CN109460856A - Consider wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment - Google Patents
Consider wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment Download PDFInfo
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Abstract
The invention discloses a kind of consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment, since change of the wind has very important influence to wind speed correlation, wind direction is influenced for existing method to consider insufficient status, this method is in the existing consideration time, on the basis of the second grade wind series of spatial coherence, increase wind direction factor, based on Von Mises distribution and Weibull distribution, analyze the correlation of the two, obtain considering wind speed-wind direction sequence of the two correlation by Monte Carlo sampling, the wind speed correlation being further described through in wind power plant, to assess mains frequency deviation caused by output of wind electric field fluctuates more accurately.
Description
Technical field
The invention belongs to wind-electricity integration control fields, and in particular to a kind of consideration wind speed-wind direction correlation wind-powered electricity generation field frequency
Rate methods of risk assessment.
Background technique
With the continuous promotion of wind-powered electricity generation permeability, the random power fluctuation of wind power plant will cause serious power system frequency
Fluctuation, and since the output power of wind power plant and the wave characteristic of wind speed are closely related, the accurate simulation to Wind speed model is to close
Key problem.
The simulation of wind speed will consider its randomness and correlation, and previous wind speed simulation mainly goes out from the angle of frequency domain
Hair has quantified degree of correlation from the position of blower, layout etc., constructs the second grade wind in the short time with spatial coherence
Speed, the mains frequency deviation for being able to reflect the wind power swing of second grade and thus causing.
However, only considering that the second grade wind speed correlation of wind speed is often not under the background of wind-powered electricity generation field frequencies range risk assessment
Enough accurate, influence of the wind direction to wind speed correlation is also very important.Lack the wind power plant for considering that wind direction influences in the prior art
Frequency methods of risk assessment.
Summary of the invention
The purpose of the present invention is to provide a kind of consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment,
The frequency evaluation of risk of wind power plant is carried out on the basis of simulation considers wind speed-wind direction correlation wind series.
The technical solution for realizing the aim of the invention is as follows: a kind of consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range wind
Dangerous appraisal procedure, comprising the following steps:
Step 1, the density function f for finding out Weibull distributionv(v)；
Even circumferential is divided into N number of section by step 2, if sample data set is { θ }, falls into the data subset in each section
For { θi, find out { θiAbout Mean Parameters μiDispersion degree κi, by κiIt substitutes into first kind Bessel equation and acquires concentration degree ginseng
Number I0(κi)；
Step 3 utilizes I0(κi) acquire the density function f that mixing Von Mises is distributedθ(θ)；
Step 4, by fv(v) and fθ(θ) substitutes into density of simultaneous distribution function, obtains two-dimentional joint probability distribution fv,θ；
Step 5, the f obtained using step 4v,θ, wind speed v and wind direction θ is subjected to Monte Carlo sampling, obtains one group of wind
Fast wind direction sequence V (N), θ (N)；
Step 6 uses V (N) as the input parameter of Kalman's power spectral density function to fit power spectral densityFrequency domain is converted by time domain；
Step 7, the wind speed that wheel hub wind speed is equivalent to blower paddle swing flap plane using wind wheeling rotor model；
If there are M × M Fans in step 8, wind power plant, the relative coefficient between blower two-by-two is calculated in M × M Fans
γ[r,c](f), the correlation matrix γ (f) for obtaining M × M, is counted and the frequency domain wind speed S of correlation by correlation matrix
(f)；
Step 9, the wind speed V (f) for converting the frequency domain wind speed S (f) of meter and correlation to by time-frequency conversion time domain；
The wind speed V (t) of step 10, the meter that every Fans are obtained by step 9 and correlation, is computed superposition and obtains wind power plant
Power swing P (t)；
Step 11 emulates the power swing P (t) that step 10 obtains by the electric network model of matlab, obtains frequency
Rate deviation delta P (t)；
Frequency departure risk is defined as the maximum deviation that wind-force fluctuation may cause by step 12, uses time accounting bent
Line assesses frequency departure risk as statistical tool.
Compared with prior art, the present invention its distinguishing feature are as follows: (1) the invention proposes a kind of consideration wind speed-wind direction phases
The wind-powered electricity generation field frequencies range methods of risk assessment of closing property can carry out the simulation of annual wind speed while reaching the time scale of second grade；
(2) the invention discloses wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment is considered, Weibull is utilized
Probability distribution and Von Mises distribution and its Joint Distribution obtain considering the mean wind speed and wind direction sequence of the two correlation, point
The correlation for analysing the two obtains considering wind speed-wind direction sequence of the two correlation, is further described through by Monte Carlo sampling
Wind speed correlation in wind power plant, to assess mains frequency deviation caused by output of wind electric field fluctuates more accurately.
Detailed description of the invention
Fig. 1 is the wind-powered electricity generation field frequencies range methods of risk assessment stream in the year of the wind direction for considering second grade wind speed correlation of the invention
Cheng Tu.
Fig. 2 is system frequency response simplified model figure required for frequency departure of the invention calculates.
Fig. 3 is Monte Carlo sampling step figure of the invention.
Fig. 4 is time accounting curve graph of the invention.
Fig. 5 (a)~Fig. 5 (c) is the experimental result of effectiveness of the invention verifying, and wherein Fig. 5 (a) is to consider and do not consider
The wind speed of spatial coherence compares figure, and Fig. 5 (b) is to consider the figure compared with not considering that the wind power of spatial coherence fluctuates,
Fig. 5 (c) is to consider the figure compared with the frequency departure for not considering spatial coherence.
Specific embodiment
In conjunction with Fig. 1, a kind of consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment, comprising the following steps:
Step 1, the density function f for finding out Weibull distributionv(v)；
Even circumferential is divided into N number of section by step 2, if sample data set is { θ }, falls into the data subset in each section
For { θi, find out { θiAbout Mean Parameters μiDispersion degree κi, by κiIt substitutes into first kind Bessel equation and acquires concentration degree ginseng
Number I0(κi)；
Step 3 utilizes I0(κi) acquire the density function f that mixing Von Mises is distributedθ(θ)；
Step 4, by fv(v) and fθ(θ) substitutes into density of simultaneous distribution function, obtains two-dimentional joint probability distribution fv,θ；
Step 5, the f obtained using step 4v,θ, wind speed v and wind direction θ is subjected to Monte Carlo sampling, obtains one group of wind
Fast wind direction sequence V (N), θ (N)；
Step 6 uses V (N) as the input parameter of Kalman's power spectral density function to fit power spectral density Svw
(f), frequency domain is converted by time domain；
Step 7, the wind speed that wheel hub wind speed is equivalent to blower paddle swing flap plane using wind wheeling rotor model；
If there are M × M Fans in step 8, wind power plant, the relative coefficient between blower two-by-two is calculated in M × M Fans
γ[r,c](f), the correlation matrix γ (f) for obtaining M × M, is counted and the frequency domain wind speed S of correlation by correlation matrix
(f)；
Step 9, the wind speed V (f) for converting the frequency domain wind speed S (f) of meter and correlation to by time-frequency conversion time domain；
The wind speed V (t) of step 10, the meter that every Fans are obtained by step 9 and correlation, is computed superposition and obtains wind power plant
Power swing P (t)；
Step 11 emulates the power swing P (t) that step 10 obtains by the electric network model of matlab, obtains frequency
Rate deviation delta P (t)；
Frequency departure risk is defined as the maximum deviation that wind-force fluctuation may cause by step 12, uses time accounting bent
Line assesses frequency departure risk as statistical tool.
Further, the Weibull probability distributing density function in step 1 are as follows:
Wherein, scale parameter c and form parameter k can be calculated with mean μ, standard deviation: k=(σ/μ)-1.086, c
=[μ/Γ (1+1/k)]-1.086。
Further, I described in step 20(κi) it is that zeroth order corrects first kind Bessel equation, formula are as follows:
Wherein 0≤μi≤ 2 π reflect { θiMean value, be Mean Parameters；κi> 0 reflection { θ iiAbout μiDispersion degree.
Further, the Von Mises distribution density function in step 3 are as follows:
Wherein, N is the mixing group number of von Mises distribution；wi> 0 power scale parameter being distributed for each group von Mise.
Further, it is to have aweather H, density function h that wind speed > 0.3m/s wind is defined in step 4V(v) and distribution letter
Number HV(v) such as following formula:
In formula,Fv(v) it is distributed for wind speed Cumulative probability, is F equipped with wind direction distribution function aweatherθ
(θ), wind speed and direction related coefficient Ψ are calculated as follows:
In formulaThere is joint distribution function aweather are as follows:
fv,θ=2 π g (ψ) fv(v)f(θ) (6)
In formula: g (ψ) is the density function of related coefficient ψ；fv(v) and fθ(θ) is to have wind direction aweather and wind speed profile close
Spend function.
Further, as shown in figure 3, the step of sampling in step 5 is as follows:
Step 5-1 initializes wind speed and direction；
Step 5-2 is individually created continuous uniform random sequence in (v-1, v+1) (θ -1, θ+1) range and calculates；
Step 5-3 calculates h (v, θ) and h (m, n) probability；
Step 5-4 judges the size of h (v, θ) and h (m, n), if h (v, θ) < h (m, n), then enable v=m, θ=n；If it is not,
Then continue to judge amplitude withSize, if amplitude is less thanThen enable v=m, θ=n；If it is not, going to step 5-
2。
Further, the power spectral density function of step 6 frequency domain can be fitted with specific formula:
WhereinZ is hub height, Vw0For 10min wind speed average value, σvwFor 10min wind
The standard deviation of speed, αLFAnd βLFThe respectively structure snd size parameter of model；
SIEC(f) effective to shorter time scale, power swing of the period in 0.02~600s, frequency range are described
In 1/600~1/0.02Hz, SLF(f) longer time scale (10 minutes~a few houres) are directed to, describe the period in 600s or more
More low frequency fluctuations in wind speed,The power spectral density function as fitted.
Further, the equivalent wind speed in step 7:
WhereinFor the corresponding power spectral density of wheel hub wind speed of the i-th Fans,For the i-th Fans
The wind speed for being equivalent to paddle swing flap plane power spectral density, FWT[i](f) for using blower frequency domain response equivalent model,
V0For mean wind speed, R is wind wheel radius, L1For length dimension, A is dependent attenuation coefficient, and Large Scale Wind Farm Integration takes 12.
Further, the correlation matrix of step 8:
Wherein, γ[r, c] (f) be matrix element, indicate blower r and blower c between equivalent wind speed correlation；A[r,c]For
Decay factor；d[r,c]For the distance between blower r and blower c；
Wherein α[r,c]Incidence angle for blower relative to blower r and blower c line, AlongAnd AlatRespectively longitude and latitude
The decay factor in direction is spent, it is general to useAnd Alat=(17.5 ± 5) σ is approximate.
According to the available meter of correlation matrix and the frequency domain wind speed of spatial coherence:
Further, as shown in Fig. 2, the electric network model of step 11:
Wherein, H is generator inertia time constant, and D is oscillation damping coefficient, and R is rotor radius, and T is the governor time
Constant, K are power gain factor, and F indicates a part of wind turbine electric generation.Input Δ PWFor power swing, exporting Δ f is
Inclined rate deviation.
Further, as shown in figure 4, the duration curve of step 12 can indicate the frequency departure of different level when
Between.Y-axis is frequency departure (by the unitization of rating system frequency 50hz), and x-axis is that frequency departure is inclined higher than a frequency of y-axis
The percentage of time of difference.Maximum value is not easy to restrain, so defining the corresponding exemplary frequency deviation values conduct of 1% accounting of time graph
Risk assessment.
Further detailed description is done to the present invention below with reference to emulation example:
Embodiment
The present embodiment serves as a wind power plant with four Fans of 2 × 2 arrangements, emulates in MATLAB environment, specific to join
Number is shown in Table 1.
Table 1: simulation parameter
Consider the structure of the wind-powered electricity generation field frequencies range methods of risk assessment in the year of second grade wind speed correlation as shown in Figure 1, main
Including three parts: annual mean wind speed wind direction, second the grade wind speed, frequency offset estimation for considering correlation.
Assuming that four Fans wheel hubs received at this time is identical mean wind speed and standard deviation, be added wind direction because
It element and is added without shown in the grade wind speed of annual second and power such as Fig. 5 (a) and Fig. 5 (b) that wind direction factor obtains.
As can be seen that considering wind direction factor and not considering the output of wind electric field phase of wind direction factor from Fig. 5 (a), Fig. 5 (b)
Difference is larger, and the fluctuation range of frequency departure is obviously reduced after considering wind direction factor, the reason is that, the correlation of wind speed is in transverse direction
Be with longitudinal attenuation degree it is inconsistent, by measured data it is found that longitudinal attenuation degree is deeper, i.e., longitudinal degree of correlation is more
Height, under the premise of wind vector, the wind speed degree of relevancy of longitudinal seat in the plane can than it is fixed windward to when reduce, therefore, wind direction
Factor will affect the degree of correlation between wind power plant inner blower, and the decrease of degree of correlation reduces the fluctuation of output of wind electric field, with
Simulation result is consistent.
Caused by the power swing that system frequency response shown in Fig. 2 calculates available discussed above wind direction factor
Frequency departure, as a result as shown in Fig. 5 (c).
It can be seen that, consider wind direction factor from Fig. 5 (c) and do not consider frequency caused by the fluctuation of wind direction factor wind power
Rate deviation different from considers that the second grade power swing of wind direction factor can more effectively estimate frequency departure.
Claims (10)
1. a kind of consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment, which is characterized in that including following step
It is rapid:
Step 1, the density function f for finding out Weibull distributionv(v)；
Even circumferential is divided into N number of section by step 2, if sample data set is { θ }, the data subset for falling into each section is
{θi, find out { θiAbout Mean Parameters μiDispersion degree κi, by κiIt substitutes into first kind Bessel equation and acquires concentration degree parameter
I0(κi)；
Step 3 utilizes I0(κi) acquire the density function f that mixing Von Mises is distributedθ(θ)；
Step 4, by fv(v) and fθ(θ) substitutes into density of simultaneous distribution function, obtains two-dimentional joint probability distribution fv,θ；
Step 5, the f obtained using step 4v,θ, wind speed v and wind direction θ is subjected to Monte Carlo sampling, obtains one group of wind speed and wind
To sequence V (N), θ (N)；
Step 6 uses V (N) as the input parameter of Kalman's power spectral density function to fit power spectral densityIt will
Time domain is converted into frequency domain；
Step 7, the wind speed that wheel hub wind speed is equivalent to blower paddle swing flap plane using wind wheeling rotor model；
If there are M × M Fans in step 8, wind power plant, the relative coefficient between blower two-by-two is calculated in M × M Fans
γ[r,c](f), the correlation matrix γ (f) for obtaining M × M, is counted and the frequency domain wind speed S of correlation by correlation matrix
(f)；
Step 9, the wind speed V (f) for converting the frequency domain wind speed S (f) of meter and correlation to by time-frequency conversion time domain；
The wind speed V (t) of step 10, the meter that every Fans are obtained by step 9 and correlation is computed superposition and obtains the function of wind power plant
Rate fluctuates P (t)；
Step 11 emulates the power swing P (t) that step 10 obtains by the electric network model of matlab, and it is inclined to obtain frequency
Poor Δ P (t)；
Frequency departure risk is defined as the maximum deviation that wind-force fluctuation may cause by step 12, and time accounting curve is used to make
Frequency departure risk is assessed for statistical tool.
2. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature exist
In Weibull probability distributing density function in step 1 are as follows:
Wherein, scale parameter c and form parameter k can be calculated with mean μ, standard deviation: k=(σ/μ)-1.086, c=
[μ/Γ(1+1/k)]-1.086。
3. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature exist
In I described in step 20(κi) it is that zeroth order corrects first kind Bessel equation, formula are as follows:
Wherein 0≤μi≤ 2 π reflect { θiMean value, be Mean Parameters；κi> 0 reflection { θiAbout μiDispersion degree.
4. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature exist
In Von Mises distribution density function in step 3 are as follows:
Wherein, N is the mixing group number of von Mises distribution；wi> 0 power scale parameter being distributed for each group von Mise.
5. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature exist
In defining wind speed > 0.3m/s wind in step 4 is to have aweather, density function hV(v) and distribution function HV(v) such as following formula:
In formula,Fv(v) it is distributed for wind speed Cumulative probability, is F equipped with wind direction distribution function aweatherθ(θ),
Wind speed and direction related coefficient ψ is calculated as follows:
In formulaThere is joint distribution function aweather are as follows:
fv,θ=2 π g (ψ) fv(v)fθ(θ) (6)
In formula: g (ψ) is the density function of related coefficient ψ；fv(v) and fθ(θ) is the wind direction and wind speed profile density letter having aweather
Number.
6. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature exist
In the step of sampling is as follows in step 5:
Step 5-1 initializes wind speed and direction；
Step 5-2 is individually created continuous uniform random sequence in (v-1, v+1) (θ -1, θ+1) range and calculates；
Step 5-3 calculates h (v, θ) and h (m, n) probability；
Step 5-4 judges the size of h (v, θ) and h (m, n), if h (v, θ) < h (m, n), then enable v=m, θ=n；If it is not, then after
It is continuous judge amplitude andSize, if amplitude is less thanThen enable v=m, θ=n；If it is not, going to step 5-2.
7. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature exist
In the power spectral density function of step 6 frequency domain is fitted with specific formula:
WhereinZ is hub height, Vw0For 10min wind speed average value, σvwFor 10min wind speed
Standard deviation, αLFAnd βLFThe respectively structure snd size parameter of model；SIEC(f) power of the description period in 0.02~600s
Fluctuation, SLF(f) description the period the more low frequency of 600s or more fluctuations in wind speed,For the power spectral density letter fitted
Number.
8. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature exist
In equivalent wind speed in step 7:
WhereinFor the corresponding power spectral density of wheel hub wind speed of the i-th Fans,For the equivalent of the i-th Fans
For the power spectral density of the wind speed of paddle swing flap plane, FWT[i]It (f) is the equivalent model for utilizing the frequency domain response of blower, V0It is flat
Equal wind speed, R are wind wheel radius, L1For length dimension, A is dependent attenuation coefficient.
9. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature exist
In the correlation matrix of step 8:
Wherein, γ[r,c](f) it is matrix element, indicates the equivalent wind speed correlation between blower r and blower c；A[r,c]For decaying because
Son；d[r,c]For the distance between blower r and blower c；
Wherein α[r,c]Incidence angle for blower relative to blower r and blower c line, AlongAnd AlatRespectively longitude and latitude side
To decay factor.
According to the available meter of correlation matrix and the frequency domain wind speed of spatial coherence:
10. consideration wind speed-wind direction correlation wind-powered electricity generation field frequencies range methods of risk assessment according to claim 1, feature
It is, electric network model in step 11 are as follows:
Wherein, H is generator inertia time constant, and D is oscillation damping coefficient, and R is rotor radius, and T is governor time constant,
K is power gain factor, and F indicates a part of wind turbine electric generation, inputs Δ PWFor power swing, output Δ f is inclined rate
Deviation.
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| CN113449968A (en) * | 2021-06-09 | 2021-09-28 | 重庆锦禹云能源科技有限公司 | New energy power grid frequency risk assessment method and device based on deep learning |
| CN113449968B (en) * | 2021-06-09 | 2023-08-29 | 重庆锦禹云能源科技有限公司 | New energy power grid frequency risk assessment method and device based on deep learning |
| CN113468481A (en) * | 2021-07-06 | 2021-10-01 | 中国人民解放军63796部队 | Multilayer wind direction and wind speed probability distribution calculation method for tower wind measurement |
| CN113468481B (en) * | 2021-07-06 | 2023-09-26 | 中国人民解放军63796部队 | Multi-layer wind direction and wind speed probability distribution calculation method for tower wind measurement |
| CN113505460A (en) * | 2021-07-30 | 2021-10-15 | 广东电网有限责任公司 | Power line breaking tower-falling probability model establishing method and probability prediction method |
| CN114662800A (en) * | 2022-05-18 | 2022-06-24 | 浙江浙能能源服务有限公司 | Wind power prediction method and system based on artificial neural network |
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