CN109217367B - Wind power generation prediction method, device and equipment - Google Patents
Wind power generation prediction method, device and equipment Download PDFInfo
- Publication number
- CN109217367B CN109217367B CN201811165404.0A CN201811165404A CN109217367B CN 109217367 B CN109217367 B CN 109217367B CN 201811165404 A CN201811165404 A CN 201811165404A CN 109217367 B CN109217367 B CN 109217367B
- Authority
- CN
- China
- Prior art keywords
- wind power
- measured
- point
- meteorological
- data
- 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.)
- Expired - Fee Related
Links
- 238000010248 power generation Methods 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000004364 calculation method Methods 0.000 claims abstract description 35
- 238000012545 processing Methods 0.000 claims description 18
- 238000005516 engineering process Methods 0.000 claims description 14
- 238000003708 edge detection Methods 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 230000006870 function Effects 0.000 description 6
- 238000012937 correction Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000001914 filtration Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000007664 blowing Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 238000013179 statistical model Methods 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- H02J3/386—
-
- 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
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Wind Motors (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a wind power generation prediction method, a wind power generation prediction device and wind power generation prediction equipment, and belongs to the technical field of power generation prediction. The embodiment of the invention provides a wind power generation prediction method, a wind power generation prediction device and wind power generation prediction equipment, wherein a meteorological value of a point to be measured is obtained at regular time, wind power data is extracted from the meteorological value of the point to be measured, then the wind power data is processed to convert the wind power data into a plurality of types of data, then a calculation factor of the point to be measured is obtained, the plurality of types of data are processed by combining the calculation factor to determine a corrected wind power value, and finally the power generation power of the point to be measured is predicted according to the corrected wind power value, so that the output power of a wind power plant for a long time can be accurately predicted, and the accuracy of power grid scheduling task allocation.
Description
Technical Field
The invention relates to the technical field of power generation prediction, in particular to a method, a device and equipment for predicting wind power generation.
Background
In recent years, energy conservation and emission reduction, green energy, low-carbon economy development and sustainable development become the focus of attention of all countries, the proportion of clean energy and renewable energy in energy of all countries is more and more, and especially wind energy becomes the energy with the top development speed at present. The prime power of wind power generation is wind energy which has strong randomness and volatility, so that the wind power accessed to a power grid presents a violent fluctuation characteristic.
At present, in order to reduce the influence of wind power integration on a system, the wind power of a wind power plant needs to be predicted in advance, the first method is to directly predict the output power of the wind power plant, the voltage and current data of each wind driven generator in a large wind power plant are needed, for example, each wind driven generator is regarded as a data acquisition device, so that the information contained in the input time sequence data in a power generation power prediction model of the whole wind power plant is more comprehensive and more accurate, but the prediction time of the method is shorter, and the method is not suitable for daily prediction; the second method is to predict the wind speed and then obtain the power output of the wind farm according to the power curve of the wind turbine or the wind farm, and the method adopts a statistical model. At present, statistical model prediction methods mainly include kalman filtering method, random time series method, fuzzy logic method, artificial neural network method (ANN), mixed expert experience method (mixture of experiments ME), Nearest Neighbor Search (NNS), ant colony optimization (PSO), Support Vector Machine (SVM), and the like. By utilizing the algorithms, the relation between the weather condition and the output of the wind power plant can be found out according to historical statistical data without considering the physical process of wind speed change, and then the data power of the wind power plant is predicted according to the measured data and the data of numerical weather forecast (NWP), but the method can meet the precision requirement on the wind power prediction result which is 3-4 hours in advance, but the precision is not accurate for the prediction result which is longer in advance, so that the power grid scheduling task cannot be accurately performed.
Disclosure of Invention
The invention provides a wind power generation prediction method, a wind power generation prediction device and wind power generation prediction equipment, aiming at the problems that the wind power generation prediction method in the prior art can only adapt to short-term prediction, but the prediction precision for a longer time is not very accurate, and the power grid scheduling task cannot be accurately carried out.
In a first aspect, an embodiment of the present invention provides a wind power generation prediction method, where the method includes: acquiring meteorological values of points to be measured at regular time;
extracting wind power data from the meteorological values of the point to be measured, and processing the wind power data to convert the wind power data into a plurality of class data;
acquiring a calculation factor of the point to be measured;
processing the plurality of class data in combination with the calculation factors to determine a corrected wind power value;
and predicting the generated power of the point to be measured according to the corrected wind power value.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the step of obtaining the meteorological data of the point to be measured at regular time includes:
acquiring first weather information on a weather station webpage of a point to be measured at regular time through an Internet, and displaying the first weather information on a screen;
the method comprises the steps of shooting first weather information displayed on a screen at regular time through a power system network, and converting the first weather information into weather values of a point to be measured.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the method for periodically capturing, by a power system network, first weather information displayed on a screen and converting the first weather information into a weather value of a point to be measured includes:
and converting the first meteorological information into meteorological values of the point to be measured by adopting an image edge detection technology and an identification technology.
With reference to the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the step of obtaining the calculation factor of the point to be measured includes:
acquiring second meteorological information on weather station webpages in east, west, south and north directions of a point to be measured at regular time through an Internet, and displaying the second meteorological information on a screen;
and shooting second meteorological information displayed on a screen at fixed time through the power system network, and taking the second meteorological information as a calculation factor.
With reference to the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the step of predicting the generated power of the point to be measured according to the corrected wind power value includes:
finding out daily average power generation amount corresponding to the corrected wind power value from a preset mass database;
and predicting the generated power of the point to be measured according to the daily average generated energy.
With reference to the first possible implementation manner of the first aspect, the embodiment of the present invention provides a fifth possible implementation manner of the first aspect, wherein the frequency of periodically acquiring the first weather information on the weather station webpage of the point to be measured through the Internet network is equal to the frequency of periodically shooting the first weather information displayed on the screen through the power system network.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where a start time of the power system network differs from a start time of the Internet network by one hour.
In a second aspect, an embodiment of the present invention further provides a wind power generation prediction apparatus, where the wind power generation prediction apparatus includes:
the first acquisition module is used for acquiring the meteorological value of the point to be measured in a timing manner;
the first conversion module is used for extracting wind power data from the meteorological numerical values of the point to be measured and processing the wind power data so as to convert the wind power data into a plurality of types of data;
the second acquisition module is used for acquiring the calculation factor of the point to be measured;
the determining module is used for processing the plurality of types of data by combining the calculation factors to determine a corrected wind power value;
and the prediction module is used for predicting the power generation power of the point to be measured according to the corrected wind power value.
With reference to the second aspect, an embodiment of the present invention provides a first possible implementation manner of the second aspect, where the apparatus further includes:
and the second conversion module is used for converting the first meteorological information into a meteorological value through an image edge detection technology and an identification technology.
In a third aspect, an embodiment of the present invention further provides a wind power generation prediction apparatus, where the wind power generation prediction apparatus includes: a memory for storing and supporting a processor to execute a program of any of the methods of the first aspect, and a processor configured to execute the program stored in the memory.
The embodiment of the invention has the following beneficial effects:
according to the wind power generation prediction method, the device and the equipment provided by the embodiment of the invention, the meteorological values of the points to be measured are obtained at regular time, the wind power data are extracted from the meteorological values of the points to be measured, then the wind power data are processed, so that the wind power data are converted into a plurality of types of data, the calculation factors of the points to be measured are obtained, the plurality of types of data are processed by combining the calculation factors, the corrected wind power values are determined, and finally the power generation power of the points to be measured is predicted according to the corrected wind power values, so that the output power of the wind power plant for a long time can be accurately predicted, and the accuracy of the distribution of the scheduling tasks.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a wind power generation prediction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a wind power generation prediction method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a wind power generation predicting apparatus according to another embodiment of the present invention;
fig. 4 is a block diagram of a wind power generation prediction device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a wind power generation prediction method, a device and equipment, and firstly introduces the wind power generation prediction method in detail.
Example one
The embodiment provides a wind power generation prediction method, as shown in fig. 1, the method includes:
and S102, acquiring the meteorological value of the point to be measured at regular time.
In order to ensure the safety of the power system network, first weather information on a weather station webpage of a point to be measured is acquired at regular time through the Internet, the first weather information is displayed on a screen, then the first weather information displayed on the screen is shot at regular time through the power system network, and the first weather information is converted into a weather numerical value of the point to be measured.
Specifically, the first weather information can include wind power, wind direction and wind speed of the point to be measured, three types of application program development interfaces are provided in front of the central weather station, each type of interface outputs weather information in a standard JSON format, the application program development interfaces of the central weather station are accessed at regular time through each type of interface, and weather forecast information of the point to be measured and five measuring points including the east, south, west and north of the point to be measured are analyzed from the output JSON file. In order to obtain the first weather information, the first weather information of the point to be measured can be accessed through an interface corresponding to the point to be measured, and the first weather information is displayed on a display screen in a form of coded numbers, for example, the wind direction in the first weather information is displayed according to eight directions: the north, the northeast, the east, the southeast, the south, the southwest, the west and the northwest are classified and respectively represented by Arabic numerals 1,2, 3, 4, 5, 6, 7 and 8, and for example, wind power refers to the force exerted by wind blowing on an object, and is generally classified into 13 grades, the minimum grade is 0 grade and the maximum grade is 12 grades according to various phenomena generated by wind blowing on the object on the ground or on the water surface. However, in general, 11-class and 12-class are almost impossible to occur on land, and the wind power on land is generally between 0 and 10-class more.
It is understood that a camera, such as a camera, is provided on the power system network. When wind power generation is carried out, the power system network starts the camera at regular time, weather information displayed on the screen is photographed, and the weather information is converted into a weather numerical value of a point to be measured. It should be noted that, in order to ensure that the weather information acquired by the Internet network is not missed, the timing frequency of shooting the weather information displayed on the screen through the Internet network is equal to the timing frequency of acquiring the weather information on the weather station web page through the power system network, but the start time of the power system network differs from the start time of the Internet network by one hour.
In order to obtain the meteorological value of the point to be measured, an image edge detection technology and an identification technology are required to convert meteorological information into the meteorological value of the point to be measured. Specifically, the embodiment of the invention processes the image by using the laplacian gaussian algorithm, namely weather information displayed on a screen. The method combines Gaussian filtering and Laplace detection operators together for edge detection, and the method mainly comprises the following steps:
(1) filtering: the meteorological information displayed on the screen, i.e. the image f (x, y), is first filtered smoothly, selecting the filter function as a gaussian function, i.e.:
where G (x, y) is a circularly symmetric function whose smoothing effect is controllable by σ. Convolving image G (x, y) with f (x, y) yields a smoothed image, namely:
g(x,y)＝f(x,y)*G(x,y)
(2) enhancing: the smoothed image g (x, y) is subjected to laplacian operations, namely:
(3) and (3) detection: the edge detection criterion is the zero crossing (i.e., point) of the second derivative and corresponds to a larger peak of the first derivative. And combining a Gaussian smoothing filter and a Laplace sharpening filter by adopting a Gaussian-Laplace operator, flattening noise, and then carrying out edge detection.
(4) Identification: the numbers in the image are identified by matching the detected boundaries of the regional objects with the intrinsic edge characteristics of the numbers 0,1,2, …, 9 and the ". quadrature.. And then the weather information is converted into the weather value of the point to be measured by utilizing the identified numbers.
And step S104, extracting wind power data from the meteorological values of the points to be measured, and processing the wind power data to convert the wind power data into a plurality of types of data.
In order to improve the calculation accuracy, before calculating the output power of the wind farm, the embodiment adopts a K-means algorithm to set a K value, and divides the sample data into a plurality of gradient data. Specifically, the accuracy of the sample data is improved by setting a K value by adopting a K-means clustering algorithm in the Mahout. For example, selecting a meteorological value with wind power of 0-10 level, and taking k as 110, the sample data can be divided into 110 gradient data with the accuracy of 0.1. Therefore, when the wind farm output power is calculated, all integer and decimal data in 0.1, 0.2 and 0.3 … 10.9.9 can be selected as sample data to calculate the output power of the wind farm, and the calculation accuracy is improved.
And step S106, obtaining a calculation factor of the point to be measured.
As shown in fig. 2, in calculating the output power, the influence of the measuring points in the regions of the south, east, west and north on the point to be measured is taken into account, and the influence is related to the distances from the measuring points in the regions of the south, east, west and north to the point to be measured, so that the wind power can be converted into the corresponding wind speed, that is, the distance of air flowing in unit time, which is expressed in meters/second. Wherein the wind speed of grade 1 wind is equal to 1 m/s, and the wind speed of grade 2 wind is equal to 2 m/s. The wind level of the 3-level wind is added with 1, and the wind speed is equal to 4 m/s. The 4 to 9 stages subtract 2 and multiply by 3 on the series to obtain the wind speed of the corresponding stage. The wind speed algorithm of stages 10 to 12 is: the wind speed of 10 grade is 27 m/s, on the basis, the wind speed of 11 grade is increased by 4 to be 31 m/s, and the wind speed of 12 grade is increased by 4 to be 35 m/s, so that the calculation factor of the point to be measured is obtained.
And step S108, processing the multiple kinds of data by combining the calculation factors, and determining the corrected wind power value.
Specifically, a calculation factor in the sample data needs to be extracted, then a weight value is calculated according to the calculation factor, and then a correction wind power value is determined according to the weight value. The specific idea is as follows:
(1) calculating a weight value:
wherein x ∈ [ e, s, w, n ∈ [ ]]I is the ith measurement, wi,xIs the wind speed, dxFor the distance from east to west, south and northDistance, omega, from the measuring point of the zone to the central measuring pointi,eIs a weight value.
(2) Calculating output power:
Ai＝ωi,e*(wi,e-wi)+ωi,s*(wi,s-wi)+ωi,w*(wi,w-wi)+ωi,n*(wi,n-wi)；
ki＝wi*10+Miwherein A isiIs the output power value. However, when the calculation factor of wind power is actually acquired, the influence of measuring points in regions from east, west, south and north to the central measuring point is difficult to exceed 10, so that the accuracy of calculation is improved by extending each calculated output power by ten times, namely, the corrected wind power value is determined.
And step S110, predicting the generated power of the point to be measured according to the corrected wind power value. .
In order to increase the accuracy of the weather value statistics with the increase in the number of statistics and to hopefully store weather values for the last ten years or several decades and once an hour, Hbase (distributed, column-oriented, open source database) is used as a database for storing weather values by using the distributed mass data processing technique. The HBase is a platform which is established on hdfs (Hadoop distributed file system) and used for storing unstructured and semi-structured loose data, is a database system which can provide high reliability, high performance, column storage, scalability, real-time reading and writing, is between nosql (non-relational database) and RDBMS (relational database management system), can only retrieve data through a main key (row key) and a range of the main key, and only supports single-row transactions. Meanwhile, the device can be seamlessly integrated with Hadoop, the calculation and storage capacity is increased by mainly depending on transverse expansion and continuously adding cheap commercial servers, then the obtained correction wind power value is compared with the average power generation amount stored in distributed mass data, and the average power generation amount corresponding to the correction wind power value is used as the output power of a point to be measured.
The embodiment of the invention provides a wind power generation prediction method, which comprises the steps of obtaining a meteorological number of a point to be measured at regular time, extracting wind power data from the meteorological number of the point to be measured, processing the wind power data to convert the wind power data into a plurality of types of data, obtaining a calculation factor of the point to be measured, processing the plurality of types of data by combining the calculation factor, determining a corrected wind power number, predicting the power generation power of the point to be measured according to the corrected wind power number, improving the calculation accuracy, accurately predicting the output power of wind power generation for a long time and improving the accuracy of power grid scheduling task allocation.
Example two
In correspondence with the above method embodiment, the present embodiment provides a wind power generation prediction apparatus, as shown in fig. 3, the apparatus including:
the first obtaining module 31 is configured to obtain a meteorological value of a point to be measured at regular time.
And the first conversion module 32 is used for extracting the wind power data from the meteorological values of the points to be measured and processing the wind power data so as to convert the wind power data into a plurality of types of data.
And a second obtaining module 33, configured to obtain a calculation factor of the point to be measured.
And the determining module 34 is used for processing the plurality of types of data by combining the calculation factors to determine the corrected wind power value.
And the prediction module 35 is used for predicting the power generation power of the point to be measured according to the corrected wind power value.
In an optional embodiment, the wind power generation prediction device further comprises: and the second conversion module is used for converting the meteorological information into a meteorological numerical value of the point to be measured through an image edge detection technology and an identification technology.
The embodiment of the invention provides a wind power generation prediction device, which is characterized in that a meteorological value of a point to be measured is obtained at regular time, then wind power data is extracted from the meteorological value of the point to be measured and processed, so that the wind power data is converted into a plurality of types of data, then a calculation factor of the point to be measured is obtained, the plurality of types of data are processed by combining the calculation factor, a corrected wind power value is determined, and finally the power generation power of the point to be measured is predicted according to the corrected wind power value, so that the calculation accuracy is improved, meanwhile, the output power of wind power generation for a long time can be predicted more accurately, and the accuracy of power grid scheduling task allocation is improved.
EXAMPLE III
On the basis of the above embodiments, the present disclosure provides a wind power generation prediction device, as shown in fig. 4, the electronic device includes: a processor 41, a memory 42, a display unit 44, and a camera 45, and the respective unit modules of the wind power generation predicting apparatus are connected by a bus 43. The memory 42 may be configured to store software programs and modules, such as program instructions/modules corresponding to the wind power generation predicting device in the embodiment of the present invention, and the processor 41 may execute corresponding various functional applications and data processing by running the software programs and modules stored in the memory 42 and storing data displayed by the display unit 44 and data acquired by the camera 45, such as the wind power generation predicting method provided in the embodiment of the present invention, and the memory 42 may mainly include a storage program area and a storage data area, where the storage program area may store an operating system, an application program required by at least one function (such as the wind power generation predicting method in the embodiment of the present invention), and the like; the storage data area may store data (such as weather information/weather values) created from the use of the wind power generation prediction device, and the like. Further, the memory 42 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 41 is a control center of the wind power generation predicting apparatus, connects various parts of the entire apparatus using various interfaces and lines, and performs various functions and processes data by operating or executing software programs and/or modules stored in the memory 42 and calling data stored in the memory 42, thereby performing overall monitoring of the wind power generation predicting apparatus. Alternatively, processor 41 may include one or more processing units.
And the display unit 44 is used for displaying the meteorological information acquired by the Internet in a digital coding mode.
The cameras 45 can shoot the weather information on the display unit 44 at regular time, and optionally, one or more cameras 45 can be arranged on the power system network, and the plurality of cameras 45 can shoot the weather information for a plurality of times and store the shot weather information in the database.
And the bus 43 is connected with each module unit of the wind power generation prediction device and is used for sending control instruction information to the processor 41, transmitting information stored in the memory 42 to the processor 41, and sending data acquired by the camera 45 and data displayed by the display unit 44 to the processor 41.
Embodiments of the present invention may also provide wind power generation prediction devices that include more or fewer components than those shown in FIG. 4, or that have a different configuration than that shown in FIG. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.
The wind power generation prediction method, the device and the equipment provided by the embodiment of the invention have the same technical characteristics, so the same technical problems can be solved, and the same technical effects can be achieved.
It should be noted that, in the embodiments provided in the present invention, it should be understood that the disclosed system and method can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided by the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (7)
1. A method for predicting wind power generation, comprising:
acquiring meteorological values of points to be measured at regular time; the step of regularly acquiring the meteorological number of the point to be measured comprises the following steps: acquiring first weather information on a weather station webpage of a point to be measured at regular time through an Internet, and displaying the first weather information on a screen; shooting first weather information displayed on a screen at regular time through a power system network, and converting the first weather information into a weather value of a point to be measured;
the method for regularly shooting the first meteorological information displayed on the screen through the power system network and converting the first meteorological information into the meteorological value of a point to be measured comprises the following steps: converting the first meteorological information into meteorological values of the point to be measured by adopting an image edge detection technology and an identification technology;
extracting wind power data from the meteorological values of the point to be measured, and processing the wind power data to convert the wind power data into a plurality of class data;
acquiring a calculation factor of the point to be measured; the step of obtaining the calculation factor of the point to be measured comprises the following steps: acquiring second meteorological information on weather station webpages in east, west, south and north directions of a point to be measured at regular time through an Internet, and displaying the second meteorological information on a screen; shooting second meteorological information displayed on a screen at regular time through a power system network, and taking the second meteorological information as a calculation factor;
processing the plurality of class data in combination with the calculation factors to determine a corrected wind power value;
and predicting the generated power of the point to be measured according to the corrected wind power value.
2. The method according to claim 1, wherein the step of predicting the generated power of the point to be measured according to the corrected wind power value comprises the following steps:
finding out daily average power generation amount corresponding to the corrected wind power value from a preset mass database;
and predicting the generated power of the point to be measured according to the daily average generated energy.
3. The method as claimed in claim 1, wherein the frequency of the timing acquisition of the first weather information on the weather station webpage of the point to be measured through the Internet network is equal to the frequency of the timing shooting of the first weather information displayed on the screen through the power system network.
4. The method of claim 1, wherein the start-up time of the power system network differs from the start-up time of the Internet network by one hour.
5. A wind power generation prediction device, comprising:
the first acquisition module is used for acquiring the meteorological value of the point to be measured in a timing manner; the first obtaining module comprises: the system comprises a first acquisition unit, a second acquisition unit and a display unit, wherein the first acquisition unit is used for acquiring first weather information on a weather station webpage of a point to be measured at fixed time through the Internet and displaying the first weather information on a screen; the first conversion unit is used for shooting first meteorological information displayed on a screen at regular time through a power system network and converting the first meteorological information into a meteorological numerical value of a point to be measured;
the first conversion unit is also used for converting the first meteorological information into a meteorological numerical value of a point to be measured by adopting an image edge detection technology and an identification technology;
the first conversion module is used for extracting wind power data from the meteorological numerical values of the point to be measured and processing the wind power data so as to convert the wind power data into a plurality of types of data;
the second acquisition module is used for acquiring the calculation factor of the point to be measured; the second acquisition module includes: the second acquisition unit is used for acquiring second meteorological information on weather station webpages in the east, the west, the south and the north of the point to be measured at regular time through the Internet and displaying the second meteorological information on a screen; the second conversion unit is used for shooting second meteorological information displayed on a screen at fixed time through the power system network and taking the second meteorological information as a calculation factor;
the determining module is used for processing the plurality of types of data by combining the calculation factors to determine a corrected wind power value;
and the prediction module is used for predicting the power generation power of the point to be measured according to the corrected wind power value.
6. The apparatus of claim 5, further comprising:
and the second conversion module is used for converting the first meteorological information into a meteorological value through an image edge detection technology and an identification technology.
7. A wind power generation prediction device, comprising: a memory for storing and supporting a processor to execute a program of the method of any one of claims 1 to 4, and a processor configured to execute the program stored in the memory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811165404.0A CN109217367B (en) | 2018-09-30 | 2018-09-30 | Wind power generation prediction method, device and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811165404.0A CN109217367B (en) | 2018-09-30 | 2018-09-30 | Wind power generation prediction method, device and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109217367A CN109217367A (en) | 2019-01-15 |
| CN109217367B true CN109217367B (en) | 2020-11-06 |
Family
ID=64983082
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811165404.0A Expired - Fee Related CN109217367B (en) | 2018-09-30 | 2018-09-30 | Wind power generation prediction method, device and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109217367B (en) |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100802402B1 (en) * | 2006-06-15 | 2008-02-14 | 한국에너지기술연구원 | Forecasting method of wind power generation by classification of wind speed patterns |
| CN101493312B (en) * | 2009-03-06 | 2011-01-12 | 上海市激光技术研究所 | Micro imaging high precision three-dimensional detection device and method |
| US8600572B2 (en) * | 2010-05-27 | 2013-12-03 | International Business Machines Corporation | Smarter-grid: method to forecast electric energy production and utilization subject to uncertain environmental variables |
| US9069103B2 (en) * | 2010-12-17 | 2015-06-30 | Microsoft Technology Licensing, Llc | Localized weather prediction through utilization of cameras |
| CN202167895U (en) * | 2011-02-11 | 2012-03-14 | 宁夏回族自治区电力公司 | Wind/photovoltaic power predication system |
| JP6400467B2 (en) * | 2014-12-26 | 2018-10-03 | 株式会社日立パワーソリューションズ | Wind power generation forecasting method |
| CN106372749B (en) * | 2016-08-29 | 2019-07-02 | 南京国电南自维美德自动化有限公司 | Ultra-short term photovoltaic power prediction technique based on the analysis of cloud variation |
| CN108133182B (en) * | 2017-12-18 | 2020-06-26 | 国能日新科技股份有限公司 | New energy power generation prediction method and device based on cloud imaging |
-
2018
- 2018-09-30 CN CN201811165404.0A patent/CN109217367B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN109217367A (en) | 2019-01-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11408399B2 (en) | Forecasting output power of wind turbine in wind farm | |
| CN110750524A (en) | Method and system for determining fault characteristics of active power distribution network | |
| Jung et al. | Development of onshore wind turbine fleet counteracts climate change-induced reduction in global capacity factor | |
| KR101285044B1 (en) | Decision method for spillway discharge of hydroelectronic power plant at a flood season | |
| CN107437135B (en) | Novel energy storage type selection method | |
| CN110543987A (en) | Intelligent climate prediction system | |
| Zhu et al. | A decision tree model for meteorological disasters grade evaluation of flood | |
| CN117332291B (en) | Distributed photovoltaic-oriented regional resource monitoring layout method and system | |
| CN109242218B (en) | Method and device for predicting wind power in plateau mountain area based on SVM | |
| Li et al. | Prediction of wind turbine blades icing based on CJBM with imbalanced data | |
| CN116993227B (en) | Heat supply analysis and evaluation method, system and storage medium based on artificial intelligence | |
| CN109217367B (en) | Wind power generation prediction method, device and equipment | |
| CN108932554B (en) | Configuration optimization method and device for wind power plant flow field measurement points | |
| Fan et al. | Evaluation of environmental quality based on remote sensing data in the coastal lands of eastern China | |
| Omary et al. | An interactive predictive system for weather forecasting | |
| CN116484468A (en) | Risk assessment method, device and equipment for reservoir dam and storage medium | |
| Sachindra et al. | Potential improvements to statistical downscaling of general circulation model outputs to catchment streamflows with downscaled precipitation and evaporation | |
| Ulbricht et al. | Optimized renewable energy forecasting in local distribution networks | |
| CN110019343A (en) | A kind of new energy meteorological data management method and system | |
| Zhang et al. | Fault Prediction Method for Distribution Network Outage Based on Feature Selection and Ensemble Learning | |
| CN110175639A (en) | A kind of short-term wind power forecast method based on Feature Selection | |
| Wang et al. | Integrative probabilistic short-term prediction and uncertainty quantification of wind power generation | |
| CN115858895B (en) | Multi-source heterogeneous data processing method and system for smart city | |
| CN117353302B (en) | New energy power generation power prediction method, device, equipment and medium | |
| CN114048614B (en) | Method and device for predicting environmental pollution |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20201106 Termination date: 20210930 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |