CN114741760A - Wind speed field numerical simulation method and system with adjustable probability density - Google Patents

Wind speed field numerical simulation method and system with adjustable probability density Download PDF

Info

Publication number
CN114741760A
CN114741760A CN202210397013.1A CN202210397013A CN114741760A CN 114741760 A CN114741760 A CN 114741760A CN 202210397013 A CN202210397013 A CN 202210397013A CN 114741760 A CN114741760 A CN 114741760A
Authority
CN
China
Prior art keywords
wind speed
probability density
wind
density distribution
sequence
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210397013.1A
Other languages
Chinese (zh)
Inventor
张军强
陈本阳
李晓兵
王涛
高永亮
李晓婷
邢琳
徐健鑫
杨瑶光
贺博
祝军超
常彩霞
郑艳红
盛飞
高若楠
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian Jiaotong University
Economic and Technological Research Institute of State Grid Shaanxi Electric Power Co Ltd
Original Assignee
Xian Jiaotong University
Economic and Technological Research Institute of State Grid Shaanxi Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian Jiaotong University, Economic and Technological Research Institute of State Grid Shaanxi Electric Power Co Ltd filed Critical Xian Jiaotong University
Priority to CN202210397013.1A priority Critical patent/CN114741760A/en
Publication of CN114741760A publication Critical patent/CN114741760A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Geometry (AREA)
  • Computer Hardware Design (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Computational Mathematics (AREA)
  • Computing Systems (AREA)
  • Fluid Mechanics (AREA)
  • Algebra (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Mathematical Physics (AREA)
  • Architecture (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A wind speed field numerical simulation method and system with adjustable probability density is disclosed, wherein the simulation method comprises establishing a simulation model of a power transmission line in actual engineering; obtaining an average wind profile according to a static flow field simulation result; acquiring a pulsating wind speed sequence which accords with expected probability density distribution by combining a linear filtering method and a Gurley correlation deformation formula; and constructing an appointed wind load through the obtained average wind profile and the pulsating wind speed sequence, performing transient response analysis on the constructed appointed wind load applied to the simulation model of the power transmission line, selecting a typical tower response value, and searching for the relation between the probability density distribution of the response value and the input wind speed field probability density distribution. The invention can simulate the fluctuation of the wind speed field which is more in line with the actual measurement condition of the meteorological bureau, and is not limited to normal distribution. By utilizing the advantages that the average value, the standard deviation, the kurtosis and the skewness of the wind speed are all adjustable, the potential safety hazard caused by the local abnormal wind speed in a climate condition complex area to building of a building can be researched.

Description

Wind speed field numerical simulation method and system with adjustable probability density
Technical Field
The invention belongs to the technical field of wind load simulation, and particularly relates to a wind speed field numerical simulation method and system with adjustable probability density.
Background
Taking an overhead transmission line as an example, the current design specifications mostly adopt a method of applying static wind load and ice load to consider, the safe and stable operation of the transmission line is ensured mainly by increasing the load coefficient, the method is simple and direct, and the whole dynamic change process of bearing complex load by the transmission line in practice is not described. Therefore, it is necessary to study and simulate the concrete structure of the wind velocity field in a local area having complicated climatic conditions, such as an actual canyon, a desert, and an island.
The existing wind load simulation methods at home and abroad comprise three types of field actual measurement, wind tunnel test and simulation, and the field actual measurement and the wind tunnel test cannot be well and rapidly developed due to the restriction of field conditions, equipment price and other factors. Therefore, the wind load simulation method widely applied at present is wind load numerical simulation, and the numerical simulation is convenient to operate and is very scientific and rigorous. The harmonic wave superposition method and the linear filtering method are two common numerical simulation methods for simulating pulsating wind load.
The harmonic superposition method is a widely applied numerical simulation method, has the characteristic of discretization, and generally adopts a discrete spectrum to approach a target and establish a random model during analysis. For any pulsating wind power density function, a wind speed time-course sample meeting the power density function can be generated by utilizing a harmonic superposition method. However, when the number of the simulation points is particularly large, the time for using the harmonic superposition method is greatly increased, and the probability density distribution characteristic of the wind speed cannot be considered in the data obtained by the harmonic superposition method. The linear filtering method is a random process, and uses a white noise sequence with zero mean value to make the output spectrum have specific spectral characteristics under the action of a filter. The method has the advantages of high simulation speed, good tightness and convenience in operation, and the simulated wind speed time course sample has stronger diversity. Considering that the standard deviation of the actual wind load needs to meet certain requirements and the probability density is adjusted subsequently, the model of the autoregressive linear filter AR in the linear filtering method is selected to simulate the fluctuating wind speed, so that the exploitable potential is larger.
Disclosure of Invention
The invention aims to provide a wind speed field numerical simulation method and system with adjustable probability density, aiming at the problems in the prior art, and the method and system can be used for adjusting the probability density function of the wind speed time sequence which accords with the just-too-distributed wind speed time sequence through the change condition of the statistical parameters of the actual wind speed field, so as to realize the wind speed field numerical simulation of a large field area under the whole extreme climate condition.
In order to achieve the purpose, the invention has the following technical scheme:
a method for simulating a wind speed field value with adjustable probability density comprises the following steps:
establishing a simulation model of the power transmission line in actual engineering;
obtaining an average wind profile according to a static flow field simulation result;
acquiring a pulsating wind speed sequence which accords with expected probability density distribution by combining a linear filtering method and a Gurley correlation deformation formula;
and constructing an appointed wind load through the obtained average wind profile and the pulsating wind speed sequence, performing transient response analysis on the constructed appointed wind load applied to the simulation model of the power transmission line, selecting a typical tower response value, and searching for the relation between the probability density distribution of the response value and the input wind speed field probability density distribution.
Preferably, the establishing of the simulation model of the power transmission line in the actual engineering includes:
adopting a truss-girder hybrid modeling mode for the iron tower;
and a modeling mode of updating coordinates under the action of gravity is adopted for the wire.
Preferably, the step of obtaining the average wind profile according to the static flow field simulation result calculates the average wind speed by using a Davenport exponential rate model as follows:
Figure BDA0003599435370000021
in the formula (I), the compound is shown in the specification,
Figure BDA0003599435370000022
is the basic wind speed; y is the coordinate height; alpha is the roughness coefficient of the ground;
converting the terrain environment shot by the meteorological bureau into an equal-scale reduction model;
and setting the simulated residual error to obtain the average wind profile of the converged flow field.
Preferably, the ground roughness coefficient α is set to 0.12, 0.16, 0.22 and 0.30 according to A, B, C, D corresponding to four different landform categories.
Preferably, the step of obtaining the pulsating wind speed sequence conforming to the expected probability density distribution by combining the linear filtering method and the Gurley correlation deformation formula includes:
obtaining a random sequence which accords with normal distribution and has a mean value of 0 by utilizing an AR model of a linear filtering method;
calculating the standard deviation according to the discrete degree of the required wind speed data, and obtaining a fluctuating wind speed random sequence with the error of no more than 0.1 between the actual standard deviation and the target standard deviation by utilizing an inequality screening method through multiple cycles;
and selecting kurtosis and skewness of the wind speed data to describe the non-normality of the probability density distribution of the wind speed data, and substituting the designated kurtosis and skewness into a formula to convert the fluctuating wind speed random sequence conforming to the normal distribution into a fluctuating wind speed sequence with adjustability in the probability density distribution.
Preferably, the expression of the random sequence of the pulsating wind speed with the mean value of 0, which is obtained by the AR model using the linear filtering method and conforms to the normal distribution, is as follows:
Figure BDA0003599435370000031
multiplying both sides of the above formula by v simultaneouslyn T(t-j Δ t) and taking the expected:
Figure BDA0003599435370000032
in the formula, ΨkIs an autoregressive coefficient matrix; k is the group number of the time step and is less than p; j is also the group number of the time step but is more than k, delta t is the time step, and p is the autoregressive order; n (t) is a standard normal random sequence; l is a through-pair autocorrelation matrix RNAnd (5) performing Cholesky decomposition to obtain a lower triangular matrix.
Preferably, the Gurley correlation deformation formula is specifically as follows:
Figure BDA0003599435370000041
in the formula, RnonFor cross-correlation matrix after correlation deformation, RnIs a cross-correlation matrix before the correlation distortion, vnonFor the sequence of pulsating wind speeds after the distortion of the correlation, vnA sequence of pulsating wind speeds before the associated deformation, alpha, h3And h4Are all undetermined coefficients.
The unknown undetermined coefficients in the above formula are adjusted by the specified kurtosis and skewness changes:
Figure BDA0003599435370000042
gamma in the formula3Representing deviation of pulsating wind speed, gamma4Representing the kurtosis of the pulsating wind speed.
Preferably, the applying the constructed specified wind load to the simulation model of the power transmission line for transient response analysis, selecting a typical tower response value, and finding the relation between the probability density distribution of the response value and the input wind speed field probability density distribution includes:
drawing a probability density distribution diagram of the wind speed sequence with different kurtosis and skewness and comparing the probability density distribution diagram with normal distribution;
selecting a typical tower response value, drawing a probability density distribution diagram, and extracting kurtosis and skewness corresponding to the response value;
and acquiring a variation trend generated by the probability density distribution of response values of a typical tower when the kurtosis, the skewness and the standard deviation of the wind speed are respectively changed by using a control variable method, and establishing quantitative relation on a mathematical function.
A probability density adjustable wind speed field numerical simulation system comprises:
the simulation model establishing module is used for establishing a simulation model of the power transmission line in actual engineering;
the average wind profile acquisition module is used for acquiring an average wind profile according to a static flow field simulation result;
the system comprises a pulsating wind speed sequence acquisition module, a linear filtering method and a Gurley correlation deformation formula, wherein the pulsating wind speed sequence acquisition module is used for acquiring a pulsating wind speed sequence which accords with expected probability density distribution by combining the linear filtering method and the Gurley correlation deformation formula;
and the response analysis module is used for constructing an appointed wind load through the acquired average wind profile and the pulsating wind speed sequence, performing transient response analysis on the constructed appointed wind load applied to the simulation model of the power transmission line, selecting a typical tower response value, and searching the relation between the probability density distribution of the response value and the input wind speed field probability density distribution.
Compared with the prior art, the invention has the following beneficial effects:
the wind speed field numerical simulation method can simulate the vertical fluctuation of the wind speed field which is more in line with the actual measurement condition of the meteorological bureau, and is not limited to normal distribution. By utilizing the advantage that the average value, the standard deviation, the kurtosis and the skewness of the wind speed are all adjustable, the potential safety hazards of local abnormal wind speeds in complex climatic condition areas such as canyons, deserts and islands on buildings such as lookout desks, power transmission lines and lighthouses can be researched. By studying the relationship between the probability density distribution of the response values in the wind speed field and the specified building safety regulations, the approximate prediction of the specified building mechanical response values can be realized even only through the probability density distribution condition of the wind speed field data of the actual region. Before the engineering plan is implemented, related personnel can predict a time-course change curve of a response value after the building falls to the ground according to statistical data of a wind speed field of an area where the building is located, and determine the material strength of the building to be built in advance.
Furthermore, the wind speed field numerical simulation method provided by the invention considers the requirements on rigidity and displacement in simulation calculation, adopts a modeling mode of truss-girder frame mixing and coordinate updating under the action of gravity, and overcomes the defects of overlarge rigidity of a truss frame model, insufficient constraint of the truss model and weak real operability of a catenary equation. Aiming at the terrain environment with extreme weather conditions, the equal-scale reduction model is directly led into a simulation module, and the residual error is less than 10-4And the number of the convergence steps is more than 5000 steps, so that the obtained average wind profile is ensured to be closer to the actual working condition, the average wind profile can be externally adjusted according to the basic wind speed data obtained by the meteorological bureau, and the method is flexible, convenient and wide in application range. The wind speed time-course samples simulated by the AR model of the linear filtering method have stronger diversity but can not control the discrete degree, the standard deviation of the pulsating wind speed can be controlled by a method of carrying out screening by multiple cycles and using inequality, and the error of the standard deviation can be set to be 0.1 by combining the common requirements of time cost and simulation precision. At the moment, the kurtosis and skewness of the wind speed field which accords with normal distribution are 3 and 0, and in order to describe the distortion of the wind speed field under the actual extreme climate condition, the adjustability of the probability density distribution of the wind speed field is realized by controlling the kurtosis, skewness and standard deviation parameters from the viewpoint of statistics. And establishing a functional relation between the probability density related parameters of the typical tower response values and the probability density related parameters of the wind speed, and laying a foundation for realizing prediction of the mechanical response values of the building to be built through wind speed field data of an actual area and improving the safety coefficient after landing.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments are briefly described below, it should be understood that the following drawings only show some embodiments of the present invention, and it is obvious to those skilled in the art that other related drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for simulating a wind speed field value with adjustable probability density according to the present invention;
FIG. 2 is a three-dimensional schematic diagram of a simulation model of a power transmission line according to an embodiment of the invention;
FIG. 3 is a graph of the simulation results of the average wind YZ profile static flow field of the embodiment of the invention;
FIG. 4 is a graph of a simulation result of an average wind XZ profile static flow field according to an embodiment of the present invention;
FIG. 5 is a time-course graph of a pulsating wind speed with a mean value of 0, a target standard deviation of 3.2 and a normal distribution;
FIG. 6 is a time-course graph of a pulsating wind speed with a kurtosis of 3.5 and a skewness of 0.7 according to an embodiment of the present invention;
FIG. 7 is a probability density distribution diagram of a fluctuating wind speed with a kurtosis of 3.5 and a skewness of 0.7 according to an embodiment of the present invention;
FIG. 8 is a probability density distribution diagram of the maximum tensile stress of the iron tower under the impact of the fluctuating wind speed with the kurtosis of 3.5 and the skewness of 0.7 in the embodiment of the invention;
FIG. 9 is a time-course graph of a pulsating wind speed with a kurtosis of 4.5 and an skewness of 0.7 according to an embodiment of the present invention;
FIG. 10 is a probability density distribution diagram of a fluctuating wind speed with a kurtosis of 4.5 and a skewness of 0.7 according to an embodiment of the present invention;
FIG. 11 is a probability density distribution diagram of maximum tensile stress of an iron tower under the impact of a fluctuating wind speed with a kurtosis of 4.5 and an skewness of 0.7 in the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention.
Based on the embodiments of the present invention, those skilled in the art can make several simple modifications and decorations without creative efforts, and all other embodiments obtained belong to the protection scope of the present invention.
Reference in the present specification to "an example" means that a particular feature, structure, or characteristic described in connection with the example may be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by a person skilled in the art that the embodiments described in the present invention can be combined with other embodiments.
Referring to fig. 1, a method for simulating a wind velocity field with adjustable probability density according to an embodiment of the present invention includes the following steps:
s1, establishing a simulation model of the power transmission line in the actual engineering;
s2, obtaining an average wind profile according to the static flow field simulation result;
s3, acquiring a pulsating wind speed sequence according with expected probability density distribution by combining a linear filtering method and a Gurley correlation deformation formula;
s4, constructing a designated wind load through the obtained average wind profile and the obtained fluctuating wind speed sequence, performing transient response analysis on the constructed designated wind load applied to the simulation model of the power transmission line, selecting a typical tower response value, and searching for the relation between the probability density distribution of the response value and the input wind speed field probability density distribution.
In a possible embodiment, the step S1 of building a simulation model of the transmission line in the actual engineering includes:
adopting a truss-girder hybrid modeling mode for the iron tower;
and a modeling mode of updating coordinates under the action of gravity is adopted for the wire.
In one possible embodiment, step S2 calculates the average wind speed by the Davenport index rate model as follows:
Figure BDA0003599435370000071
in the formula (I), the compound is shown in the specification,
Figure BDA0003599435370000072
is the base wind speed; y is the coordinate height; alpha is the roughness coefficient of the ground;
converting the terrain environment shot by the meteorological bureau into an equal-scale reduction model;
and setting the simulated residual error to obtain the average wind profile of the converged flow field.
The ground roughness coefficient alpha is correspondingly set according to different landform categories according to the following relation:
landform categories Coefficient of roughness alpha
A 0.12
B 0.16
C 0.22
D 0.30
According to industry standards, the roughness of the ground can be classified into A, B, C, D four types:
1. class A refers to offshore sea surface, island, coast, lakeshore and desert areas;
2. class B refers to fields, villages, jungles, hills, and rural and urban suburbs with sparser houses;
3. class C refers to urban areas with dense building groups;
4. class D refers to urban areas with dense building groups and tall houses.
In one possible embodiment, the step S3 of obtaining a sequence of fluctuating wind speeds that conforms to the expected probability density distribution includes:
obtaining a random sequence which accords with normal distribution and has a mean value of 0 by utilizing an AR model of a linear filtering method;
calculating the standard deviation according to the discrete degree of the required wind speed data, and obtaining a fluctuating wind speed random sequence with the error of no more than 0.1 between the actual standard deviation and the target standard deviation by utilizing an inequality screening method through multiple cycles;
and selecting kurtosis and skewness of the wind speed data to describe the non-normality of the probability density distribution of the wind speed data, and substituting the designated kurtosis and skewness into a formula to convert the fluctuating wind speed random sequence conforming to the normal distribution into a fluctuating wind speed sequence with adjustability in the probability density distribution.
Furthermore, the obtained random sequence expression of the pulsating wind speed conforming to the normal distribution and having the average value of 0 is as follows:
Figure BDA0003599435370000081
multiplying both sides of the above formula by v simultaneouslyn T(t-j Δ t) and taking the expected:
Figure BDA0003599435370000091
in the formula, psikIs an autoregressive coefficient matrix; k is the group number of the time step and is less than p; j is also the group number of the time step but is more than k, delta t is the time step, and p is the autoregressive order; n (t) is a standard normal random sequence; l is a through-pair autocorrelation matrix RNAnd (5) performing Cholesky decomposition to obtain a lower triangular matrix.
The Gurley correlation deformation formula is specifically as follows:
Figure BDA0003599435370000092
in the formula, RnonFor cross-correlation matrix after correlation deformation, RnIs a cross-correlation matrix before the correlation distortion, vnonFor the sequence of pulsating wind speeds after the deformation of the correlation, vnSequence of pulsating wind speeds before the relevant deformation, alpha, h3And h4Are all undetermined coefficients.
The unknown undetermined coefficients in the above formula are adjusted by the specified kurtosis and skewness changes:
Figure BDA0003599435370000093
gamma in the formula3Representing deviation of pulsating wind speed, gamma4Representing the kurtosis of the pulsating wind speed.
In one possible embodiment, the step S4 of finding the connections between the probability density distributions includes:
drawing a probability density distribution diagram of the wind speed sequence with different kurtosis and skewness and comparing the probability density distribution diagram with the normal distribution;
selecting a typical tower response value, drawing a probability density distribution diagram, and extracting kurtosis and skewness corresponding to the response value;
and acquiring a variation trend generated by the probability density distribution of response values of a typical tower when the kurtosis, the skewness and the standard deviation of the wind speed are respectively changed by using a control variable method, and establishing quantitative relation on a mathematical function.
According to an embodiment of the invention, a truss-girder hybrid modeling mode is adopted for an iron tower of a power transmission line, main materials, inclined materials and transverse partition materials of the iron tower are regarded as beam units, auxiliary materials are regarded as rod units, the maximum displacement of a lead under self weight is only 0.00365m, the maximum displacement can be ignored within an allowable error range, and a specific tangent tower power transmission line simulation model is shown in fig. 2.
The embodiment of the invention selects local uplifted terrain with certain height difference, sets the basic wind speed as 22.5m/s, and simulates residual error less than 10-4And the convergence step number is larger than 5000 steps, and average wind static flow field simulation result graphs of YZ and XZ sections are respectively extracted and are respectively shown in fig. 3 and fig. 4.
The target standard deviation of the wind speed data required by the embodiment of the invention is 3.2, a plurality of groups of wind speed random sequences with the mean value of 0 and in accordance with normal distribution are obtained by utilizing an AR model of a linear filtering method, a fluctuating wind speed random sequence with the actual standard deviation of 3.24 is obtained by utilizing an inequality of 3.1 < sigma < 3.3 through a multi-cycle method and screening, and the error is less than 0.1, as shown in figure 5.
According to the embodiment of the invention, the kurtosis and skewness of the fluctuating wind speed data are selected to describe the non-normality of the probability density distribution, the specified kurtosis and skewness are substituted into a formula to convert the fluctuating wind speed sequence conforming to normal distribution into the fluctuating wind speed sequence with adjustability of probability density distribution, the probability density distribution diagram of the wind speed sequence with different kurtosis and skewness is drawn, and the probability density distribution diagram is compared with the normal distribution. And selecting the maximum tensile stress of the iron tower as a response value of a typical tower, drawing a probability density distribution diagram of the maximum tensile stress of the iron tower, and extracting corresponding kurtosis and skewness of the maximum tensile stress of the iron tower. The control variable method is used for researching the change trend of the probability density distribution of the maximum tensile stress of the iron tower when the kurtosis, the skewness and the standard difference of the wind speed are respectively changed, and the quantitative relation on the mathematical function is established. Wherein, the time-course curve of the fluctuating wind speed with the kurtosis of 3.5 and the skewness of 0.7, the probability density distribution and the probability density distribution corresponding to the maximum tensile stress response value of the iron tower under impact are respectively shown in fig. 6, 7 and 8; the kurtosis is 4.5, and the time-course curve of the pulsating wind speed and the probability density distribution when the skewness is 0.7 and the probability density distribution corresponding to the maximum tensile stress response value of the iron tower under impact are respectively shown in fig. 9, fig. 10 and fig. 11, which show some situations when the kurtosis is changed without changing the skewness.
Another embodiment of the present invention further provides a system for simulating a wind velocity field with adjustable probability density, including:
the simulation model establishing module is used for establishing a simulation model of the power transmission line in actual engineering;
the average wind profile acquisition module is used for acquiring an average wind profile according to a static flow field simulation result;
the system comprises a pulsating wind speed sequence acquisition module, a linear filtering method and a Gurley correlation deformation formula, wherein the pulsating wind speed sequence acquisition module is used for acquiring a pulsating wind speed sequence which accords with expected probability density distribution by combining the linear filtering method and the Gurley correlation deformation formula;
and the response analysis module is used for constructing an appointed wind load through the acquired average wind profile and the pulsating wind speed sequence, performing transient response analysis on the constructed appointed wind load applied to the simulation model of the power transmission line, selecting a typical tower response value, and searching the relation between the probability density distribution of the response value and the input wind speed field probability density distribution.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (9)

1. A method for simulating a wind speed field value with adjustable probability density is characterized by comprising the following steps:
establishing a simulation model of the power transmission line in actual engineering;
obtaining an average wind profile according to a static flow field simulation result;
acquiring a pulsating wind speed sequence which accords with expected probability density distribution by combining a linear filtering method and a Gurley correlation deformation formula;
and constructing an appointed wind load through the obtained average wind profile and the pulsating wind speed sequence, performing transient response analysis on the constructed appointed wind load applied to the simulation model of the power transmission line, selecting a typical tower response value, and searching for the relation between the probability density distribution of the response value and the input wind speed field probability density distribution.
2. The method for numerical simulation of a wind speed field with adjustable probability density according to claim 1, wherein the establishing of the simulation model of the power transmission line in the actual engineering comprises:
adopting a truss-girder hybrid modeling mode for the iron tower;
and a modeling mode of updating coordinates under the action of gravity is adopted for the wire.
3. The method for numerical simulation of a wind speed field with adjustable probability density according to claim 1, wherein the step of obtaining the average wind profile according to the simulation result of the static flow field calculates the average wind speed by using a Davenport exponential rate model as follows:
Figure FDA0003599435360000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003599435360000012
is the basic wind speed; y is the coordinate height; alpha is the roughness coefficient of the ground;
converting the terrain environment shot by the meteorological bureau into an equal-scale reduction model;
and setting the simulated residual error to obtain the average wind profile of the converged flow field.
4. The method for numerical simulation of a wind speed field with adjustable probability density of claim 3, wherein the ground roughness coefficient α is set to 0.12, 0.16, 0.22 and 0.30 according to A, B, C, D for four different landform categories.
5. The method for numerical simulation of a wind speed field with adjustable probability density according to claim 1, wherein the step of obtaining the pulsating wind speed sequence conforming to the expected probability density distribution by combining a linear filtering method and a Gurley correlation deformation formula comprises:
obtaining a random sequence which accords with normal distribution and has a mean value of 0 by utilizing an AR model of a linear filtering method;
calculating the standard deviation according to the discrete degree of the required wind speed data, and obtaining a fluctuating wind speed random sequence with the error of no more than 0.1 between the actual standard deviation and the target standard deviation by utilizing an inequality screening method through multiple cycles;
and selecting kurtosis and skewness of the wind speed data to describe the non-normality of the probability density distribution of the wind speed data, and substituting the designated kurtosis and skewness into a formula to convert the fluctuating wind speed random sequence conforming to the normal distribution into a fluctuating wind speed sequence with adjustability in the probability density distribution.
6. The method for numerical simulation of a wind speed field with adjustable probability density of claim 5, wherein the random sequence of pulsating wind speeds with an average value of 0, which is obtained by the AR model using the linear filtering method, conforms to normal distribution, and is expressed as follows:
Figure FDA0003599435360000021
multiplying both sides of the above formula by v simultaneouslyn T(t-j Δ t) and taking the expected:
Figure FDA0003599435360000022
in the formula, ΨkIs an autoregressive coefficient matrix; k is the group number of the time step and is less than p; j is also the group number of the time step but is more than k, delta t is the time step, and p is the autoregressive order; n (t) is a standard normal random sequence; l is a through-pair autocorrelation matrix RNAnd (5) performing Cholesky decomposition to obtain a lower triangular matrix.
7. The method for numerical simulation of a wind speed field with adjustable probability density of claim 5, wherein the Gurley correlation deformation formula is specifically as follows:
Figure FDA0003599435360000023
in the formula, RnonFor cross-correlation matrix after correlation deformation, RnIs a cross-correlation matrix before the correlation distortion, vnonFor the sequence of pulsating wind speeds after the deformation of the correlation, vnSequence of pulsating wind speeds before the relevant deformation, alpha, h3And h4All are undetermined coefficients;
the unknown undetermined coefficients in the above formula are adjusted by the specified kurtosis and skewness changes:
Figure FDA0003599435360000031
gamma in the formula3Deflection, gamma, representing the pulsating wind speed4Representing the kurtosis of the pulsating wind speed.
8. The wind speed field numerical simulation method with the adjustable probability density of claim 1, wherein the step of performing transient response analysis on the constructed specified wind load applied to the simulation model of the power transmission line, selecting a typical tower response value, and finding the relation between the probability density distribution of the response value and the input wind speed field probability density distribution comprises the steps of:
drawing a probability density distribution diagram of the wind speed sequence with different kurtosis and skewness and comparing the probability density distribution diagram with the normal distribution;
selecting a typical tower response value, drawing a probability density distribution graph and extracting kurtosis and skewness corresponding to the response value;
and obtaining a variation trend generated by the probability density distribution of the response value of the typical tower when the kurtosis, the skewness and the standard deviation of the wind speed are respectively changed by using a control variable method, and establishing a quantitative relation on a mathematical function.
9. A wind speed field numerical simulation system with adjustable probability density is characterized by comprising:
the simulation model establishing module is used for establishing a simulation model of the power transmission line in actual engineering;
the average wind profile acquisition module is used for acquiring an average wind profile according to a static flow field simulation result;
the system comprises a pulsating wind speed sequence acquisition module, a linear filtering method and a Gurley correlation deformation formula, wherein the pulsating wind speed sequence acquisition module is used for acquiring a pulsating wind speed sequence which accords with expected probability density distribution by combining the linear filtering method and the Gurley correlation deformation formula;
and the response analysis module is used for constructing a specified wind load through the obtained average wind profile and the pulsation wind speed sequence, performing transient response analysis on the constructed specified wind load applied to the simulation model of the power transmission line, selecting a typical tower response value, and searching the relation between the probability density distribution of the response value and the input wind speed field probability density distribution.
CN202210397013.1A 2022-04-15 2022-04-15 Wind speed field numerical simulation method and system with adjustable probability density Pending CN114741760A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210397013.1A CN114741760A (en) 2022-04-15 2022-04-15 Wind speed field numerical simulation method and system with adjustable probability density

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210397013.1A CN114741760A (en) 2022-04-15 2022-04-15 Wind speed field numerical simulation method and system with adjustable probability density

Publications (1)

Publication Number Publication Date
CN114741760A true CN114741760A (en) 2022-07-12

Family

ID=82280858

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210397013.1A Pending CN114741760A (en) 2022-04-15 2022-04-15 Wind speed field numerical simulation method and system with adjustable probability density

Country Status (1)

Country Link
CN (1) CN114741760A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117250632A (en) * 2023-08-18 2023-12-19 华南理工大学 Urban landform roughness category and wind field characteristic acquisition method, system, equipment and medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117250632A (en) * 2023-08-18 2023-12-19 华南理工大学 Urban landform roughness category and wind field characteristic acquisition method, system, equipment and medium
CN117250632B (en) * 2023-08-18 2024-03-08 华南理工大学 Urban landform roughness category and wind field characteristic acquisition method, system, equipment and medium

Similar Documents

Publication Publication Date Title
CN109635511B (en) High-rise residential district compulsory emission scheme generation design method for generating countermeasure network based on conditions
CN109934423A (en) Predicting power of photovoltaic plant method and system based on gird-connected inverter operation data
CN109359359B (en) Power transmission line wind speed field simulation method considering wind load space-time distribution characteristics
CN106873359A (en) A kind of wind-powered electricity generation noise rating method based on cluster analysis and neutral net
CN105825002B (en) A kind of wind power plant dynamic equivalent modeling method based on dynamic Gray Association Analysis
CN114741760A (en) Wind speed field numerical simulation method and system with adjustable probability density
CN110571926A (en) intelligent power distribution network based on Internet of things technology and data model construction method thereof
CN113360980A (en) BIM technology-based deformed steel structure visualization construction method
CN115858940A (en) Steel structure welding process quality management recommendation method based on big data processing
CN115659756A (en) Method for analyzing windproof performance of transmission tower
CN113836625A (en) Power transmission tower anti-seismic performance grade division method and system based on power spectrum method
CN105303055B (en) UHV transmission line nonlinear dynamic analysis method under wind shake incentive action
CN115544624A (en) Method for performing data interaction of assembled substation based on BIM technology
CN110645939A (en) System and method for dynamically predicting relative vertical distance between two wires
CN110648280A (en) Data processing method for splicing large-scale karst cave mass point cloud data
CN113449966B (en) Gypsum board equipment inspection method and system
CN114861263A (en) Method and system for finely simulating mountainous terrain wind speed field
CN112597564A (en) Extra-high voltage transmission tower structure seismic fortification classification method and system
CN115620278B (en) Method for identifying and measuring materials
CN114418194B (en) Tower damage prediction method and device based on data driving and model driving
CN115796564B (en) Colliery work management system based on meteorological supervision
CN114239353B (en) Extremum response surface-based shared pole tower newly-added base station mounting optimization arrangement method
CN117315135A (en) Depth feature fusion-based cable membrane structure digital twin model reconstruction method
CN114243770A (en) Method and system for determining distributed photovoltaic development adaptability level
CN112531683A (en) Distribution network line load prediction method based on solution of Ornstein-Urnbek process

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination