to recover the method for long history climatic data, namely atmosphere data " is analyzed " again.It is exactly a kind ofly utilize the most perfect data assimilation system that the observational data in all kinds and source and short-term numerical weather forecast product are merged the process integrated with optimum again that atmosphere data " is analyzed " again.
At present, global atmosphere data again plan of analysis mainly contains: (1948 so far) NCEP/NCAR global atmosphere analysis of data plans again in 50 years of the pre-measured center of Environmental (NCEP) and Center for Atmospheric Research (NCAR), and the NCEP/DOE global atmosphere analysis of data plan again of NCEP and USDOE (DOE), 16 years (1980 ~ nineteen ninety-five) the global atmosphere analysis of data plan (NASA/DAO again of US National Aeronautics and Space Administration (NASA) Data Assimilation portion (DAO), also GEOS-1 is claimed), (ECMWF) 15 years (1979) 1993 years, medium-range numerical forecast center, Europe) global atmosphere analysis of data plan again (ERA-15) and 45 years (1957 ~ 2002 years) global atmospheres analysis of data plan again (ERA-40), 25 years (1979 ~ 2004 years) global atmospheres analysis of data plan again (JRA-25) etc. that Japan Meteorological Agency (JMA) and electric central research institute (CRIEPI) united organization are implemented.Wherein, NCEP/NCAR and NCEP/DOE, ERA-40 and JRA-25 apply reanalysis datasets comparatively widely at present.The analysis of data again chosen in the application is NCEP/NCAR reanalysis datasets.
Step 2: adopt genetic algorithm optimization wind measurement network layout;
Genetic algorithm is a kind of highly-parallel, random, self-adaptive search algorithm of using for reference organic sphere natural selection and evolutionary mechanism and growing up.Be different from traditional search and optimization method, it is the expansion of difference along with Questions types and problem scale, the method that can well solve search with limited cost and optimize.
As Fig. 3, wind measurement network layout optimization target is under the condition of setting quantity anemometer tower, realizes the maximization of coverage.Step 2 specifically comprises the following steps:
Step 2-1: carry out integer coding to anemometer tower position, determine chromosome, generates initial population;
In the genetic algorithm of standard, chromosome usually represents by binary coding, and we will use scale-of-two variation (mutation) and intersection (crossover) like this.Then this type of coded system cannot be used in wind measurement network Optimizing Site Selection, because the optimization space that this patent is selected is the net point of 5 × 5km resolution, if each net point is carried out number consecutively, then optimized variable is continuous print integer, therefore, position for anemometer tower adopts the mode of integer coding, as shown in Figure 2, for the grid of 2 × 2, each net point number consecutively is 1 ..., 9, then chromosome is divided into 9 sections, and wherein each section is the numbering of correspondence position, if 1|3|8|7|6|2|9|4|5| in this example is exactly a legal chromosome.
Step 2-2: select operation mainly in order to avoid the loss of effective gene, high performance individuality is survived with larger probability, thus reaches global convergence and counting yield.So definition fitness function, and K the individuality that the individuality selecting K fitness larger replaces fitness in initial population less, but the invariable number of colony will be kept in this process.This can make the average fitness value of colony to improve significantly by the method improved, and its speed of convergence also may be made to accelerate.
In described step 2-2, if grid adds up to M in wind measurement network, the quantity of newly-increased anemometer tower is N, certain legal chromosome Q={q of grid arrangement
1, q
2..., q
n..., q
m, fitness function E is expressed as:
Wherein, A
ifor q
1the area of effective coverage that place's grid is corresponding;
Step 2-3: individual crossover and mutation;
Adopt the method for semi-match to carry out individual interlace operation, from parent, select the sequence of certain group order set up filial generation and preserve the order of parent as far as possible; Quote polynomial expression and carry out individual variation operation, individual X in parent
ivariation is individual X in filial generation
i+1detailed process as follows:
(1) selected random number R
i∈ [0,1);
(2) the individual X set
iprobability density function P (δ
i) be expressed as:
Wherein,
for the profile exponent of user's setting, δ
ifor threshold parameter, be expressed as:
(3) through the offspring individual X obtained that makes a variation
i+1be expressed as:
Wherein,
with
be respectively subsidiary individual X
ibound;
Step 2-4: the renewal of population and termination;
In order to ensure that optimum solution is become better and better along with the increase of iterations when being allowed to condition at calculating, parent individuality is added in population through the offspring individual obtained that makes a variation, and after variation produce random individual and also add population, to ensure population invariable number, complete the renewal of population;
By arranging greatest iteration step number N, and record often walks the offspring individual of renewal, when the offspring individual of continuous three times is constant, exports iteration result, population iteration ends.
Step 3: calculate the wind measurement network layout area of effective coverage through optimizing according to the fitness function of formula (1), judge whether wind measurement network layout area of effective coverage meets end condition, if this area of effective coverage reaches 90% of the area coverage of anemometer tower region or more, then export this wind measurement network layout; Otherwise return step 2 to continue to perform.
Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit; those of ordinary skill in the field still can modify to the specific embodiment of the present invention with reference to above-described embodiment or equivalent replacement; these do not depart from any amendment of spirit and scope of the invention or equivalent replacement, are all applying within the claims of the present invention awaited the reply.
Claims (8)
1., based on a wind measurement network layout optimization method for analysis of data again, it is characterized in that: said method comprising the steps of:
Step 1: choose analysis of data again;
Step 2: adopt genetic algorithm optimization wind measurement network layout;
Step 3: judge whether wind measurement network layout area of effective coverage meets end condition, if meet, then exports the anemometer tower layout through optimizing; Otherwise, return step 2 and continue to perform.
2. the wind measurement network layout optimization method based on analysis of data more according to claim 1, is characterized in that: in described step 1, from global atmosphere data again plan of analysis, chooses analysis of data again; The analysis of data again chosen is NCEP/NCAR reanalysis datasets.
3. the wind measurement network layout optimization method based on analysis of data more according to claim 1, is characterized in that: in described step 2, and wind measurement network layout optimization target is under the condition of setting quantity anemometer tower, realizes the maximization of coverage.
4. the wind measurement network layout optimization method based on analysis of data again according to claim 1 or 3, is characterized in that: described step 2 specifically comprises the following steps:
Step 2-1: carry out integer coding to anemometer tower position, determine chromosome, generates initial population;
Step 2-2: definition fitness function, and K the individuality that the individuality selecting K fitness larger replaces fitness in initial population less;
Step 2-3: individual crossover and mutation;
Step 2-4: the renewal of population and termination.
5. the wind measurement network layout optimization method based on analysis of data more according to claim 4, is characterized in that: in described step 2-2, if grid adds up to M in wind measurement network, the quantity of newly-increased anemometer tower is N, certain legal chromosome Q={q of grid arrangement
1, q
2..., q
n..., q
m, fitness function E is expressed as:
Wherein, A
ifor q
1the area of effective coverage that place's grid is corresponding.
6. the wind measurement network layout optimization method based on analysis of data more according to claim 4, it is characterized in that: in described step 2-3, adopt the method for semi-match to carry out individual interlace operation, from parent, select the sequence of certain group order set up filial generation and preserve the order of parent as far as possible; Quote polynomial expression and carry out individual variation operation, individual X in parent
ivariation is individual X in filial generation
i+1detailed process as follows:
(1) selected random number R
i∈ [0,1);
(2) the individual X set
iprobability density function P (δ
i) be expressed as:
Wherein,
for the profile exponent of user's setting, δ
ifor threshold parameter, be expressed as:
(3) through the offspring individual X obtained that makes a variation
i+1be expressed as:
Wherein,
with
be respectively subsidiary individual X
ibound.
7. the wind measurement network layout optimization method based on analysis of data more according to claim 4, it is characterized in that: in described step 2-4, parent individuality is added in population through the offspring individual obtained that makes a variation, and after variation produce random individual and also add population, to ensure population invariable number, complete the renewal of population;
By arranging greatest iteration step number N, and record often walks the offspring individual of renewal, when the offspring individual of continuous three times is constant, exports iteration result, population iteration ends.
8. the wind measurement network layout optimization method based on analysis of data more according to claim 1, it is characterized in that: in described step 3, the wind measurement network layout area of effective coverage through optimizing is calculated according to the fitness function of formula (1), if this area of effective coverage reaches 90% of the area coverage of anemometer tower region or more, then export this wind measurement network layout; Otherwise return step 2 to continue to perform.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105956663A (en) * | 2016-03-04 | 2016-09-21 | 安徽工程大学 | Parallel stock layout optimization method of special shaped part blanking |
| CN108932554A (en) * | 2017-05-26 | 2018-12-04 | 西安交通大学 | The method for optimizing configuration and device of a kind of wind power plant flow field measuring point |
| CN109598064A (en) * | 2018-12-04 | 2019-04-09 | 华润电力技术研究院有限公司 | A kind of wind-resources zoning optimization method based on OpenFOAM |
| CN110490294A (en) * | 2019-07-17 | 2019-11-22 | 湖北工业大学 | Forecast of solar irradiance Data Assimilation algorithm based on parallel double population PSO |
| CN110598939A (en) * | 2019-09-18 | 2019-12-20 | 中国电建集团青海省电力设计院有限公司 | Method for improving wind measuring efficiency and reliability of wind measuring system |
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2014
- 2014-09-05 CN CN201410448787.8A patent/CN104268635A/en active Pending
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105956663A (en) * | 2016-03-04 | 2016-09-21 | 安徽工程大学 | Parallel stock layout optimization method of special shaped part blanking |
| CN108932554A (en) * | 2017-05-26 | 2018-12-04 | 西安交通大学 | The method for optimizing configuration and device of a kind of wind power plant flow field measuring point |
| CN108932554B (en) * | 2017-05-26 | 2021-03-23 | 西安交通大学 | Configuration optimization method and device for wind power plant flow field measurement points |
| CN109598064A (en) * | 2018-12-04 | 2019-04-09 | 华润电力技术研究院有限公司 | A kind of wind-resources zoning optimization method based on OpenFOAM |
| CN109598064B (en) * | 2018-12-04 | 2023-06-02 | 华润电力技术研究院有限公司 | Wind resource calculation region optimizing method based on OpenFOAM |
| CN110490294A (en) * | 2019-07-17 | 2019-11-22 | 湖北工业大学 | Forecast of solar irradiance Data Assimilation algorithm based on parallel double population PSO |
| CN110598939A (en) * | 2019-09-18 | 2019-12-20 | 中国电建集团青海省电力设计院有限公司 | Method for improving wind measuring efficiency and reliability of wind measuring system |
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Application publication date: 20150107 |