CN116050201B - Three-dimensional wind field modeling method and device based on intelligent sensor real-time acquisition - Google Patents
Three-dimensional wind field modeling method and device based on intelligent sensor real-time acquisition Download PDFInfo
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Abstract
The application relates to a three-dimensional wind field modeling method and device based on intelligent sensor real-time acquisition. The specific scheme is as follows: acquiring an integral wind field airflow diagram, and determining wind speed data of a target area based on the integral wind field airflow diagram; inputting wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected; carrying out complex fluid-solid coupling analysis on the fan blade based on the fan information and the wind speed data; determining mechanical torque and electromagnetic torque of the fan based on the analysis result, the wind speed data and the fan information; respectively comparing the mechanical torque and the electromagnetic torque of each fan, and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of the fan based on the comparison result; determining a fan wake model based on respective pitch angle adjustment instructions and yaw adjustment instructions of the plurality of fans; and superposing the wake flow model and the three-dimensional wind field flow model to be corrected to obtain the corrected three-dimensional wind field flow model. The method improves the accuracy of establishing the three-dimensional wind field flow model.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a three-dimensional wind field modeling method and device based on real-time acquisition of an intelligent sensor.
Background
In the related art, after the large wind farm is built and operated, the original local near-stratum atmosphere movement characteristics are definitely changed, and the change of the near-stratum atmosphere movement further generates influences on momentum, heat and water vapor transmission in the whole atmosphere boundary layer. The large-scale development of offshore wind power has certain influence on local wind speed, temperature, precipitation, turbulence intensity and the like. In many current prediction methods, only a single variable of wind speed or wind power is used as input data, but wind energy is also influenced by surrounding environment factors, physical factors, fluid-solid coupling of a fan body and the like, and difficulty is caused to accurate modeling of comprehensive complex wind resources.
Disclosure of Invention
Therefore, the application provides a three-dimensional wind field modeling method and device based on real-time acquisition of intelligent sensors. The technical scheme of the application is as follows:
According to a first aspect of an embodiment of the present application, there is provided a three-dimensional wind field modeling method based on real-time acquisition of an intelligent sensor, wherein the method includes:
Acquiring an integral wind field airflow diagram, and determining wind speed data of a target area based on the integral wind field airflow diagram;
inputting the wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected;
Acquiring fan information, and carrying out complex fluid-solid coupling analysis on fan blades based on the fan information and the wind speed data to obtain an analysis result; the fan information comprises fan sub-information of a plurality of fans arranged in the target area;
determining respective mechanical torque and electromagnetic torque of the plurality of fans based on the analysis result, the wind speed data and the fan information;
Respectively comparing the mechanical torque and the electromagnetic torque of each fan, and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each fan based on the comparison result;
determining a fan wake model based on respective pitch angle adjustment instructions and yaw adjustment instructions of the plurality of fans;
And superposing the wake flow model and the three-dimensional wind field flow model to be corrected to obtain a corrected three-dimensional wind field flow model.
According to an embodiment of the present application, the comparing the mechanical torque and the electromagnetic torque of each of the plurality of fans, and outputting the pitch angle adjustment command and the yaw adjustment command of each of the plurality of fans based on the comparison result, includes:
comparing the mechanical torque and the electromagnetic torque of each fan to obtain a comparison result;
Responding to the comparison result that the mechanical torque is smaller than the electromagnetic torque, and outputting a yaw adjustment instruction so as to increase the windward area of the fan blade;
And outputting a pitch angle adjustment instruction to stop the fan in response to the comparison result that the mechanical torque is larger than the electromagnetic torque.
According to one embodiment of the present application, the determining a fan wake model based on the pitch angle adjustment instructions and the yaw adjustment instructions of each of the plurality of fans includes:
For each fan, determining an included angle between a wind speed vector and a wind wheel rotation tangential direction of the fan based on the wind speed data, the pitch angle adjustment instruction and the yaw adjustment instruction corresponding to the fan, and determining fan wake information of the fan based on the included angle of the wind wheel rotation tangential direction;
And generating a fan wake model based on fan wake information of each fan.
According to one embodiment of the present application, the acquiring an overall wind farm airflow map, the determining wind speed data of a target area based on the overall wind farm airflow map includes:
Acquiring an integral wind field airflow diagram;
performing segmentation decoupling processing on the integral wind field airflow diagram to obtain intermediate data;
determining an area meeting a first preset condition in the integral wind field as the target area based on the intermediate data; wind speed data of the target area with a time sequence is determined based on the target area.
According to a second aspect of an embodiment of the present application, there is provided a three-dimensional wind field modeling apparatus based on real-time acquisition of intelligent sensors, the apparatus comprising:
the acquisition module is used for acquiring an integral wind field airflow diagram and determining wind speed data of a target area based on the integral wind field airflow diagram;
The input module is used for inputting the wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected;
The analysis module is used for acquiring fan information, and carrying out complex fluid-solid coupling analysis on the fan blades based on the fan information and the wind speed data to obtain an analysis result; the fan information comprises fan sub-information of a plurality of fans arranged in the target area;
A first determining module for determining respective mechanical torque and electromagnetic torque of the plurality of fans based on the analysis result, the wind speed data, and the fan information;
The output module is used for respectively comparing the mechanical torque and the electromagnetic torque of each of the plurality of fans and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each of the plurality of fans based on the comparison result;
the second determining module is used for determining a fan wake model based on the respective pitch angle adjustment instruction and yaw adjustment instruction of the fans;
and the correction module is used for carrying out superposition processing on the wake flow model and the three-dimensional wind field flow model to be corrected so as to obtain a corrected three-dimensional wind field flow model.
According to one embodiment of the application, the output module comprises:
The comparison sub-module is used for comparing the mechanical torque and the electromagnetic torque of each fan to obtain a comparison result;
The first output submodule is used for outputting a yaw degree adjustment instruction to increase the windward area of the fan blade in response to the comparison result that the mechanical torque is smaller than the electromagnetic torque;
And the second output sub-module is used for outputting a pitch angle adjustment instruction to stop the fan in response to the comparison result that the mechanical torque is larger than the electromagnetic torque.
According to one embodiment of the application, the second determining module comprises:
The first determining submodule is used for determining an included angle between a wind speed vector and a wind wheel rotation tangential direction of each fan based on the wind speed data, the pitch angle adjusting instruction and the yaw angle adjusting instruction corresponding to the fan, and determining fan wake information of the fan based on the included angle between the wind wheel rotation tangential direction;
And the generation sub-module is used for generating the fan wake model based on the fan wake information of the fans of each fan.
According to one embodiment of the application, the acquisition module comprises:
the acquisition sub-module is used for acquiring an integral wind field airflow diagram;
The processing sub-module is used for carrying out segmentation decoupling processing on the integral wind field airflow diagram to obtain intermediate data;
The second determining submodule is used for determining an area meeting a first preset condition in the whole wind field as the target area based on the intermediate data; wind speed data of the target area with a time sequence is determined based on the target area.
According to a third aspect of an embodiment of the present application, there is provided an electronic apparatus including: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
The processor executes computer-executable instructions stored by the memory to implement the method of any one of the first aspects.
According to a fourth aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, are adapted to carry out the method according to any one of the first aspects
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
Determining wind speed data of a target area based on the integral wind field airflow diagram by acquiring the integral wind field airflow diagram; inputting wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected; acquiring fan information, and carrying out complex fluid-solid coupling analysis on fan blades based on the fan information and the wind speed data to obtain an analysis result; determining respective mechanical torque and electromagnetic torque of the plurality of fans based on the analysis result, the wind speed data and the fan information; respectively comparing the mechanical torque and the electromagnetic torque of each of the plurality of fans, and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each of the plurality of fans based on a comparison result; determining a fan wake model based on respective pitch angle adjustment instructions and yaw adjustment instructions of the plurality of fans; and superposing the wake flow model and the three-dimensional wind field flow model to be corrected to obtain the corrected three-dimensional wind field flow model.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application and do not constitute a undue limitation on the application.
FIG. 1 is a flow chart of a three-dimensional wind field modeling method based on intelligent sensor real-time acquisition in an embodiment of the application;
FIG. 2 is a block diagram of a three-dimensional wind field modeling device based on real-time acquisition of intelligent sensors in an embodiment of the application;
fig. 3 is a block diagram of an electronic device in an embodiment of the application.
Detailed Description
In order to enable a person skilled in the art to better understand the technical solutions of the present application, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
It should be noted that wind energy resource assessment is a process of analyzing long-term wind energy resource meteorological parameters of an area to be assessed. The wind power density and the equivalent parameters of the effective number of years and hours are estimated through analysis and treatment of the local wind speed, wind direction, air temperature, air pressure, air density and other observation parameters. The wind energy resource reserves of the areas can be determined through wind energy resource evaluation, and reference basis is provided for wind power plant site selection, wind power generator set type selection, determination of a set arrangement scheme and electric quantity calculation. Compared with onshore wind power, the offshore hydro-meteorological observation data are missing, the offshore observation cost is high, the difficulty is high, and the like, so that the technical difficulty of offshore wind energy resource assessment is further improved. The offshore severe development environment greatly increases the cost and risk of wind power project development, and provides more severe technical requirements for the development of offshore wind power. The scientific evaluation and accurate prediction of wind energy resources and marine environment are necessary routes for guaranteeing the high-quality development of offshore wind power business.
The sampling frequency and the communication transmission speed of the existing intelligent sensor such as a laser radar can be matched with a high-frequency real-time simulator, but the existing laser radar is only used for providing actual measurement parameters for a control system, and data cannot be directly connected into a laboratory simulation system, so that a simulation result obtained by adopting an average ideal wind speed is inconsistent with a complex and variable actual environment, the actual control system can only adopt the simplest but least economical shutdown processing under the complex wind condition, and the actual complex and variable wind resource condition cannot be accurately fitted under the simulation environment. Wind resource data collected by a meteorological station and a sensor contain a plurality of turbulence influences, and wind output conditions of fans among all areas are different due to different wind conditions of all areas of an offshore wind field, so that the existing wind field modeling cannot accurately model a multi-partition multi-scale wind field model in detail. After the large wind farm is built and operated, the original local near-stratum atmosphere movement characteristics are definitely changed, and the change of the near-stratum atmosphere movement further generates influences on momentum, heat and water vapor transportation in the whole atmosphere boundary layer. The large-scale development of offshore wind power has certain influence on local wind speed, temperature, precipitation, turbulence intensity and the like. In many current prediction methods, only a single variable of wind speed or wind power is used as input data, but wind energy is also influenced by surrounding environment factors, physical factors, fluid-solid coupling of a fan body and the like, and difficulty is caused to accurate modeling of comprehensive complex wind resources.
Based on the problems, the application provides a three-dimensional wind field modeling method and device based on real-time acquisition of intelligent sensors, which can determine wind speed data of a target area based on an integral wind field airflow diagram by acquiring the integral wind field airflow diagram; inputting wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected; acquiring fan information, and carrying out complex fluid-solid coupling analysis on fan blades based on the fan information and the wind speed data to obtain an analysis result; determining respective mechanical torque and electromagnetic torque of the plurality of fans based on the analysis result, the wind speed data and the fan information; respectively comparing the mechanical torque and the electromagnetic torque of each of the plurality of fans, and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each of the plurality of fans based on a comparison result; determining a fan wake model based on respective pitch angle adjustment instructions and yaw adjustment instructions of the plurality of fans; and superposing the wake flow model and the three-dimensional wind field flow model to be corrected to obtain the corrected three-dimensional wind field flow model.
Fig. 1 is a flowchart of a three-dimensional wind field modeling method based on intelligent sensor real-time acquisition in an embodiment of the application.
As shown in fig. 1, the three-dimensional wind field modeling method based on real-time acquisition of intelligent sensors includes:
step 101, acquiring an integral wind field airflow diagram, and determining wind speed data of a target area based on the integral wind field airflow diagram.
In some embodiments of the present application, step 101 includes:
and a step a1, obtaining an integral wind field airflow diagram.
Optionally, the integral wind field airflow graph may be obtained by collecting wind field airflow data through an intelligent sensor in the integral wind field.
And a2, performing segmentation decoupling treatment on the integral wind field airflow diagram to obtain intermediate data.
Step a3, determining an area meeting a first preset condition in the integral wind field as a target area based on the intermediate data; wind speed data of the target area with a time sequence is determined based on the target area.
It is understood that the integral wind field airflow diagram includes time series wind speed data of the integral wind field.
As an example of one possible implementation manner, the split decoupling processing is performed on the whole wind field airflow diagram to obtain intermediate data, and in the intermediate data, a region meeting the first preset condition is acquired and determined as a target region, and wind speed data with time sequence of the target region is determined based on the target region.
Optionally, the first preset condition may be that the target area is an area containing a wind speed superposition effect within a certain wind field range.
And 102, inputting wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected.
As an example of one possible implementation, the above-mentioned preset model may be obtained by: and dividing different wind speed areas in the offshore wind farm according to the average wind speed and the average wind power density of each area of the sea surface in the representative year data. The fixed wind field scale is set in any area and comprises area, fan number, predicted output power and fan arrangement points. Dividing the area into grids with certain density and quantity by throwing the area based on the air volume resolution requirement according to a finite element modeling method, and designating the inlet of the wind and the positive wind direction of the area.
And step 103, obtaining fan information, and carrying out complex fluid-solid coupling analysis on the fan blades based on the fan information and the wind speed data to obtain an analysis result.
In the embodiment of the application, the fan information comprises fan sub-information of a plurality of fans arranged in the target area.
As an example of one possible implementation, based on the fan information, each level of the fan position and the fan body is acquired, and the wind speed vector with time sequence corresponding to each level of the fan position and the fan body is decomposed in the wind speed data. And carrying out complex fluid-solid coupling analysis and calculation on the fan blade based on the wind speed vector with the time sequence.
Step 104, determining the mechanical torque and the electromagnetic torque of each of the plurality of fans based on the analysis result, the wind speed data and the fan information.
In the embodiment of the present application, the mechanical torque T opt may be calculated by the following formula:
Wherein omega g is the rotation angular velocity of the wind driven generator, R is the impeller radius, n is the gear box transmission ratio, K opt is the optimal control coefficient of torque, and C Pmax is the maximum wind energy utilization coefficient.
In the embodiment of the application, the electromagnetic torque is generated by cutting magnetic force lines of the wind driven generator body at a certain rotating speed under the drive of the impeller and the transmission shaft.
And 105, respectively comparing the mechanical torque and the electromagnetic torque of each of the plurality of fans, and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each of the plurality of fans based on the comparison result.
In some embodiments of the present application, step 105 includes:
and b1, comparing the mechanical torque and the electromagnetic torque of each fan to obtain a comparison result.
And b2, outputting a yaw adjustment command to increase the windward area of the fan blade in response to the fact that the mechanical torque is smaller than the electromagnetic torque as a result of the comparison.
As an example of a possible implementation manner, if the comparison result is that the mechanical torque is larger than the electromagnetic torque, the wind energy is too small to drive the fan to rotate or the wind energy can be driven but the generated energy is too small, on one hand, the generator actively excites and adjusts the operation working point of the motor to keep the optimal tip speed ratio so as to obtain the maximum wind energy, on the other hand, the fan master control sends a yaw command, the yaw operation is carried out according to the wind direction collected by the anemoscope, and the wind receiving area of the blade is increased.
And b3, outputting a pitch angle adjustment instruction to stop the operation of the fan in response to the comparison result that the mechanical torque is larger than the electromagnetic torque.
As an example of one possible implementation, in response to the comparison result being that the mechanical torque is greater than the electromagnetic torque, the wind energy is too great, which causes the fan to speed up or exceeds the rated rotational speed, which causes the power generation frequency to increase, when the generator reaches the power limit or the load limit, a pitch command is sent, the pitch angle is adjusted to 90 ° feathering, the blades no longer capture the wind energy, the rotational speed drops to 0, the fan is prevented from being damaged by strong wind, and the wind turbine is locked into a shutdown mode.
And 106, determining a fan wake model based on the pitch angle adjustment instruction and the yaw adjustment instruction of each fan.
In some embodiments of the present application, step 106 includes:
step c1, determining an included angle between a wind speed vector and a wind wheel rotation tangential direction of the fan based on wind speed data, a pitch angle adjustment instruction and a yaw adjustment instruction corresponding to the fan, and determining fan wake information of the fan based on the included angle between the wind wheel rotation tangential direction.
As an example of one possible implementation, for each fan, based on wind speed data, a pitch angle adjustment instruction and a yaw adjustment instruction corresponding to the fan, an included angle θ between a wind speed vector and a tangential direction of rotation of a wind wheel of the fan is determined, and a vortex flow velocity v θ is calculated by the following formula:
Wherein Γ is the vortex ring quantity, alpha is the included angle between the velocity vector and the tangential direction of the point, J is the vortex flux of the fluid, r is the radius of the blade, l is the distance from the blade root, v is the wind speed from the blade root, and according to Stokes' theorem, in the vortex field, the velocity ring quantity along any closed circumferential line is equal to the vortex intensity of the curved surface area surrounded by the circumferential line. The vortex flow velocity is fan wake information, and a fan wake model can be generated based on the fan wake information.
And c2, generating a fan wake model based on fan wake information of the fans of each fan.
As one possible implementation example, a nonlinear wake model of a control volume method is used for generating a respective microscopic-scale fan wake model of each fan based on fan wake information of the fan of each fan. Analyzing a single machine wake field, and assuming u0 and u are wind speeds of a position, which is in front of a fan and is away from the fan x, behind the fan respectively, and D0 is the diameter of a wind wheel; da. D is the wake diameter behind the fan and at the mechanism fan x respectively. According to Euler's conveying formula and kinetic energy theorem:
Wherein C T is thrust coefficient, A 0 and A are downstream wake flow area of the fan,
The diameter of the tail flow of a single fan is influenced by selecting a nonlinear expansion model:
D(x)＝(b+tx)1/nD0
Wherein the nonlinear equation set operator b= [ (2-a)/(2-2 a) ] 3/2;t＝{[CT/(2c-2c2)]3/2-b}/(mD0).
To sum up, a nonlinear expansion model is selected for the influence diameter of the fan wake:
For any selected fan, the relation between the thrust coefficient C T and the working wind speed u can be set as C T =f (u), and the function is a mathematical expression of a nonlinear expansion wake model of a single fan.
As one possible implementation example, the respective fan wake models for each fan may be superimposed according to the location of the fan to obtain an overall fan wake model.
For example, in response to the nth too much fan being at the edge position of the target area, the wake wind speed u n axially arranged at the nth fan is calculated by the following formula:
u13(u1-u13)A13+…+un-1,n(un-1-un-1,n)An-1,n＝un(un-u1)An That is, the wake wind speed of the nth fan is mainly influenced by the upstream nth-1 fan, and the upstream n-2 fans only influence the wake recovery speed of the wake boundary of the fan and do not directly influence the wind speed. Where u 13 is the wake wind speed of the upstream fan 1 at the fan 3, and a 13 is the wake impact area of the upstream fan 1 at the fan 3.
In response to the nth too much fan being at a non-edge position of the target area, the wake wind speed u n axially arranged at the nth fan is calculated by the following formula:
Namely, for the nth fan, the wake influence of the upstream n-1 fans is direct, and the influence on the wind speed plays the same role. In the middle of For average wind speed,/>Is the overlapping area of the n-2 th fan and the n-1 th fan
And step 107, superposing the wake flow model and the three-dimensional wind field flow model to be corrected to obtain a corrected three-dimensional wind field flow model. Wind resource modeling combined with finite element multi-body dynamics calculation is adopted, and the influence factors of complex fluid-solid coupling characteristics of fan aerodynamics and structural dynamics on the wind flow field are taken as correction factors to account for the three-dimensional modeling of the whole wind flow field under the macro scale.
According to the three-dimensional wind field modeling method based on intelligent sensor real-time acquisition, the wind speed data of a target area is determined based on the integral wind field airflow diagram by acquiring the integral wind field airflow diagram; inputting wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected; acquiring fan information, and carrying out complex fluid-solid coupling analysis on fan blades based on the fan information and the wind speed data to obtain an analysis result; determining respective mechanical torque and electromagnetic torque of the plurality of fans based on the analysis result, the wind speed data and the fan information; respectively comparing the mechanical torque and the electromagnetic torque of each of the plurality of fans, and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each of the plurality of fans based on a comparison result; determining a fan wake model based on respective pitch angle adjustment instructions and yaw adjustment instructions of the plurality of fans; and superposing the wake flow model and the three-dimensional wind field flow model to be corrected to obtain the corrected three-dimensional wind field flow model. The actual wind condition input by combining the actual measurement data of the target fan and the observation data of the weather station can reflect the actual wind conditions of the fan and the wind field, and particularly, the accurate modeling of complex free vortex and wake flow among units, which cannot be fitted by numerical calculation, is realized, so that the accuracy of building the three-dimensional wind field flow model is improved.
Fig. 2 is a structural block diagram of a three-dimensional wind field modeling device based on real-time acquisition of intelligent sensors in an embodiment of the application.
As shown in fig. 2, the three-dimensional wind field modeling device based on real-time acquisition of intelligent sensors includes:
an acquisition module 201, configured to acquire an overall wind field airflow diagram, and determine wind speed data of a target area based on the overall wind field airflow diagram;
The input module 202 is configured to input wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected;
the analysis module 203 is configured to obtain fan information, perform complex fluid-solid coupling analysis on the fan blade based on the fan information and the wind speed data, and obtain an analysis result; the fan information comprises fan sub-information of a plurality of fans arranged in the target area;
a first determining module 204, configured to determine mechanical torque and electromagnetic torque of each of the plurality of fans based on the analysis result, the wind speed data, and the fan information;
The output module 205 is configured to compare the mechanical torque and the electromagnetic torque of each of the plurality of fans, and output a pitch angle adjustment instruction and a yaw adjustment instruction of each of the plurality of fans based on the comparison result;
a second determining module 206, configured to determine a fan wake model based on the pitch angle adjustment instruction and the yaw adjustment instruction of each of the plurality of fans;
and the correction module 207 is configured to perform superposition processing on the wake flow model and the three-dimensional wind field flow model to be corrected, so as to obtain a corrected three-dimensional wind field flow model.
In some embodiments of the application, the output module comprises:
The comparison submodule is used for comparing the mechanical torque and the electromagnetic torque of each fan to obtain a comparison result;
the first output sub-module is used for outputting a yaw adjustment instruction to increase the windward area of the fan blade in response to the fact that the mechanical torque is smaller than the electromagnetic torque as a result of comparison;
and the second output sub-module is used for outputting a pitch angle adjustment instruction to stop the fan in response to the comparison result that the mechanical torque is larger than the electromagnetic torque.
In some embodiments of the application, the second determining module includes:
The first determining submodule is used for determining an included angle between a wind speed vector and a wind wheel rotation tangential direction of each fan based on wind speed data, a pitch angle adjusting instruction and a yaw angle adjusting instruction corresponding to the fan, and determining fan wake information of the fan based on the included angle between the wind wheel rotation tangential direction;
And the generation sub-module is used for generating a fan wake model based on the fan wake information of the fans of each fan.
In some embodiments of the present application, the acquisition module includes:
the acquisition sub-module is used for acquiring an integral wind field airflow diagram;
The processing sub-module is used for carrying out segmentation decoupling processing on the integral wind field airflow diagram to obtain intermediate data;
The second determining submodule is used for determining an area meeting the first preset condition in the integral wind field as a target area based on the intermediate data; wind speed data of the target area with a time sequence is determined based on the target area.
According to the three-dimensional wind field modeling device based on intelligent sensor real-time acquisition, through obtaining the integral wind field airflow diagram, the wind speed data of a target area is determined based on the integral wind field airflow diagram; inputting wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected; acquiring fan information, and carrying out complex fluid-solid coupling analysis on fan blades based on the fan information and the wind speed data to obtain an analysis result; determining respective mechanical torque and electromagnetic torque of the plurality of fans based on the analysis result, the wind speed data and the fan information; respectively comparing the mechanical torque and the electromagnetic torque of each of the plurality of fans, and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each of the plurality of fans based on a comparison result; determining a fan wake model based on respective pitch angle adjustment instructions and yaw adjustment instructions of the plurality of fans; and superposing the wake flow model and the three-dimensional wind field flow model to be corrected to obtain the corrected three-dimensional wind field flow model. The actual wind condition input by combining the actual measurement data of the target fan and the observation data of the weather station can reflect the actual wind conditions of the fan and the wind field, and particularly, the accurate modeling of complex free vortex and wake flow among units, which cannot be fitted by numerical calculation, is realized, so that the accuracy of building the three-dimensional wind field flow model is improved.
Fig. 3 is a block diagram of an electronic device in an embodiment of the application. As shown in fig. 3, the electronic device may include: a transceiver 31, a processor 32, a memory 33.
Processor 32 executes the computer-executable instructions stored in memory, causing processor 32 to perform the aspects of the embodiments described above. The processor 32 may be a general-purpose processor including a central processing unit CPU, a network processor (network processor, NP), etc.; but may also be a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.
The memory 33 is connected to the processor 32 via a system bus and communicates with each other, the memory 33 being arranged to store computer program instructions.
The transceiver 31 may be used to obtain a task to be run and configuration information of the task to be run.
The system bus may be a peripheral component interconnect (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The system bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The transceiver is used to enable communication between the database access device and other computers (e.g., clients, read-write libraries, and read-only libraries). The memory may include random access memory (random access memory, RAM) and may also include non-volatile memory (non-volatile memory).
The electronic device provided by the embodiment of the application can be the terminal device of the embodiment.
The embodiment of the application also provides a chip for running the instruction, which is used for executing the technical scheme of the message processing method in the embodiment.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and when the computer instructions run on a computer, the computer is caused to execute the technical scheme of the message processing method of the embodiment.
The embodiment of the application also provides a computer program product, which comprises a computer program stored in a computer readable storage medium, wherein at least one processor can read the computer program from the computer readable storage medium, and the technical scheme of the message processing method in the embodiment can be realized when the at least one processor executes the computer program.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (6)
1. The three-dimensional wind field modeling method based on intelligent sensor real-time acquisition is characterized by comprising the following steps:
Acquiring an integral wind field airflow diagram, and determining wind speed data of a target area based on the integral wind field airflow diagram; the integral wind field airflow diagram is obtained by collecting wind field airflow data through an intelligent sensor in the integral wind field;
inputting the wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected;
Acquiring fan information, and carrying out complex fluid-solid coupling analysis on fan blades based on the fan information and the wind speed data to obtain an analysis result; the fan information comprises fan sub-information of a plurality of fans arranged in the target area; the fan information comprises fan position information and height layer information of the fan;
Determining respective mechanical torque and electromagnetic torque of the plurality of fans based on the analysis result, the wind speed data and the fan information; the electromagnetic torque is generated by cutting magnetic lines of force of the wind driven generator body at a preset rotating speed under the drive of the impeller and the transmission shaft;
Respectively comparing the mechanical torque and the electromagnetic torque of each fan, and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each fan based on the comparison result;
determining a fan wake model based on respective pitch angle adjustment instructions and yaw adjustment instructions of the plurality of fans;
superposing the wake flow model and the three-dimensional wind field flow model to be corrected to obtain a corrected three-dimensional wind field flow model;
the method comprises the steps of comparing the mechanical torque and the electromagnetic torque of each fan, outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each fan based on a comparison result, and comprises the following steps:
comparing the mechanical torque and the electromagnetic torque of each fan to obtain a comparison result;
Responding to the comparison result that the mechanical torque is smaller than the electromagnetic torque, and outputting a yaw adjustment instruction so as to increase the windward area of the fan blade;
Responding to the comparison result that the mechanical torque is larger than the electromagnetic torque, and outputting a pitch angle adjustment instruction so as to stop the operation of the fan;
wherein, based on the respective pitch angle adjustment instruction and yaw adjustment instruction of the plurality of fans, determining a fan wake model includes:
For each fan, determining an included angle between a wind speed vector and a wind wheel rotation tangential direction of the fan based on the wind speed data, the pitch angle adjustment instruction and the yaw adjustment instruction corresponding to the fan, and determining fan wake information of the fan based on the included angle of the wind wheel rotation tangential direction;
and generating a fan wake model based on the fan wake information of each fan.
2. The method of claim 1, wherein the acquiring the global wind farm airflow map, the determining wind speed data for the target area based on the global wind farm airflow map, comprises:
Acquiring an integral wind field airflow diagram;
performing segmentation decoupling processing on the integral wind field airflow diagram to obtain intermediate data;
determining an area meeting a first preset condition in the integral wind field as the target area based on the intermediate data; wind speed data of the target area with a time sequence is determined based on the target area.
3. Three-dimensional wind field modeling device based on intelligent sensor real-time acquisition, characterized in that, the device includes:
The acquisition module is used for acquiring an integral wind field airflow diagram and determining wind speed data of a target area based on the integral wind field airflow diagram; the integral wind field airflow diagram is obtained by collecting wind field airflow data through an intelligent sensor in the integral wind field;
The input module is used for inputting the wind speed data into a preset model to obtain a three-dimensional wind field flow model to be corrected;
The analysis module is used for acquiring fan information, and carrying out complex fluid-solid coupling analysis on the fan blades based on the fan information and the wind speed data to obtain an analysis result; the fan information comprises fan sub-information of a plurality of fans arranged in the target area; the fan information comprises fan position information and height layer information of the fan;
A first determining module for determining respective mechanical torque and electromagnetic torque of the plurality of fans based on the analysis result, the wind speed data, and the fan information; the electromagnetic torque is generated by cutting magnetic lines of force of the wind driven generator body at a preset rotating speed under the drive of the impeller and the transmission shaft;
The output module is used for respectively comparing the mechanical torque and the electromagnetic torque of each of the plurality of fans and outputting a pitch angle adjustment instruction and a yaw adjustment instruction of each of the plurality of fans based on the comparison result;
the second determining module is used for determining a fan wake model based on the respective pitch angle adjustment instruction and yaw adjustment instruction of the fans;
the correction module is used for carrying out superposition processing on the wake flow model and the three-dimensional wind field flow model to be corrected so as to obtain a corrected three-dimensional wind field flow model;
Wherein the output module comprises:
The comparison sub-module is used for comparing the mechanical torque and the electromagnetic torque of each fan to obtain a comparison result;
The first output submodule is used for outputting a yaw degree adjustment instruction to increase the windward area of the fan blade in response to the comparison result that the mechanical torque is smaller than the electromagnetic torque;
The second output sub-module is used for outputting a pitch angle adjustment instruction to stop the fan in response to the comparison result that the mechanical torque is larger than the electromagnetic torque;
wherein the second determining module includes:
The first determining submodule is used for determining an included angle between a wind speed vector and a wind wheel rotation tangential direction of each fan based on the wind speed data, the pitch angle adjusting instruction and the yaw angle adjusting instruction corresponding to the fan, and determining fan wake information of the fan based on the included angle between the wind wheel rotation tangential direction;
And the generation sub-module is used for generating the fan wake model based on the fan wake information of each fan.
4. The apparatus of claim 3, wherein the acquisition module comprises:
the acquisition sub-module is used for acquiring an integral wind field airflow diagram;
The processing sub-module is used for carrying out segmentation decoupling processing on the integral wind field airflow diagram to obtain intermediate data;
The second determining submodule is used for determining an area meeting a first preset condition in the whole wind field as the target area based on the intermediate data; wind speed data of the target area with a time sequence is determined based on the target area.
5. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
The processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-2.
6. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any of claims 1-2.
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