CN111652431B - Wind power plant power prediction method, device, equipment and storage medium - Google Patents
Wind power plant power prediction method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method for predicting power of a wind power plant, which comprises the following steps: determining a reference wind power plant and further determining a predictable area; selecting a wind power plant to be predicted in the predictable area, acquiring the wind field distance between the reference wind power plant and the wind power plant to be predicted, and determining a wind direction angle according to the reference wind power plant and the wind power plant to be predicted; acquiring a first wind speed and a first total active power of the whole wind field at the reference wind field, acquiring a second wind speed at the wind field to be predicted, and acquiring a wind energy loss coefficient according to the first wind speed and the second wind speed; determining window time according to the wind field distance, the wind direction angle and the first wind speed, and calculating the second total active power of the wind power plant to be predicted after the window time according to the first total active power and the wind energy loss coefficient. The invention also discloses a wind power plant power prediction device, equipment and a storage medium, which realize accurate prediction of the ultra-short-term power of the wind power plant.
Description
Technical Field
The invention relates to the technical field of wind power data processing, in particular to a method, a device, equipment and a storage medium for predicting power of a wind power plant.
Background
With the increasingly mature wind power generation technology, the capacity of a single wind power machine and the scale of a grid-connected wind power plant are continuously enlarged, and the proportion of wind power in the total power generation amount of a power system is increased year by year. The penetration power of a wind power plant is continuously increased, a series of problems brought to an electric power system are increasingly prominent, serious threats are caused, and the electric power system is safe, stable, economical and reliable in operation.
The existing wind power prediction method generally uses historical data to guide prediction or generates prediction data after a large amount of training through a machine learning method, the prediction result is often not accurate due to long data acquisition time, and the machine learning method has the problem of high implementation difficulty.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for predicting power of a wind power plant, and aims to solve the technical problem that the ultra-short-term power of the wind power plant cannot be accurately predicted.
In order to achieve the above object, the present invention provides a wind farm power prediction method, comprising the steps of:
in one embodiment, the wind farm power prediction method comprises the following steps:
determining a reference wind power plant, and determining a predictable area according to the wind direction at the position of the reference wind power plant;
selecting a wind power plant to be predicted in the region capable of being predicted, obtaining a wind field distance between the reference wind power plant and the wind power plant to be predicted, and determining a wind direction angle according to the reference wind power plant and the wind power plant to be predicted;
acquiring a first wind speed and a first total active power of the whole wind field at the reference wind field, acquiring a second wind speed at the wind field to be predicted, and acquiring a wind energy loss coefficient according to the first wind speed and the second wind speed;
and determining window time according to the wind field distance, the wind direction angle and the first wind speed, and calculating second full field total active power of the wind power plant to be predicted after the window time passes according to the first full field total active power and the wind energy loss coefficient.
In one embodiment, the step of determining a reference wind farm, and determining a predictable area based on wind direction at the location of the reference wind farm, comprises:
selecting a wind power plant in a preset area as a reference wind power plant;
and according to the current wind direction of the reference wind farm, taking a region positioned at the downwind direction of the reference wind farm in a preset region as a predictable region.
In an embodiment, the step of determining a wind direction angle according to the reference wind farm and the wind farm to be predicted includes:
determining a straight line according to the reference wind power plant and the wind power plant to be predicted;
and determining an included angle between the straight line and the wind direction, and taking the included angle as a wind direction angle.
In an embodiment, the step of determining a window time according to the wind field distance, the wind direction angle and the first wind speed includes:
inputting the wind field distance and the wind direction angle into a first preset formula to calculate the wind direction distance, wherein the first preset formula is as follows: sk = Sab cos α; the Sk is a wind direction distance, the Sab is a wind field distance, and the alpha is a wind direction angle;
inputting the wind direction distance and the first wind speed into a second preset formula, and determining the window time, wherein the second preset formula is as follows: x = Sk/Wa; the x is window time, the Sk is wind direction distance, and the Wa is first wind speed.
In an embodiment, the step of obtaining a wind energy loss factor based on the first wind speed and the second wind speed comprises:
inputting the first wind speed and the second wind speed into a third preset formula to obtain a wind energy loss coefficient, wherein the third preset formula is as follows: kx = Wb 3 /Wa 3 (ii) a And the Kx wind energy loss coefficient, wb is a second wind speed, and Wa is a first wind speed.
In an embodiment, the step of calculating, according to the first full-farm total active power and the wind energy loss coefficient, a second full-farm total active power of the wind farm to be predicted after the window time elapses includes:
inputting the first full-field total active power and the wind energy loss coefficient into a fourth preset formula to obtain a second full-field total active power of the wind power plant to be predicted after the window time, wherein the fourth preset formula is as follows: pbx = Kx × K × Pa; and Pbx is the second full-field total active power, kx is the wind energy loss coefficient, K is a constant, and Pa is the first full-field total active power.
In an embodiment, after the step of calculating the second full-farm total active power of the wind farm to be predicted after the window time elapses according to the first full-farm total active power and the wind energy loss coefficient, the method includes:
taking each wind power plant in the predictable area as a wind power plant to be predicted, and acquiring the window time of each wind power plant to be predicted in the predictable area and the second total active power of the wind power plant to be predicted after the window time;
and generating a wind power dispatching instruction according to the second full active power, and sending the wind power dispatching instruction to a dispatching terminal so that the dispatching terminal carries out wind power dispatching according to the wind power dispatching instruction.
In addition, to achieve the above object, the present invention further provides a wind farm power prediction device, including:
a region determination module: the method comprises the steps of determining a reference wind power plant, and determining a predictable area according to the wind direction at the position of the reference wind power plant;
a first data acquisition module: the wind power field prediction method comprises the steps of selecting a wind power field to be predicted in the predictable area, obtaining the wind field distance between the reference wind power field and the wind power field to be predicted, and determining a wind direction angle according to the reference wind power field and the wind power field to be predicted;
the second data acquisition module: the wind power generation system is used for acquiring a first wind speed and a first total active power at the reference wind power plant, acquiring a second wind speed at the wind power plant to be predicted, and acquiring a wind energy loss coefficient according to the first wind speed and the second wind speed;
a prediction module: and the wind power generation system is used for determining window time according to the wind field distance, the wind direction angle and the first wind speed, and calculating second full field total active power of the wind power plant to be predicted after the window time according to the first full field total active power and the wind energy loss coefficient.
In addition, in order to achieve the purpose, the invention also provides a wind power plant power prediction device;
the wind farm power prediction device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein:
the computer program, when executed by the processor, implements the steps of the wind farm power prediction method as described above.
In addition, to achieve the above object, the present invention also provides a computer storage medium;
the computer storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the wind farm power prediction method as described above.
According to the method, the device, the equipment and the storage medium for predicting the power of the wind power plant, a reference wind power plant is determined, and a predictable area is determined according to the wind direction at the position of the reference wind power plant; selecting a wind power plant to be predicted in the region to be predicted, obtaining a wind field distance between the reference wind power plant and the wind power plant to be predicted, determining a wind direction angle according to the reference wind power plant and the wind power plant to be predicted, obtaining a first wind speed and a first total active power at the reference wind power plant, and obtaining a second wind speed at the wind power plant to be predicted, wherein the calculation parameters can be obtained relatively simply and conveniently by using the prior art, a wind energy loss coefficient is obtained according to the first wind speed and the second wind speed, and losses of factors such as terrain and friction to wind energy are fully considered; determining window time according to the wind field distance, the wind direction angle and the first wind speed, namely embodying a predictable time point, calculating the second full active power of the wind power plant to be predicted after the window time passes according to the first full active power and the wind energy loss coefficient, predicting the power of the wind power plant to be predicted in an ultra-short time according to current parameters obtained from a reference wind power plant and parameters such as the distance between the reference wind power plant and the wind power plant to be predicted, and realizing more accurate and simple prediction of the ultra-short-term power of the wind power plant.
Drawings
FIG. 1 is a schematic diagram of an apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a wind farm power prediction method according to a first embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Because the prior art does not have a method for accurately predicting the ultra-short term power of the wind power plant, the invention provides a solution, a predictable area is determined according to the wind direction at the position of a reference wind power plant by determining the reference wind power plant; selecting a wind power plant to be predicted in the region capable of being predicted, obtaining a wind field distance between the reference wind power plant and the wind power plant to be predicted, determining a wind direction angle according to the reference wind power plant and the wind power plant to be predicted, obtaining a first wind speed and a first total active power at the reference wind power plant, and obtaining a second wind speed at the wind power plant to be predicted, wherein the calculation parameters can be obtained by the prior art relatively simply and conveniently, a wind energy loss coefficient is obtained according to the first wind speed and the second wind speed, and losses of factors such as terrain and friction to wind energy are fully considered; and determining window time according to the wind field distance, the wind direction angle and the first wind speed, namely embodying a predictable time point, calculating second full field total active power of the wind power plant to be predicted after the window time according to the first full field total active power and the wind energy loss coefficient, and completing calculation of the second full field total active power after the window time so as to realize accurate prediction of the ultra-short-term power of the wind power plant.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a terminal (also called a wind farm power prediction device, where the wind farm power prediction device may be formed by a single wind farm power prediction device, or may be formed by combining other devices with the wind farm power prediction device) of a hardware operating environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a fixed terminal or a mobile terminal, such as an intelligent air conditioner with a networking function, an intelligent lamp, an intelligent power supply, an intelligent sound box, an automatic driving automobile, a Personal Computer (PC), a smart phone, a tablet computer, an electronic book reader, a portable computer and the like.
As shown in fig. 1, the terminal may include: a processor 1001, e.g., a Central Processing Unit (CPU), a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., WIFI interface, WIreless FIdelity, WIFI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the terminal may further include a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, and a WiFi module; the input unit is compared with a display screen and a touch screen; the network interface may optionally be other than WiFi, bluetooth, probe, etc. in the wireless interface. Such as light sensors, motion sensors, and other sensors, among others. In particular, the light sensor may include an ambient light sensor and a proximity sensor; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which are not described herein again.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, the computer software product is stored in a storage medium (storage medium: also called computer storage medium, computer medium, readable storage medium, computer readable storage medium, or direct storage medium, etc., and the storage medium may be a non-volatile readable storage medium, such as RAM, magnetic disk, optical disk, etc.), and includes several instructions to enable a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the method according to the embodiments of the present invention, and a memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a computer program.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and processor 1001 may be configured to invoke a computer program stored in memory 1005 and perform the steps in the wind farm power prediction method provided by the following embodiments of the present invention.
Referring to fig. 2, in a first embodiment of a wind farm power prediction method according to the present invention, the wind farm power prediction method includes:
and S10, determining a reference wind power plant, and determining a predictable area according to the wind direction at the position of the reference wind power plant.
The method for predicting the power of the wind farm includes the steps that a wind farm power prediction device determines a reference wind farm, and a predictable area is determined according to the wind direction at the position of the reference wind farm, the wind farm power prediction device may include fixed devices such as a desktop computer, a Digital TV and the like, and mobile terminals such as a mobile phone, a tablet computer, a notebook computer, a palm computer, a Personal Digital Assistant (PDA) and the like with certain data processing and computing capabilities, the reference wind farm is a wind farm used for collecting partial data to guide the wind farm to be predicted to perform power prediction, and the predictable area is an area containing at least one wind farm to be predicted, which can be determined according to the wind direction at the position of the reference wind farm, and specifically, the step S10 may include:
step a1, selecting a wind power plant in a preset area as a reference wind power plant.
And a2, according to the current wind direction of the reference wind farm, taking a region positioned at the downwind direction of the reference wind farm in a preset region as a predictable region.
The method comprises the following steps that a wind power plant is selected from a preset area by wind power plant power prediction equipment to serve as a reference wind power plant, the preset area is an area containing at least two wind power plants, according to the current wind direction of the reference wind power plant, the area, located at the downwind direction of the reference wind power plant, in the preset area serves as a predictable area, and the downwind direction is specifically defined as: the other areas where the current wind surface arrives at the reference wind farm first and then arrives are the downwind direction, the other areas can be predicted areas, and particularly, according to the description of the wind surface, the connecting lines of the rest wind farms and the reference wind farm are not necessarily coincident with the wind direction.
And S20, selecting a wind power plant to be predicted in the predictable area, acquiring the wind field distance between the reference wind power plant and the wind power plant to be predicted, and determining the wind direction angle according to the reference wind power plant and the wind power plant to be predicted.
Selecting a wind farm to be predicted in the predictable area by the wind farm power prediction device, obtaining a wind farm distance between the reference wind farm and the wind farm to be predicted, and determining a wind direction angle according to the reference wind farm and the wind farm to be predicted, wherein as described in the step, the predictable area includes at least one wind farm, selecting one wind farm as the wind farm to be predicted, obtaining a wind farm distance between the reference wind farm and the wind farm to be predicted, the wind farm distance is a straight line distance between the reference wind farm and the wind farm to be predicted, considering that the wind farm has a large area, an error may occur in the wind farm distance determination, generally selecting a distance between a geometric center of the reference wind farm and a geometric center of the wind farm to be predicted as the wind farm distance, optionally, determining one point after adjusting the geometric center according to conditions such as a wind farm topography, a wind farm fan distribution density and the like, determining the manner of the distance between the two points in geography, which is the prior art, and no redundancy is given here, the wind direction angle is determined by the method of obtaining the wind farm at present, and the step S20 may be repeated:
and b1, determining a straight line according to the reference wind power plant and the wind power plant to be predicted.
And b2, determining an included angle between the straight line and the wind direction, and taking the included angle as a wind direction angle.
And the wind power prediction equipment determines a straight line according to the reference wind power plant and the wind power plant to be predicted, the straight line passes through the geometric centers of the two wind power plants explained in the previous step, and then the included angle between the straight line and the wind direction is used as a wind direction angle, and the wind direction refers to the current wind direction of the reference wind power plant.
And S30, acquiring a first wind speed and a first total active power at the reference wind power plant, acquiring a second wind speed at the wind power plant to be predicted, and acquiring a wind energy loss coefficient according to the first wind speed and the second wind speed.
The method comprises the steps that wind power prediction equipment obtains a first wind speed and a first total active power at a reference wind power plant, obtains a second wind speed at a to-be-predicted wind power plant, and obtains a wind energy loss coefficient according to the first wind speed and the second wind speed, wherein the wind energy loss coefficient is the energy loss generated when wind energy is influenced by factors such as terrain and friction in the advancing process, the wind energy loss coefficient is the energy loss representing the wind from the reference wind power plant to the to-be-predicted wind power plant, the first wind speed and the second wind speed are respectively the current wind speed of the reference wind power plant and the current wind speed of the to-be-predicted wind power plant, a specific calculation method of the wind energy loss coefficient is described in the following embodiments, and is not described herein.
And S40, determining window time according to the wind field distance, the wind direction angle and the first wind speed, and calculating second full field total active power of the wind power plant to be predicted after the window time passes according to the first full field total active power and the wind energy loss coefficient.
The wind farm power prediction device determines a window time according to the wind farm distance, the wind direction angle and the first wind speed, and after the window time is calculated according to the first full-farm total active power and the wind energy loss coefficient, the second full-farm total active power of the wind farm to be predicted is calculated, the window time refers to the time when the wind blowing through the reference wind farm currently reaches the wind farm to be predicted, that is, the time capable of being predicted in this embodiment, and if the window time is 30 minutes, the second total active power of the wind farm to be predicted after 30 minutes can be predicted, specifically, the step S40 of determining the window time according to the wind farm distance, the wind direction angle and the first wind speed may include:
step c1, inputting the wind field distance and the wind direction angle into a first preset formula to calculate the wind direction distance, wherein the first preset formula is as follows: sk = Sab cos α; the Sk is the wind direction distance, the Sab is the wind field distance, and the alpha is the wind direction angle.
Step c2, inputting the wind direction distance and the first wind speed into a second preset formula, and determining the window time, wherein the second preset formula is as follows: x = Sk/Wa; the x is window time, the Sk is wind direction distance, and the Wa is first wind speed.
The wind power plant power prediction equipment inputs the wind field distance and the wind direction angle into a first preset formula to calculate the wind direction distance, wherein the first preset formula is as follows: sk = Sab cos α; the Sk is a wind direction distance, the Sab is a wind field distance, the α is a wind direction angle, and particularly, the wind direction distance is different from the wind field distance, the wind direction distance refers to a projection distance of the wind field distance in a wind direction, that is, an actual moving distance of a wind surface from a reference wind power plant to a wind power plant to be predicted, the wind power prediction device inputs the cylinder separation distance and the first wind speed into a second preset formula to calculate window time, inputs the wind direction distance and the first wind speed into the second preset formula to determine the window time, wherein the second preset formula is as follows: x = Sk/Wa; the window time can be determined by using the wind field distance, the wind direction angle and the first wind speed parameter obtained through the steps, and then the second full-field total active power of the wind power plant to be predicted after the window time is further determined, and a calculation mode of the second full-field total active power will be described in the following embodiments.
In the embodiment, the wind farm power prediction device determines a predictable area according to the wind direction at the position of a reference wind farm by determining the reference wind farm; selecting a wind power plant to be predicted in the region capable of being predicted, obtaining a wind field distance between the reference wind power plant and the wind power plant to be predicted, determining a wind direction angle according to the reference wind power plant and the wind power plant to be predicted, obtaining a first wind speed and a first total active power at the reference wind power plant, and obtaining a second wind speed at the wind power plant to be predicted, wherein the calculation parameters can be obtained by the prior art relatively simply and conveniently, a wind energy loss coefficient is obtained according to the first wind speed and the second wind speed, and losses of factors such as terrain and friction to wind energy are fully considered; determining window time according to the wind field distance, the wind direction angle and the first wind speed, namely embodying a predictable time point, calculating second full field total active power of the wind power plant to be predicted after the window time according to the first full field total active power and the wind energy loss coefficient, and completing calculation of the second full field total active power after the window time so as to realize accurate prediction of the ultra-short-term power of the wind power plant.
Further, on the basis of the first embodiment of the present invention, a second embodiment of the wind farm power prediction method according to the present invention is further provided, and this embodiment describes in detail the step of calculating the wind energy loss coefficient in step S30 in the first embodiment, and includes:
step d1, inputting the first wind speed and the second wind speed into a third preset formula to obtain a wind energy loss coefficient, wherein the third preset formula is as follows: kx = Wb 3 /Wa 3 (ii) a And the Kx wind energy loss coefficient, wb is a second wind speed, and Wa is a first wind speed.
The wind power plant power prediction equipment inputs the first wind speed and the second wind speed into a third preset formula to obtain a wind energy loss coefficient, wherein the third preset formula is as follows: kx = Wb 3 /Wa 3 (ii) a And the Kx wind energy loss coefficient, wb is a second wind speed, and Wa is a first wind speed.
In the embodiment, energy loss caused by influence of terrain, friction and the like on wind energy is comprehensively considered by providing a calculation mode of a wind energy loss coefficient, so that the second full-field total active power of a follow-up wind power plant to be predicted is more accurately predicted.
Further, on the basis of the first embodiment of the present invention, a third embodiment of the method for predicting power of a wind farm according to the present invention is further provided, where this embodiment specifies in detail the step of calculating, according to the first total active power of the full farm and the wind energy loss coefficient, the second total active power of the wind farm to be predicted after the window time elapses in step S40 in the first embodiment, and includes:
step e1, inputting the first full-field total active power and the wind energy loss coefficient into a fourth preset formula, and obtaining a second full-field total active power of the wind power plant to be predicted after window time, wherein the fourth preset formula is as follows: pbx = Kx × K × Pa; and Pbx is the second full-field total active power, kx is the wind energy loss coefficient, K is a constant, and Pa is the first full-field total active power.
The wind power plant power prediction device inputs the first full active power and the wind energy loss coefficient into a fourth preset formula, and obtains a second full active power of the wind power plant to be predicted after window time, wherein the fourth preset formula is as follows: pbx = Kx × K × Pa; and Pbx is the second full-field total active power, kx is the wind energy loss coefficient, K is a constant and is usually 1.23, and Pa is the first full-field total active power.
In this embodiment, by providing a second full-field total active power calculation method, with reference to the first full-field total active power of the wind farm as a reference, factors such as wind energy loss are comprehensively considered, and thus, accurate prediction of the ultra-short-term power of the wind farm is achieved.
Further, on the basis of the above embodiment of the present invention, a fourth embodiment of the wind farm power prediction method of the present invention is further provided, where this embodiment is a post-step of step S40 in the first embodiment, and includes the following steps:
and f1, taking each wind power plant in the predictable area as a wind power plant to be predicted, and acquiring the window time of each wind power plant to be predicted in the predictable area and the second full-field total active power of the wind power plant to be predicted after the window time.
And f2, generating a wind power dispatching instruction according to the second full active power, and sending the wind power dispatching instruction to a dispatching terminal so that the dispatching terminal carries out wind power dispatching according to the wind power dispatching instruction.
The method comprises the steps that wind power plant power prediction equipment takes each wind power plant in a predictable area as a wind power plant to be predicted, window time of each wind power plant to be predicted in the predictable area and second full-field total active power of the wind power plant to be predicted after the window time are obtained, the wind power plant power prediction equipment generates a wind power scheduling instruction according to the second full-field total active power and sends the wind power scheduling instruction to a scheduling terminal, so that the scheduling terminal performs wind power scheduling according to the wind power scheduling instruction.
In the embodiment, the wind power plant power prediction is performed on all wind power plants to be predicted in a predictable area by using the method of the embodiment, the wind power scheduling instruction is generated according to the prediction result and is sent to the scheduling terminal to guide the scheduling terminal to perform wind power scheduling, so that the conversion from the prediction result to the ground application is realized, and more scientific scheduling of wind power is also realized.
In addition, an embodiment of the present invention further provides a wind farm power prediction device, where the wind farm power prediction device includes:
a region determination module: the method comprises the steps of determining a reference wind power plant, and determining a predictable area according to the wind direction at the position of the reference wind power plant;
a first data acquisition module: the wind power field prediction method comprises the steps of selecting a wind power field to be predicted in the predictable area, obtaining the wind field distance between the reference wind power field and the wind power field to be predicted, and determining a wind direction angle according to the reference wind power field and the wind power field to be predicted;
a second data acquisition module: the wind power generation system is used for acquiring a first wind speed and a first total active power at the reference wind power plant, acquiring a second wind speed at the wind power plant to be predicted, and acquiring a wind energy loss coefficient according to the first wind speed and the second wind speed;
a prediction module: and the wind power generation system is used for determining window time according to the wind field distance, the wind direction angle and the first wind speed, and calculating second full field total active power of the wind power plant to be predicted after the window time passes according to the first full field total active power and the wind energy loss coefficient.
The steps implemented by each functional module of the wind farm power prediction device can refer to each embodiment of the wind farm power prediction method, and are not described herein again.
In addition, the embodiment of the invention also provides wind power plant power prediction equipment.
The wind farm power prediction device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein:
the computer program, when executed by the processor, implements the operations of the wind farm power prediction method provided by the above embodiments.
In addition, the embodiment of the invention also provides a computer storage medium.
The computer storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the operations in the wind farm power prediction method provided by the above embodiments.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity/action/object from another entity/action/object without necessarily requiring or implying any actual such relationship or order between such entities/actions/objects; the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising one of ...does not exclude the presence of additional like elements in a process, method, article, or system that comprises the element.
For the apparatus embodiment, since it is substantially similar to the method embodiment, it is described relatively simply, and reference may be made to some descriptions of the method embodiment for relevant points. The above-described apparatus embodiments are merely illustrative, in that elements described as separate components may or may not be physically separate. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. 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 (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A wind farm power prediction method is characterized by comprising the following steps:
determining a reference wind power plant, and determining a predictable area according to the wind direction at the position of the reference wind power plant;
selecting a wind power plant to be predicted in the region capable of being predicted, obtaining a wind field distance between the reference wind power plant and the wind power plant to be predicted, and determining a wind direction angle according to the reference wind power plant and the wind power plant to be predicted;
acquiring a first wind speed and a first total active power of the whole wind field at the reference wind field, acquiring a second wind speed at the wind field to be predicted, and acquiring a wind energy loss coefficient according to the first wind speed and the second wind speed;
and determining window time according to the wind field distance, the wind direction angle and the first wind speed, and calculating second full field total active power of the wind power plant to be predicted after the window time passes according to the first full field total active power and the wind energy loss coefficient.
2. The method for wind farm power prediction according to claim 1, wherein the step of determining a reference wind farm, the predictable area being determined from the wind direction at the location of the reference wind farm, comprises:
selecting a wind power plant in a preset area as a reference wind power plant;
and according to the current wind direction of the reference wind farm, taking a region positioned at the downwind direction of the reference wind farm in a preset region as a predictable region.
3. The method for wind farm power prediction according to claim 1, wherein the step of determining a wind direction angle from the reference wind farm and the wind farm to be predicted comprises:
determining a straight line according to the reference wind power plant and the wind power plant to be predicted;
and determining an included angle between the straight line and the wind direction, and taking the included angle as a wind direction angle.
4. The wind farm power prediction method of claim 1, wherein the step of determining a window time based on the wind farm distance, the wind direction angle, and the first wind speed comprises:
inputting the wind field distance and the wind direction angle into a first preset formula to calculate the wind direction distance, wherein the first preset formula is as follows: sk = Sab cos α; the Sk is a wind direction distance, the Sab is a wind field distance, and the alpha is a wind direction angle;
inputting the wind direction distance and the first wind speed into a second preset formula, and determining the window time, wherein the second preset formula is as follows: x = Sk/Wa; the x is window time, the Sk is wind direction distance, and the Wa is first wind speed.
5. The wind farm power prediction method of claim 1, wherein the step of deriving a loss of wind energy coefficient from the first wind speed and the second wind speed comprises:
inputting the first wind speed and the second wind speed into a third preset formula to obtain a wind energy loss coefficient, wherein the third preset formula is as follows: kx = Wb 3 /Wa 3 (ii) a And the Kx wind energy loss coefficient, wb is a second wind speed, and Wa is a first wind speed.
6. The method for wind farm power prediction according to claim 1, wherein the step of calculating a second total active power of the wind farm to be predicted after the window time elapses according to the first total active power of the wind farm and the wind energy loss coefficient comprises:
inputting the first full-field total active power and the wind energy loss coefficient into a fourth preset formula to obtain a second full-field total active power of the wind power plant to be predicted after the window time, wherein the fourth preset formula is as follows: pbx = Kx × K × Pa; and Pbx is second full-field total active power, kx is a wind energy loss coefficient, K is a constant, and Pa is first full-field total active power.
7. A wind farm power prediction method according to any of the claims 1-6, wherein the step of calculating a second total active power of the wind farm to be predicted after the window time has elapsed from the first total active power of the wind farm and the wind energy loss factor is followed by:
taking each wind power plant in the predictable area as a wind power plant to be predicted, and acquiring the window time of each wind power plant to be predicted in the predictable area and the second full-field total active power of the wind power plant to be predicted after the window time;
and generating a wind power dispatching instruction according to the second full active power, and sending the wind power dispatching instruction to a dispatching terminal so that the dispatching terminal carries out wind power dispatching according to the wind power dispatching instruction.
8. A wind farm power prediction device, characterized in that the wind farm power prediction device comprises:
a region determination module: the method comprises the steps of determining a reference wind power plant, and determining a predictable area according to the wind direction at the position of the reference wind power plant;
a first data acquisition module: the wind power field prediction method comprises the steps of selecting a wind power field to be predicted in the predictable area, obtaining the wind field distance between the reference wind power field and the wind power field to be predicted, and determining a wind direction angle according to the reference wind power field and the wind power field to be predicted;
the second data acquisition module: the wind power generation system is used for acquiring a first wind speed and a first total active power at the reference wind power plant, acquiring a second wind speed at the wind power plant to be predicted, and acquiring a wind energy loss coefficient according to the first wind speed and the second wind speed;
a prediction module: and the wind power generation system is used for determining window time according to the wind field distance, the wind direction angle and the first wind speed, and calculating second full field total active power of the wind power plant to be predicted after the window time passes according to the first full field total active power and the wind energy loss coefficient.
9. A wind farm power prediction device, characterized in that the wind farm power prediction device comprises: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein:
the computer program, when executed by the processor, implementing the steps of the wind farm power prediction method according to any of claims 1 to 7.
10. A computer storage medium, characterized in that it has stored thereon a computer program which, when being executed by a processor, carries out the steps of the wind farm power prediction method according to any one of claims 1 to 7.
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Effective date of registration: 20231114 Address after: 6th Floor, East Building, Provincial Market Supervision and Administration Bureau, No. 108 Changfeng Street, Pingyang Road Street, Xiaodian District, Taiyuan City, Shanxi Province, 030000 Patentee after: China Resources New Energy Investment Co.,Ltd. Shanxi Branch Address before: 026000 Huarun Building, West Section of Xilin Avenue, Xilingol League, Inner Mongolia Autonomous Region Patentee before: CR POWER INVESTMENT CO.,LTD. NORTH BRANCH |