CN109948946B - Regional wind power plant group forecasting and intermittent quantitative depicting method and device - Google Patents
Regional wind power plant group forecasting and intermittent quantitative depicting method and device Download PDFInfo
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Abstract
The embodiment of the application provides a regional wind power plant group forecasting and intermittent quantitative depicting method and device, the method and device can depict a parameter of wind speed intermittency based on a wind speed abrupt change duty ratio, then clustering division is carried out on time sequences of the wind speed abrupt change duty ratios of different wind power plants by utilizing a Pearson cross correlation analysis method, the wind power plants with strong correlation are gathered into a virtual unified scheduling unit, then the Pearson autocorrelation analysis method is utilized to determine the optimal forecasting length with high predictability in the virtual unified scheduling unit, finally a BP neural network is utilized to forecast the forecasted wind speed abrupt change duty ratio of a future regional wind power plant group, and intermittent quantitative depicting of the future regional wind power plant group is completed, and the technical problem that the intermittent nature of the regional wind power plant group is not quantitatively depicted by using specific parameters in the prior art and the accuracy is not high is solved.
Description
Technical Field
The application relates to the technical field of wind power, in particular to a method and a device for group forecast and intermittent quantitative depiction of a regional wind power plant.
Background
In recent years, wind power has developed dramatically and accounts for an increasing proportion of power systems. However, due to the intermittency of wind, the stable and safe operation of the power grid can be influenced after the wind power is connected to the grid in a large scale. Therefore, for intermittent research of large-scale wind power, more detailed decision reference information can be provided for the power system, and the method has important significance for safe operation of the power system after wind power is connected.
Currently, the current research efforts are mainly focused on wind speed intermittency. Some scholars give a qualitative definition of wind speed intermittency: a rapid variation of the mean line along the time axis that is nearly discontinuous. This causes a steep change in wind power output, which poses a threat to the safe operation of the power system, especially during peak and valley periods of the load. Because the output base point of the unit is adjusted to the high point at the load peak time, the reserve margin is obviously insufficient, if the wind power output originally connected into the power grid is suddenly reduced at the moment, the output of the power grid is likely to be unable to keep up with the change of the load, so that the frequency of the system is reduced, and the active power in the system cannot be balanced. In the load valley period, the output base point of the units is adjusted to a low point, the output of each unit is pressed to a lower position, the standby downward adjustment capacity of the system is obviously insufficient, and if the wind power output of the original power grid is suddenly increased, a wind curtailment measure is inevitably adopted to maintain the balance of the system.
Therefore, in order to eliminate the intermittent influence, many researchers predict the actual wind power climbing event. Students also propose to quantitatively depict the intermittency of the wind speed by using the starting and stopping frequency of the fan, and further propose how to utilize the starting and stopping frequency parameter of a single wind turbine to realize the evaluation of the total power generation quality and the auxiliary scheduling method after the virtual aggregation of a plurality of wind turbines in a wind farm group. However, a method for quantitatively describing the intermittence of the wind power plant group by using specific parameters does not exist, and the technical problem of low accuracy exists.
Disclosure of Invention
The embodiment of the application provides a method and a device for forecasting and quantitatively depicting intermittent wind power plant groups in an area, and solves the technical problem that the precision is low because a mode of quantitatively depicting the intermittent wind power plant groups by using specific parameters does not exist in the prior art.
In view of this, a first aspect of the present application provides a method for forecasting and intermittently describing regional wind farm groups, where the method includes:
based on the definition of the wind speed abrupt change duty ratio, obtaining a wind speed abrupt change duty ratio time sequence of each wind generation set by using historical wind speed data of each wind generation set in the regional wind power plant group;
determining the correlation of the time sequences of the wind speed abrupt change duty ratios of different wind generation sets by a Pearson cross correlation analysis method, and aggregating a plurality of wind generation sets of which the cross correlation coefficients are greater than a first preset threshold value into a virtual unified scheduling unit;
superposing the wind speed steeply-changed duty ratio time sequences of the plurality of wind generation sets in each virtual unified scheduling unit to obtain wind speed steeply-changed duty ratio time sequences of the plurality of virtual unified scheduling units;
determining the predictability of the wind speed steeply-changed duty ratio time sequence of each virtual unified scheduling unit by a Pearson autocorrelation analysis method, and taking the autocorrelation length of which the autocorrelation coefficient is greater than a second preset threshold value as the optimal forecast length of the corresponding virtual unified scheduling unit;
based on the optimal forecast length of each virtual unified unit, a plurality of forecast models are established for each virtual unified scheduling unit by using a BP neural network, the forecast wind speed abrupt change duty ratio of the wind power plant group in the future area is obtained through the plurality of forecast models, and the intermittent quantitative depiction result of the wind power plant group in the area is obtained by using the forecast wind speed abrupt change duty ratio.
Optionally, based on the definition of the wind speed steeply varying duty ratio, obtaining the time sequence of the wind speed steeply varying duty ratio of each wind turbine by using the historical wind speed data of each wind turbine in the regional wind farm group specifically includes:
obtaining a wind speed variation sequence { delta v (t) } of each wind turbine generator according to historical wind speed data of each wind turbine generator in the regional wind power plant group;
counting the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator, and obtaining a first abrupt change threshold value theta of each wind turbine generator by utilizing a normal distribution confidence interval 1 And a second steeply varying threshold value theta 2 ；
Based on the definition of the wind speed abrupt change duty ratio, according to the first abrupt change threshold theta of each wind turbine 1 And a second steeply varying threshold value theta 2 Calculating a time sequence of the wind speed abrupt change duty ratio of each wind turbine generator, wherein the definition of the wind speed abrupt change duty ratio is as follows:
where Δ t is a predetermined time interval, M is a length of a sequence of wind speed variation { Δ v (t) } at the predetermined time interval Δ t, and N is 1 And N 2 The times of the steep rise and the steep fall of the wind speed in the preset time period are respectively, and the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator is higher than a first steep change threshold value theta 1 Is below a second steep threshold theta 2 The wind speed is steeply reduced in the second time period, and N is determined according to the number of times of steep rise of the wind speed in the first time period 1 Determining N according to the number of times of steep drop of wind speed in the second time period 2 。
Optionally, determining the correlation of the time series of the wind speed steeply changing duty ratios of different wind turbines by using a Pearson cross correlation analysis method, and aggregating a plurality of wind turbines of which cross correlation coefficients are greater than a first preset threshold into a virtual unified scheduling unit specifically includes:
calculating the cross-correlation coefficient of the wind speed abrupt change duty ratio time sequence of every two wind turbine generators through a Pearson cross-correlation function, wherein the Pearson cross-correlation function specifically comprises the following steps:
in the formula, k 1 Is a first predetermined delay step, n 1 For the length of the wind speed steeply varying duty cycle time series of the wind turbine,
and &>
Is the mean value of the time sequence of the wind speed steeply changing duty ratio of the two wind turbine generators, and is used for changing the duty ratio of the wind turbine generators>
The method is to make the wind speed of the wind turbine generator set change the time sequence y of the duty ratio suddenly t Delay k 1 Time series obtained after a step size, gamma x,y (k 1 ) Is a delay of k 1 Covariance, gamma, obtained after each step x,y (0) Is the delay step k 1 Covariance when =0, p x,y (k 1 ) Is a delay of k 1 Obtaining a cross correlation coefficient after each step length;
and when the cross correlation coefficient is larger than a first preset threshold value, the two wind turbine sets are integrated into a virtual unified scheduling unit.
Optionally, determining the predictability of the wind speed steeply-varying duty cycle time sequence of each virtual unified scheduling unit by a Pearson autocorrelation analysis method, and taking the autocorrelation length of which the autocorrelation coefficient is greater than a second preset threshold as the optimal forecast length of the corresponding virtual unified scheduling unit specifically includes:
calculating an autocorrelation coefficient of a wind speed steeply-varied duty ratio time sequence of each virtual unified scheduling unit through a Pearson autocorrelation function, wherein the Pearson autocorrelation function is specifically as follows:
in the formula, k 2 For the second predetermined delay step, n 2 To be virtually unifiedThe length of the wind speed abrupt duty cycle time series of the scheduling unit,
is the mean value of the time series of the wind speed steeply changing duty ratio of the virtual unified scheduling unit>
The method is to make the wind speed of the original virtual unified scheduling unit change the time sequence x of the duty ratio suddenly t Delay k 2 Time series obtained after a step size, gamma (k) 2 ) Is a delay of k 2 Covariance after step size, γ (0) is delay step size k 2 Covariance when =0, ρ (k) 2 ) Is a delay of k 2 Obtaining autocorrelation coefficients after each step length;
and when the autocorrelation coefficient is larger than a second preset threshold value, taking the autocorrelation length as the optimal forecast length of the corresponding virtual unified dispatching unit.
Optionally, based on the optimal forecast length of each virtual unified unit, establishing a plurality of forecast models for each virtual unified scheduling unit by using a BP neural network, obtaining a forecast wind speed abrupt change duty ratio of a future regional wind farm group through the plurality of forecast models, and obtaining an intermittent quantitative characterization result of the regional wind farm group by using the forecast wind speed abrupt change duty ratio, further includes:
and making an auxiliary scheduling scheme according to the forecasted wind speed abrupt change duty ratio of the regional wind power plant group and the intermittent quantitative characterization result.
The second aspect of the present application provides a device for group forecast and intermittent quantitative characterization of regional wind farm, the device includes:
the sequence generating unit is used for obtaining the wind speed abrupt change duty ratio time sequence of each wind generation set by using the historical wind speed data of each wind generation set in the regional wind power plant group based on the definition of the wind speed abrupt change duty ratio;
the cross-correlation analysis unit is used for determining the correlation of the wind speed abrupt change duty ratio time sequences of different wind generation sets through a Pearson cross-correlation analysis method and aggregating a plurality of wind generation sets of which the cross-correlation coefficients are larger than a first preset threshold value into a virtual unified scheduling unit;
the superposition unit is used for superposing the wind speed steeply-changed duty ratio time sequences of the plurality of wind generation sets in each virtual unified scheduling unit to obtain the wind speed steeply-changed duty ratio time sequences of the plurality of virtual unified scheduling units;
the autocorrelation analysis unit is used for determining the predictability of the wind speed abrupt change duty ratio time sequence of each virtual unified scheduling unit through a Pearson autocorrelation analysis method, and the autocorrelation length of which the autocorrelation coefficient is larger than a second preset threshold value is used as the optimal forecast length of the corresponding virtual unified scheduling unit;
and the forecasting and describing unit is used for establishing a plurality of forecasting models by utilizing a BP neural network aiming at each virtual unified scheduling unit based on the optimal forecasting length of each virtual unified unit, obtaining the forecasted wind speed steeply-changed duty ratio of the wind power plant group in the future area through the plurality of forecasting models, and obtaining the intermittent quantitative describing result of the wind power plant group in the area by utilizing the forecasted wind speed steeply-changed duty ratio.
Optionally, the sequence generating unit is further configured to:
obtaining a wind speed variation sequence { delta v (t) } of each wind turbine generator according to historical wind speed data of each wind turbine generator in the regional wind power plant group;
counting the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator, and obtaining a first steepness change threshold value theta of each wind turbine generator by utilizing a normal distribution confidence interval 1 And a second steeply varying threshold value theta 2 ；
Based on the definition of the wind speed abrupt change duty ratio, according to the first abrupt change threshold value theta of each wind turbine 1 And a second steeply varying threshold value theta 2 Calculating a time sequence of the wind speed abrupt change duty ratio of each wind turbine generator, wherein the definition of the wind speed abrupt change duty ratio is as follows:
wherein, Δ t is a preset time interval, and M is a sequence of wind speed variation { Δ ] at the preset time interval Δ tLength of v (t) }, N 1 And N 2 The times of the steep rise and the steep fall of the wind speed in the preset time period are respectively, and the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator is higher than a first steep change threshold value theta 1 Is below a second steep threshold theta 2 The wind speed is steeply reduced in the second time period, and N is determined according to the number of times of steep rise of the wind speed in the first time period 1 Determining N according to the number of steep drop of wind speed in the second time period 2 。
Optionally, the cross-correlation analysis unit is further configured to:
and calculating the cross-correlation coefficient of the wind speed abrupt change duty ratio time sequence of every two wind turbine generators through a Pearson cross-correlation function, wherein the Pearson cross-correlation function specifically comprises the following steps:
in the formula, k 1 Is a first predetermined delay step, n 1 For the length of the wind speed steeply varying duty cycle time series of the wind turbine,
and &>
Is the mean value of the time sequence of the wind speed steeply changing duty ratio of the two wind turbine generators, and is used for changing the duty ratio of the wind turbine generators>
The method is to make the wind speed of the wind turbine generator set change the duty ratio time sequence y t Delay k 1 Time series obtained after a step size, gamma x,y (k 1 ) Is a delay of k 1 Covariance, gamma, obtained after step size x,y (0) Is the delay step k 1 A covariance when =0 is satisfied,ρ x,y (k 1 ) Is a delay of k 1 Obtaining a cross correlation coefficient after each step length;
and when the cross correlation coefficient is larger than a first preset threshold value, the two wind turbine sets are integrated into a virtual unified scheduling unit.
Optionally, the autocorrelation analysis unit is further configured to:
calculating the autocorrelation coefficient of the wind speed abrupt change duty ratio time sequence of each virtual unified scheduling unit through a Pearson autocorrelation function, wherein the Pearson autocorrelation function is specifically as follows:
in the formula, k 2 Is a second predetermined delay step, n 2 For the length of the wind speed ramp duty cycle time series of the virtual unified scheduling unit,
is the mean value of the time sequence of the wind speed steeply changing duty ratio of the virtual unified scheduling unit>
The method is to make the wind speed of the original virtual unified scheduling unit change the time sequence x of the duty ratio suddenly t Delay k 2 Time series obtained after a step size, gamma (k) 2 ) Is a delay of k 2 Covariance after step size, γ (0) is delay step size k 2 Covariance when =0, ρ (k) 2 ) Is a delay of k 2 Obtaining autocorrelation coefficients after each step length;
and when the autocorrelation coefficient is larger than a second preset threshold value, taking the autocorrelation length as the optimal forecast length of the corresponding virtual unified dispatching unit.
Optionally, the method further comprises:
and the scheduling scheme making unit is used for making an auxiliary scheduling scheme according to the forecast wind speed abrupt change duty ratio of the regional wind power plant group and the intermittent quantitative depicting result.
According to the technical scheme, the embodiment of the application has the following advantages:
the method comprises the steps of describing a parameter of wind speed intermittency based on a wind speed abrupt change duty ratio, clustering and dividing time sequences of the wind speed abrupt change duty ratios of different wind generation sets by utilizing a Pearson cross correlation analysis method, gathering the wind generation sets with strong correlation into a virtual unified scheduling unit, determining the optimal prediction length with high predictability in the virtual unified scheduling unit by utilizing a Pearson autocorrelation analysis method, finally predicting the forecast wind speed abrupt change duty ratio of a future region wind generation set by utilizing a BP neural network, and completing the intermittent quantitative description of the future region wind generation set.
Drawings
FIG. 1 is a flowchart of a method for forecasting regional wind farm groups and describing intermittent quantitative depiction in the embodiment of the application;
FIG. 2 is a device structure diagram of a device for group forecasting and intermittent quantitative characterization of a regional wind farm in an embodiment of the present application;
FIG. 3 is a wind speed time sequence diagram of a wind power farm group of inner Mongolia in 1 month;
FIG. 4 is a time series diagram of wind speed variation;
FIG. 5 is a diagram illustrating a statistical distribution of wind speed variation;
FIG. 6 is a time series diagram of the steep duty cycle of wind speed.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The application designs a method and a device for forecasting regional wind power plant group and quantitatively depicting intermittent nature, and solves the technical problem that the precision is not high because a mode that the intermittent nature of the wind power plant group is quantitatively depicted by using specific parameters does not exist in the prior art.
For convenience of understanding, please refer to fig. 1, in which fig. 1 is a flowchart of a method for forecasting regional wind farm groups and describing intermittent quantitative characterization in an embodiment of the present application, and as shown in fig. 1, the method specifically includes:
101. based on the definition of the wind speed abrupt change duty ratio, obtaining a wind speed abrupt change duty ratio time sequence of each wind generation set by using historical wind speed data of each wind generation set in the regional wind power plant group;
102. determining the correlation of the time sequences of the wind speed abrupt change duty ratios of different wind generation sets by a Pearson cross correlation analysis method, and aggregating a plurality of wind generation sets of which the cross correlation coefficients are greater than a first preset threshold value into a virtual unified scheduling unit;
103. superposing the time sequences of the wind speed abrupt change duty ratios of the plurality of wind generation sets in each virtual unified scheduling unit to obtain the time sequences of the wind speed abrupt change duty ratios of the plurality of virtual unified scheduling units;
104. determining the predictability of the wind speed steeply-changed duty ratio time sequence of each virtual unified scheduling unit by a Pearson autocorrelation analysis method, and taking the autocorrelation length of which the autocorrelation coefficient is greater than a second preset threshold value as the optimal forecast length of the corresponding virtual unified scheduling unit;
105. based on the optimal forecast length of each virtual unified unit, a plurality of forecast models are established for each virtual unified scheduling unit by using a BP neural network, the forecast wind speed steeply-changed duty ratio of the wind power plant group in the future area is obtained through the plurality of forecast models, and the intermittent quantitative depicting result of the wind power plant group in the area is obtained by using the forecast wind speed steeply-changed duty ratio.
Further, based on the definition of the wind speed steeply varying duty ratio, obtaining the time sequence of the wind speed steeply varying duty ratio of each wind turbine by using the historical wind speed data of each wind turbine in the regional wind farm group specifically includes:
obtaining a wind speed variation sequence { delta v (t) } of each wind turbine generator according to historical wind speed data of each wind turbine generator in the regional wind power plant group;
counting the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator, and obtaining a first steepness change threshold value theta of each wind turbine generator by utilizing a normal distribution confidence interval 1 And a second steeply varying threshold value theta 2 ；
Based on the definition of the wind speed abrupt change duty ratio, according to the first abrupt change threshold theta of each wind turbine 1 And a second steeply varying threshold value theta 1 2 Calculating a time sequence of the wind speed abrupt change duty ratio of each wind turbine generator, wherein the definition of the wind speed abrupt change duty ratio is as follows:
where Δ t is a predetermined time interval, M is a length of a sequence of wind speed variation { Δ v (t) } at the predetermined time interval Δ t, and N is 1 And N 2 The times of the steep rise and the steep fall of the wind speed in the preset time period are respectively, and the distribution of the wind speed variation sequence { delta v (t) } of each wind turbine generator is higher than a first steep change threshold delta 1 Is below a second steep threshold delta 2 The wind speed is steeply reduced in the second time period, and N is determined according to the number of times of steep rise of the wind speed in the first time period 1 Determining N according to the number of times of steep drop of wind speed in the second time period 2 。
Note that, when the wind speeds at time t and t + Δ t are denoted by v (t) and v (t + Δ t), respectively, the amount of change in the wind speed Δ v (t) = v (t + Δ t) -v (t) in the Δ t time interval. For the wind speed variation, a positive first steep threshold theta is given in advance 1 And a negative second steeply changing threshold theta 2 When Δ v (t) > θ 1 Time, it shows that one wind speed occursRising steeply; when Δ v (t) < θ 2 It indicates a steep drop in wind speed. Taking a wind speed sequence in a preset time period L (which can be 1 hour or 1 day), calculating a wind speed variation sequence { delta v (t) } at a given time interval delta t, recording the length of the sequence as M, and counting the times of steep rise and steep change of the wind speed in the time period L respectively as N 1 And N 2 . On this basis, the wind speed ramp duty cycle (DRWSR) is defined as follows:
likewise, a wind speed ramp up duty cycle (DRWSRU) and a wind speed ramp down duty cycle (DRWSRD) may also be defined:
the wind speed ramp duty cycle (DRWSR) actually represents the proportion of the duration of the wind speed ramp over a period of time, with a value in the range of [0,1]. The larger the DRWSR is, the longer the duration of the steep change of the wind speed in a period of time is, the stronger the intermittence of the wind speed in the period of time is; conversely, the smaller the DRWSR is, the shorter the steep change duration of the wind speed in a period of time is, and the lower the intermittency of the wind speed in the period of time is. Therefore, the intermittent quantitative characterization of the wind speed can be realized by using the parameter of the wind speed abrupt change duty ratio.
According to the definition of the wind speed steep duty ratio, it can be seen that the wind speed steep duty ratio is related to the times of the wind speed steep rise and steep fall, and the judgment of the wind speed steep rise and steep fall is related to the first steep change threshold value theta given in advance 1 And a second steeply varying threshold value theta 2 In this regard, a determination method that gives a threshold value is required. In the present invention, we use the confidence interval method to give these two thresholds. After obtaining the wind speed variation sequence (delta v (t)), the distribution is counted. Taking normal distribution as an example, assuming that the statistical distribution of the wind speed variation sequence { Δ v (t) } conforms to normal distribution, it indicates that 95% of data in the { Δ v (t) } is in the interval [ mu-2 sigma, mu +2 sigma } []In that is
P(μ-2σ≤Δv(t)≤μ+2σ)＝95％；
Where μ is the mean of { Δ v (t) } and σ is the standard deviation of { Δ v (t) }.
Let theta 1 ＝μ+2σ，θ 2 = μ -2 σ, it indicates that we consider the variation of 95% in { Δ v (t) } as the normal wind speed variation, in the interval [ μ -2 σ, μ +2 σ }]Whereas the remaining 5% of the wind speed variation represents a steep change in wind speed.
And finally, calculating a wind speed abrupt change duty ratio time sequence of each wind turbine generator based on the real wind speed data, wherein the sampling interval of all wind speed data is 5s, and the preset time interval delta t during calculation is selected to be 1min. Whereas the power system day-ahead schedule is made in units of hours, so the preset time period L is taken to be 1 hour.
FIG. 3 is a 1 month wind speed time sequence diagram of a wind power plant group in inner Mongolia, and the sampling time is 5s. Figure 4 is a sequence of resulting wind speed variations at 1min time intervals deltav (t),
further, the method for determining the correlation of the time sequences of the wind speed abrupt change duty ratios of different wind generation sets through a Pearson cross correlation analysis method, and the step of aggregating a plurality of wind generation sets of which the cross correlation coefficients are larger than a first preset threshold value into a virtual unified scheduling unit specifically comprises the following steps:
calculating the cross-correlation coefficient of the wind speed abrupt change duty ratio time sequence of every two wind turbine generators through a Pearson cross-correlation function, wherein the Pearson cross-correlation function specifically comprises the following steps:
in the formula, k 1 Is a first predetermined delay step, n 1 The length of the wind speed steeply-changed duty ratio time sequence of the wind turbine generator,
and &>
Is two typhoonsMean value of the time sequence of the wind speed steeply changing duty ratio of the motor group>
The method is to make the wind speed of the wind turbine generator set change the time sequence y of the duty ratio suddenly t Delay k 1 Time series obtained after a step size, gamma x,y (k 1 ) Is a delay of k 1 Covariance, gamma, obtained after step size x,y (0) Is the delay step k 1 Covariance in case of =0, p x,y (k 1 ) Is a delay of k 1 Obtaining a cross-correlation coefficient after each step length;
and when the cross correlation coefficient is larger than a first preset threshold value, the two wind turbine sets are integrated into a virtual unified scheduling unit.
It should be noted that, by using Pearson cross correlation function method, let { x } t } t＝1:n And { y t } t＝1:n Is a random time sequence, then measure x t And y t Is defined as the covariance of the samples, i.e.:
the cross-correlation function is defined as:
random time series { x in the above equation t } t＝1:n And { y t } t＝1:n Respectively replacing the time sequences with 2 abrupt duty ratio time sequences of actual wind generation sets, and calculating the cross-correlation function of the time sequences of the abrupt duty ratio of the wind speed of the fan according to the definition of the cross-correlation function, wherein k is a delay step length during calculation, n is the length of the time sequences of the abrupt duty ratio of the wind speed of the fan,
and &>
Is the mean value of the time sequence of the wind speed abrupt change duty ratio of the fan, y t+k The wind speed of a fan is changed suddenly into a time sequence y of duty ratio t Time series, gamma, obtained after delaying by k steps x,y (k) Is the covariance delayed by k steps, gamma x,y (0) Is the covariance at delay step k =0, p x,y (k) Is the cross-correlation coefficient obtained after delaying by k steps.
As the cross-correlation length increases, the correlation coefficient tends to decrease. Whereas a cross-correlation coefficient between 0.5 and 0.8 is generally considered to indicate that a strong correlation exists between the data, the first predetermined threshold for the cross-correlation coefficient in this application is taken to be 0.6. When the cross correlation coefficient is larger than 0.6, the two wind generating sets can be considered to be virtually aggregated.
And finally, analyzing the cross correlation of the time sequences of the wind speed abrupt change duty ratios of every two different wind generation sets according to the calculation result, and realizing the aggregation division of the virtual power generation field groups of the multiple wind generation sets. According to the statistical result, the wind turbines with strong correlation are gathered together to form a virtual wind field, namely a virtual unified scheduling unit.
Further, determining the predictability of the wind speed steeply-changing duty ratio time sequence of each virtual unified scheduling unit by a Pearson autocorrelation analysis method, and taking the autocorrelation length of which the autocorrelation coefficient is greater than a second preset threshold as the optimal forecast length of the corresponding virtual unified scheduling unit specifically comprises:
calculating an autocorrelation coefficient of a wind speed steeply-varied duty ratio time sequence of each virtual unified scheduling unit through a Pearson autocorrelation function, wherein the Pearson autocorrelation function is specifically as follows:
in the formula, k 2 Is a second predetermined delay step, n 2 For the length of the wind speed ramp duty cycle time series of the virtual unified scheduling unit,
is the mean value of the time series of the wind speed steeply changing duty ratio of the virtual unified scheduling unit>
The method is to make the wind speed of the original virtual unified scheduling unit change the time sequence x of the duty ratio suddenly t Delay k 2 Time series obtained after a step size, gamma (k) 2 ) Is a delay of k 2 Covariance after each step, γ (0) is the delay step k 2 Covariance when =0, ρ (k) 2 ) Is a delay of k 2 Obtaining autocorrelation coefficients after each step length;
and when the autocorrelation coefficient is larger than a second preset threshold value, taking the autocorrelation length as the optimal forecast length of the corresponding virtual unified dispatching unit.
It should be noted that, the power system needs to ensure the balance of energy supply and demand, and after the large-scale wind turbine generator system is connected to the grid, in order to ensure the safe and stable operation of the system, the power system needs to provide enough rotation reserve to stabilize the fluctuation of the wind power. Aiming at the intermittent problem of the wind turbine generator, if the information about the intermittent nature of the regional wind farm group can be provided in advance, more spare capacity can be increased for the time period with stronger intermittent nature of the wind farm group, and less spare capacity can be reserved for the time period with weaker intermittent nature of the wind farm group.
The predictability of the whole wind field abrupt duty ratio is researched by utilizing a Pearson autocorrelation analysis method. Let { x t } t＝1:n Is a random time sequence, x t Delayed by k steps of sample x t+k The autocorrelation coefficient between them indicates the degree of correlation of the two signals. The larger the autocorrelation coefficient is, the stronger dependence relationship exists between the two signals, and the rules hidden in the data can be mined by using a statistical method to realize the prediction of future data. And k steps at this time represent the autocorrelation length.
The basic principle of the Pearson autocorrelation analysis method is as follows:
the classic method of time series autocorrelation is Pearson autocorrelation function, which sets { x t } t＝1:n Is a random time sequence, then measure x t Delayed by k steps of sample x t+k The autocorrelation coefficient ρ (k) of (a) is calculated as follows:
in specific calculation, the random time sequence { x in the above formula t } t＝1:n Replacing the time sequence of the wind field abrupt change duty ratio of the virtual unified scheduling unit by actual superposition, calculating the autocorrelation function of the obtained time sequence of the wind field abrupt change duty ratio of the virtual unified scheduling unit, wherein k is the delay step length, n is the length of the time sequence of the wind speed abrupt change duty ratio,
is the mean value, x, of the time series of the wind speed steeply changing duty ratio t+ k is the time series x of the abrupt change of the duty ratio of the original wind speed t And (3) obtaining a time sequence after delaying k steps, wherein gamma (k) is covariance after delaying k steps, gamma (0) is covariance when delaying step k =0, and rho (k) is an autocorrelation coefficient obtained after delaying k steps, and the predictability of the wind field abrupt duty ratio time sequence is analyzed according to the calculation result.
As the autocorrelation length increases, the autocorrelation coefficient tends to decrease. Whereas an autocorrelation coefficient between 0.5 and 0.8 is generally considered to indicate a strong correlation between the data, the threshold value taken for autocorrelation coefficients in this application is 0.6. And taking the autocorrelation length with the autocorrelation coefficient of more than 0.6 as the optimal forecast length, showing that a strong dependence exists between the signal at the current moment and the signal after the optimal forecast length time, and predicting the wind field abrupt change duty ratio in a future period of time by using the historical wind speed abrupt change duty ratio by adopting a statistical method.
The predictability of the wind field steep rising duty ratio and the wind field steep falling duty ratio time series obtained by further superposition can also be analyzed by the same method.
Further, based on the optimal forecast length of each virtual unified unit, establishing a plurality of forecast models by using a BP neural network for each virtual unified scheduling unit, obtaining the forecast wind speed abrupt change duty ratio of the wind power plant group in the future area through the plurality of forecast models, and obtaining the intermittent quantitative characterization result of the wind power plant group in the area by using the forecast wind speed abrupt change duty ratio, and the method further comprises the following steps:
and (4) making an auxiliary scheduling scheme according to the forecasted wind speed abrupt change duty ratio of the regional wind power plant group and the intermittent quantitative characterization result.
It should be noted that, a corresponding multi-step forecasting model is established by adopting a BP neural network, and the abrupt change duty ratio of the wind field is forecasted, so that intermittent quantitative depiction of the wind field in the future area is completed. And the steep rising duty ratio and the steep falling duty ratio of the regional wind farm group can also be forecasted.
The method comprises the steps of carrying out real-time scheduling and optimal control on a partitioned power system, making a more reasonable and economic scheduling plan according to a forecast result of a regional wind field abrupt duty ratio, providing more spare capacity in a time period with stronger wind field intermittence, and reserving less spare capacity in a time period with weaker wind field intermittence. In addition, the forecasting method has certain reference significance for other wind engineering fields.
The above is a description of a method flow of the regional wind farm group forecasting and intermittent quantitative characterization method provided by the present application, and the following is a description of a device structure of the regional wind farm group forecasting and intermittent quantitative characterization device provided by the present application.
Referring to fig. 2, the present application provides a device structure diagram of a device for group forecast and intermittent quantitative characterization of a regional wind farm, the device includes:
the sequence generating unit 201 is configured to obtain a wind speed steeply varying duty ratio time sequence of each wind turbine generator by using historical wind speed data of each wind turbine generator in the regional wind farm group based on the definition of the wind speed steeply varying duty ratio;
the cross-correlation analysis unit 202 is used for determining the correlation of the wind speed steeply-varying duty ratio time sequences of different wind generation sets by a Pearson cross-correlation analysis method, and aggregating a plurality of wind generation sets of which the cross-correlation coefficients are larger than a first preset threshold value into a virtual unified scheduling unit;
the superposition unit 203 is used for superposing the wind speed abrupt change duty ratio time sequences of the plurality of wind power generation sets in each virtual unified scheduling unit to obtain the wind speed abrupt change duty ratio time sequences of the plurality of virtual unified scheduling units;
the autocorrelation analysis unit 204 is configured to determine predictability of the wind speed steeply-varying duty ratio time sequence of each virtual unified scheduling unit by a Pearson autocorrelation analysis method, and use an autocorrelation length of which an autocorrelation coefficient is greater than a second preset threshold as an optimal forecast length of the corresponding virtual unified scheduling unit;
and the forecasting and describing unit 205 is used for establishing a plurality of forecasting models for each virtual unified scheduling unit by using a BP neural network based on the optimal forecasting length of each virtual unified unit, obtaining the forecasted wind speed steeply-changed duty ratio of the wind power plant group in the future region by using the plurality of forecasting models, and obtaining the intermittent quantitative describing result of the wind power plant group in the region by using the forecasted wind speed steeply-changed duty ratio.
Further, the sequence generating unit 201 is further configured to:
obtaining a wind speed variation sequence { delta v (t) } of each wind turbine generator according to historical wind speed data of each wind turbine generator in the regional wind power plant group;
counting the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator, and obtaining a first steepness change threshold value theta of each wind turbine generator by utilizing a normal distribution confidence interval 1 And a second steeply varying threshold value theta 2 ；
Based on the definition of the wind speed abrupt change duty ratio, according to the first abrupt change threshold theta of each wind turbine 1 And a second steeply varying threshold value theta 2 Calculating a time sequence of the wind speed abrupt change duty ratio of each wind turbine generator, wherein the definition of the wind speed abrupt change duty ratio is as follows:
where Δ t is a predetermined time interval, M is a length of a sequence of wind speed variation { Δ v (t) } at the predetermined time interval Δ t, and N is 1 And N 2 The times of the steep rise and the steep fall of the wind speed in the preset time period are respectively, and the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator is higher than a first steep change threshold value theta 1 Is below a second steep threshold theta 2 The wind speed is steeply decreased in the second time period, and N is determined according to the number of times of steep increase of the wind speed in the first time period 1 Determining N according to the number of times of steep drop of wind speed in the second time period 2 。
Further, the cross-correlation analysis unit 202 is further configured to:
calculating the cross-correlation coefficient of the wind speed abrupt change duty ratio time sequence of every two wind turbine generators through a Pearson cross-correlation function, wherein the Pearson cross-correlation function specifically comprises the following steps:
in the formula, k 1 Is a first predetermined delay step, n 1 The length of the wind speed steeply-changed duty ratio time sequence of the wind turbine generator,
and &>
Is the mean value of the time sequence of the wind speed steeply changing duty ratio of the two wind turbine generator sets>
The method is to make the wind speed of the wind turbine generator set change the duty ratio time sequence y t Delay k 1 Time series obtained after a step size, gamma x,y (k 1 ) Is a delay of k 1 Covariance, gamma, obtained after step size x,y (0) Is the delay step k 1 Protocol for time interval of =0Variance, ρ x,y (k 1 ) Is a delay of k 1 Obtaining a cross-correlation coefficient after each step length;
and when the cross correlation coefficient is larger than a first preset threshold value, the two wind turbine sets are integrated into a virtual unified scheduling unit.
Further, the autocorrelation analysis unit 204 is further configured to:
calculating an autocorrelation coefficient of a wind speed steeply-varied duty ratio time sequence of each virtual unified scheduling unit through a Pearson autocorrelation function, wherein the Pearson autocorrelation function is specifically as follows:
in the formula, k 2 Is a second predetermined delay step, n 2 For the length of the wind speed ramp duty cycle time series of the virtual unified scheduling unit,
is the mean value of the time sequence of the wind speed steeply changing duty ratio of the virtual unified scheduling unit>
The method is to make the wind speed of the original virtual unified scheduling unit change the time sequence x of the duty ratio suddenly t Delay k 2 Time series obtained after a step size, gamma (k) 2 ) Is a delay of k 2 Covariance after each step, γ (0) is the delay step k 2 Covariance when =0, ρ (k) 2 ) Is a delay of k 2 Obtaining autocorrelation coefficients after each step length;
and when the autocorrelation coefficient is larger than a second preset threshold value, taking the autocorrelation length as the optimal forecast length of the corresponding virtual unified scheduling unit.
Further, still include:
and the scheduling scheme making unit 206 is used for making an auxiliary scheduling scheme according to the forecasted wind speed abrupt change duty ratio of the regional wind power plant group and the intermittent quantitative depiction result.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b and c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
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, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above 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; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (8)
1. A regional wind power station group forecasting and intermittent quantitative depicting method is characterized by comprising the following steps:
based on the definition of the wind speed abrupt change duty ratio, obtaining a wind speed abrupt change duty ratio time sequence of each wind generation set by using historical wind speed data of each wind generation set in the regional wind power plant group;
the correlation of the time sequences of the wind speed abrupt change duty ratios of different wind generation sets is determined by a Pearson cross correlation analysis method, and a plurality of wind generation sets with cross correlation coefficients larger than a first preset threshold value are aggregated into a virtual unified scheduling unit, which specifically comprises the following steps:
and calculating the cross-correlation coefficient of the wind speed abrupt change duty ratio time sequence of every two wind turbine generators through a Pearson cross-correlation function, wherein the Pearson cross-correlation function specifically comprises the following steps:
in the formula, k 1 Is a first predetermined delay step, n 1 The length of the wind speed steeply-changed duty ratio time sequence of the wind turbine generator,
and &>
Is the mean value of the time sequence of the wind speed steeply changing duty ratio of the two wind turbine generators, and is used for changing the duty ratio of the wind turbine generators>
The method is to make the wind speed of the wind turbine generator set change the time sequence y of the duty ratio suddenly t Delay k 1 Time series obtained after a step size, gamma x,y (k 1 ) Is a delay of k 1 Covariance, gamma, obtained after each step x,y (0) Is the delay step k 1 Covariance in case of =0, p x,y (k 1 ) Is a delay of k 1 Obtaining a cross-correlation coefficient after each step length;
when the cross correlation coefficient is larger than a first preset threshold value, two wind turbine generator sets are integrated into a virtual unified scheduling unit;
superposing the wind speed steeply-changed duty ratio time sequences of the plurality of wind generation sets in each virtual unified scheduling unit to obtain wind speed steeply-changed duty ratio time sequences of the plurality of virtual unified scheduling units;
determining the predictability of the wind speed steeply-changed duty ratio time sequence of each virtual unified scheduling unit by a Pearson autocorrelation analysis method, and taking the autocorrelation length of which the autocorrelation coefficient is greater than a second preset threshold value as the optimal forecast length of the corresponding virtual unified scheduling unit;
based on the optimal forecast length of each virtual unified unit, a plurality of forecast models are established for each virtual unified scheduling unit by using a BP neural network, the forecast wind speed steeply-changed duty ratio of the wind power plant group in the future area is obtained through the plurality of forecast models, and the intermittent quantitative depicting result of the wind power plant group in the area is obtained by using the forecast wind speed steeply-changed duty ratio.
2. The regional wind farm group forecasting and intermittent quantitative characterization method according to claim 1, wherein the obtaining of the time series of the wind speed steeply varying duty ratio of each wind turbine generator by using the historical wind speed data of each wind turbine generator in the regional wind farm group based on the definition of the wind speed steeply varying duty ratio specifically comprises:
obtaining a wind speed variation sequence { delta v (t) } of each wind turbine generator according to historical wind speed data of each wind turbine generator in the regional wind power plant group;
counting the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator, and obtaining a first steepness change threshold value theta of each wind turbine generator by utilizing a normal distribution confidence interval 1 And a second steeply varying threshold value theta 2 ；
Based on the definition of the wind speed abrupt change duty ratio, according to the first abrupt change threshold value theta of each wind turbine 1 And a second steeply varying threshold value theta 2 Calculating a time sequence of the wind speed abrupt change duty ratio of each wind turbine generator, wherein the definition of the wind speed abrupt change duty ratio is as follows:
where Δ t is a predetermined time interval, M is a length of a sequence of wind speed variation { Δ v (t) } at the predetermined time interval Δ t, and N is 1 And N 2 The times of the steep rise and the steep fall of the wind speed in the preset time period are respectively, and the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine is higher than a first steep variation threshold value theta 1 Is below a second steep threshold theta 2 The wind speed is steeply reduced in the second time period, and N is determined according to the number of times of steep rise of the wind speed in the first time period 1 Determining N according to the number of times of steep drop of wind speed in the second time period 2 。
3. The regional wind farm group forecasting and intermittent quantitative characterization method according to claim 1, wherein the method for determining the predictability of the wind speed steeply-varying duty cycle time sequence of each virtual unified scheduling unit by a Pearson autocorrelation analysis method, and the step of taking the autocorrelation length of which the autocorrelation coefficient is greater than a second preset threshold as the optimal forecasting length of the corresponding virtual unified scheduling unit specifically comprises the steps of:
calculating the autocorrelation coefficient of the wind speed abrupt change duty ratio time sequence of each virtual unified scheduling unit through a Pearson autocorrelation function, wherein the Pearson autocorrelation function is specifically as follows:
in the formula, k 2 Is a second predetermined delay step, n 2 For the length of the wind speed steeply varying duty cycle time series of the virtual unified scheduling unit,
is the mean value of the time series of the wind speed steeply changing duty ratio of the virtual unified scheduling unit>
The method is to make the wind speed of the original virtual unified scheduling unit change the time sequence x of the duty ratio suddenly t Delay k 2 Time series obtained after a step size, gamma (k) 2 ) Is a delay of k 2 Covariance after step size, γ (0) is delay step size k 2 Covariance when =0, ρ (k) 2 ) Is a delay of k 2 Obtaining autocorrelation coefficients after each step length;
and when the autocorrelation coefficient is larger than a second preset threshold value, taking the autocorrelation length as the optimal forecast length of the corresponding virtual unified dispatching unit.
4. The regional wind farm group forecasting and intermittent quantitative characterization method according to claim 3, wherein based on the optimal forecasting length of each virtual uniform unit, a plurality of forecasting models are established for each virtual uniform scheduling unit by using a BP neural network, the forecasted wind speed steeply-varying duty ratio of the regional wind farm group in the future is obtained through the plurality of forecasting models, and after the intermittent quantitative characterization result of the regional wind farm group is obtained by using the forecasted wind speed steeply-varying duty ratio, the method further comprises:
and (4) making an auxiliary scheduling scheme according to the forecasted wind speed abrupt change duty ratio of the regional wind power plant group and the intermittent quantitative characterization result.
5. The utility model provides a regional wind-powered electricity generation field crowd forecast and intermittent type nature ration depicting device which characterized in that includes:
the sequence generating unit is used for obtaining the wind speed abrupt change duty ratio time sequence of each wind generation set by using the historical wind speed data of each wind generation set in the regional wind power plant group based on the definition of the wind speed abrupt change duty ratio;
the cross-correlation analysis unit is used for determining the correlation of the wind speed abrupt change duty ratio time sequences of different wind generation sets through a Pearson cross-correlation analysis method and aggregating a plurality of wind generation sets of which the cross-correlation coefficients are larger than a first preset threshold value into a virtual unified scheduling unit; and is also used for:
calculating the cross-correlation coefficient of the wind speed abrupt change duty ratio time sequence of every two wind turbine generators through a Pearson cross-correlation function, wherein the Pearson cross-correlation function specifically comprises the following steps:
in the formula, k 1 Is a first predetermined delay step, n 1 For the length of the wind speed steeply varying duty cycle time series of the wind turbine,
and &>
Is the mean value of the time sequence of the wind speed steeply changing duty ratio of the two wind turbine generators, and is used for changing the duty ratio of the wind turbine generators>
The method is to make the wind speed of the wind turbine generator set change the duty ratio time sequence y t Delay k 1 Time series obtained after a step size, gamma x,y (k 1 ) Is a delay of k 1 Covariance, gamma, obtained after step size x,y (0) Is the delay step k 1 Covariance in case of =0, p x,y (k 1 ) Is a delay of k 1 Obtaining a cross-correlation coefficient after each step length;
when the cross correlation coefficient is larger than a first preset threshold value, two wind turbine generator sets are integrated into a virtual unified scheduling unit;
the superposition unit is used for superposing the wind speed steeply-changed duty ratio time sequences of the plurality of wind generation sets in each virtual unified scheduling unit to obtain the wind speed steeply-changed duty ratio time sequences of the plurality of virtual unified scheduling units;
the autocorrelation analysis unit is used for determining the predictability of the wind speed steeply-changed duty ratio time sequence of each virtual unified scheduling unit by a Pearson autocorrelation analysis method, and taking the autocorrelation length of which the autocorrelation coefficient is greater than a second preset threshold value as the optimal forecast length of the corresponding virtual unified scheduling unit;
and the forecasting and describing unit is used for establishing a plurality of forecasting models by utilizing a BP neural network aiming at each virtual unified scheduling unit based on the optimal forecasting length of each virtual unified unit, obtaining the forecasted wind speed steeply-changed duty ratio of the wind power plant group in the future area through the plurality of forecasting models, and obtaining the intermittent quantitative describing result of the wind power plant group in the area by utilizing the forecasted wind speed steeply-changed duty ratio.
6. The regional wind farm crowd forecasting and intermittent quantitative characterization device according to claim 5, wherein the sequence generation unit is further configured to:
obtaining a wind speed variation sequence { delta v (t) } of each wind turbine generator according to historical wind speed data of each wind turbine generator in the regional wind power plant group;
counting a wind speed variation sequence (delta v (t)) of each wind turbine generator) Obtaining a first steepness change threshold value theta of each wind turbine generator set by utilizing a normal distribution confidence interval 1 And a second steeply varying threshold value theta 2 ；
Based on the definition of the wind speed abrupt change duty ratio, according to the first abrupt change threshold value theta of each wind turbine 1 And a second steeply varying threshold value theta 2 Calculating a time sequence of the wind speed abrupt change duty ratio of each wind turbine generator, wherein the definition of the wind speed abrupt change duty ratio is as follows:
where Δ t is a predetermined time interval, M is a length of a sequence of wind speed variation { Δ v (t) } at the predetermined time interval Δ t, and N is 1 And N 2 The times of the steep rise and the steep fall of the wind speed in the preset time period are respectively, and the distribution of the wind speed variation sequence (delta v (t)) of each wind turbine generator is higher than a first steep change threshold value theta 1 Is increased sharply below a second steep threshold theta 2 The wind speed is steeply reduced in the second time period, and N is determined according to the number of times of steep rise of the wind speed in the first time period 1 Determining N according to the number of times of steep drop of wind speed in the second time period 2 。
7. The regional wind farm crowd forecasting and intermittent quantitative characterization device according to claim 6, wherein the autocorrelation analysis unit is further configured to:
calculating an autocorrelation coefficient of a wind speed steeply-varied duty ratio time sequence of each virtual unified scheduling unit through a Pearson autocorrelation function, wherein the Pearson autocorrelation function is specifically as follows:
in the formula, k 2 Is a second predetermined delay step, n 2 For the length of the wind speed ramp duty cycle time series of the virtual unified scheduling unit,
is the mean value of the time sequence of the wind speed steeply changing duty ratio of the virtual unified scheduling unit>
The wind speed of the original virtual unified scheduling unit is changed into the time sequence x of the duty ratio sharply t Delay k 2 Time series obtained after a step size, gamma (k) 2 ) Is a delay of k 2 Covariance after step size, γ (0) is delay step size k 2 Covariance when =0, ρ (k) 2 ) Is a delay of k 2 Obtaining autocorrelation coefficients after each step;
and when the autocorrelation coefficient is larger than a second preset threshold value, taking the autocorrelation length as the optimal forecast length of the corresponding virtual unified dispatching unit.
8. The device for regional wind farm crowd forecasting and intermittent quantitative characterization according to claim 7, further comprising:
and the scheduling scheme making unit is used for making an auxiliary scheduling scheme according to the forecast wind speed abrupt change duty ratio of the regional wind power plant group and the intermittent quantitative depicting result.
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