CN110717277A - Time sequence wind speed simulation method - Google Patents
Time sequence wind speed simulation method Download PDFInfo
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Abstract
The invention relates to a time sequence wind speed simulation method, which mainly embodies the time sequence of wind speed by solving the accumulated distribution function of the variable quantities of two adjacent time sequences by segments of historical actual wind speed, namely the change condition of the adjacent time sequences can accurately represent the distribution condition similar to the actual wind speed. According to the n wind speed points with the least occurrence frequency in the historical wind speed data, the n wind speed points serve as the dividing points, the variation of the whole historical wind speed is counted, and the defect that wind speed interval selection has a large influence on simulation accuracy in the past wind speed modeling is overcome.
Description
Technical Field
The invention belongs to the technical field of wind speed modeling, and particularly relates to a time sequence wind speed simulation method.
Background
In recent years, with the continuous development of the wind power industry, the grid connection problem of wind power generation is increasingly severe. One of the main problems is the difference between regional wind speed modeling and actual wind speed distribution. At present, most wind speed simulation adopts a Weibull distribution point sampling method, and although the probability density of the actual wind speed can be completely represented, the description of the wind speed time sequence is lacked. This causes a certain deviation in the results of the wind power grid-connection planning.
The uncertainty of the wind speed is a core problem of wind speed modeling, and changes of the wind speed are affected by different weather, different seasons, temperature changes and the like, so that the previous wind speed data are counted, the wind speed change rule is analyzed according to the previous time sequence wind speed, and a new time sequence wind speed is simulated. Based on this, documents "jiang, horror 32704, zhulong, lujiayu, li hai peak. 22-27, a first-order Markov chain is used for calculating a time sequence change rule of the wind speed, different conversion probability matrixes are calculated according to different seasons, temperatures and time, and further actual wind speed is simulated.
Disclosure of Invention
The present invention is directed to provide a novel time-series wind speed simulation method that can effectively solve the above-mentioned technical problems. The method mainly comprises the step of solving the cumulative distribution function of the variable quantities of two adjacent time sequences by segments of historical actual wind speeds to show the time sequence of the wind speeds, namely the change conditions of the adjacent time sequences.
In order to achieve the purpose of the invention, the following technical scheme is adopted:
a time sequence wind speed simulation method is characterized by comprising the following steps:
step 1, obtaining historical wind speed data of the same region for a period of time;
step 2, analyzing the historical wind speed data of the period of time to obtain a historical wind speed distribution division point a of one year1、a2、a3…anDetermining the maximum wind speed Vmax of the historical wind speed;
step 3, dividing the historical wind speed data into points according to the wind speedThe wind speed range of (0, Vmax) is divided into n +1 wind speed sections, which are respectively (0, a)1)、(a1，a2)、(a2，a3)…(anVmax); extracting wind speed data of each wind speed interval, calculating wind speed variation delta V of each wind speed and the next time sequence in each wind speed interval so as to obtain n +1 groups of wind speed variation data, and applying the n +1 groups of wind speed variations to X1、X2、X3…Xn+1Represents;
step 4, respectively obtaining n +1 groups of wind speed variation data, namely X1、X2、X3…Xn+1The cumulative distribution function of (3), the cumulative distribution function being represented by equation (2);
Fi(Δv)＝P(ΔV≤Δv)(i∈(1，n+1)) (2)
wherein, Δ V is an independent variable, Δ V is a wind speed variation smaller than Δ V, and P is a probability that Δ V falls in a (0, Δ V) interval; fi(Δ v) is XiA cumulative distribution score of;
step 6, calculating the average value of the historical data of the whole wind speed as the initial wind speed V1Judgment of V1The wind speed interval of the n +1 groups is (a)j-1，aj) J ∈ (1, n +1), a number b between 0 and 1 is randomly generated1In the set XjIn the inverse function of the cumulative distribution function of (1), a wind speed variation Δ V is obtained1Adding it to V1Upper obtained wind speed V2Then, judging V again2At the position (a) of the wind speed intervalk-1，ak) K ∈ (1, n +1), a number b between 0 and 1 is randomly generated2In the set XkTo obtain a wind speed variation DeltaV2Adding it to V2Upper obtained wind speed V3And adding N times in such a way, wherein N is the number of points of the simulated wind speed, so as to generate the simulated wind speed for a period of time.
In step 1, the time scale of the historical wind speed data is one year, and the time interval of the historical data points of one year is 10 min.
In step 2, selecting a wind speed historical wind speed distribution division point, taking a wind speed point with lower occurrence probability in the regional historical wind speed data as a wind speed distribution division point, a1、a2、a3…anThe numerical value of (c) increases in order. Wind speed a1、a2、a3…anThe wind speed point is obtained by analyzing the historical wind speed of the region for years, and n depends on the actual conditions of different regions. n is an integer greater than 2.
In step 5, a corresponding inverse function of the cumulative distribution function is obtained, and an inverse function curve is obtained, wherein the abscissa is the distribution probability smaller than the variation Δ V in the range of 0 to 1, and the ordinate is the variation Δ V.
Compared with the prior art, the invention has the beneficial effects that:
(1) compared with the conventional random sampling method such as Weibull, the method simulates the time sequence of the wind speed and accords with the characteristic that the actual wind speed changes on a time sequence.
(2) The method establishes an accumulative distribution function by counting the variable quantity of the wind speed of the adjacent time sequence, and further simulates a section of wind speed sequence of the area in the similar distribution condition with the selected historical actual wind speed data.
(3) The method takes n wind speed points with the least occurrence frequency in historical wind speed data as the division points, but the selection of the division points has little influence on the accuracy of the simulated wind speed, and the variation of the whole historical wind speed is counted, namely the division points n only need to take a value larger than 2, so that the defect that the influence of the wind speed interval selection on the simulation precision is large in the past wind speed modeling is avoided.
Drawings
FIG. 1 is a plot of 5000 actual wind speed points for a history of a region.
Fig. 2 is a graph of 5000 wind speed points simulated based on a markov chain according to a historical wind speed.
FIG. 3 is a graph of 5000 wind speed points simulated based on the method of the present invention based on historical wind speeds.
FIG. 4 is a flow chart of the method of the present invention.
Detailed Description
Specific examples of the present invention are given below. The specific examples are only for illustrating the present invention in further detail and do not limit the scope of protection of the present application.
The invention relates to a wind speed time sequence simulation method (see figure 4), which comprises the following steps:
step 1, acquiring historical wind speed data M of the same region in one year;
step 2, finding out n wind speed points with the least occurrence frequency in the historical wind speed data, and determining the wind speed points as wind speed distribution division points, a1、a2、a3…anThe numerical values of (a) and (b) are sequentially increased;
step 3, dividing historical wind speed data of one year into n +1 wind speed intervals according to wind speed division points, wherein the n +1 wind speed intervals are respectively (0, a)1)、(a1，a2)、(a2，a3)…(anVmax), where Vmax is a maximum wind speed of the historical wind speeds, extracting wind speed data for each wind speed interval, calculating a wind speed variation Δ V of each wind speed in each interval and a next time series, thereby obtaining n +1 groups of wind speed variation data, and using X for the n +1 groups of wind speed variations1、X2、X3…Xn+1And (4) showing. If the wind speed at the moment t is VaThe wind speed at the time of t +1 is VbAnd V isa∈(ai-1，ai) Calculating Δ VXiAs shown in formula (1), Δ VXi∈Xi。
ΔVXi＝Vb-Va(1)
Wherein X1Is the interval (0, a)1) The set of variations between each wind speed point and the next time series, because of Va∈(ai-1，ai) Therefore, is recorded as Δ VXi(ΔVXi∈Xi)。
Step 4, calculating through MATLAB software, and respectively obtaining n +1 groups of wind speed variation data, namely X, by using an ecdf function1、X2、X3…Xn+1The cumulative distribution function of (2);
Fi(Δv)＝P(ΔV≤Δv)(i∈(1，n+1)) (2)
wherein Δ V is an independent variable, and Δ V is a wind speed variation smaller than Δ V. P is the probability that Δ V falls within the interval (0, Δ V).
Fi -1(P)＝Δv(i∈(1，n+1)) (3)
step 6, calculating the average value of the historical data of the whole wind speed as the initial wind speed V1Judgment of V1The wind speed interval of the n +1 groups is (a)j-1，aj) J ∈ (1, n +1), a number b between 0 and 1 is randomly generated1In the set XjIn the inverse function of the cumulative distribution function of (1), a wind speed variation Δ V is obtained1Adding it to V1Upper obtained wind speed V2Then, judging V again2At the position (a) of the wind speed intervalk-1，ak) K ∈ (1, n +1), a number b between 0 and 1 is randomly generated2In the set XkTo obtain a wind speed variation DeltaV2Adding it to V2Upper obtained wind speed V3And adding N times in such a way, wherein N is the number of points of the simulated wind speed, so as to generate the simulated wind speed for a period of time.
b1＝rand(1) (4)
Examples
The time sequence wind speed simulation method comprises the following steps:
step 1, acquiring historical wind speed data M of the same region in one year, and simulating the wind speed of the region in the same year as the historical wind speed;
step 2, randomly taking the number n larger than 2, finding out n wind speed points with the minimum occurrence frequency in the historical wind speed data, and obtaining the historical wind speed distribution in the wind speed a of one year1、a2、a3…anThe probability of the wind speed around the wind speed point is lower and is ensuredDetermining the plurality of wind speed points as wind speed distribution division points;
step 3, dividing historical wind speed data of one year into n +1 wind speed intervals according to wind speed division points, wherein the n +1 wind speed intervals are respectively (0, a)1)、(a1，a2)、(a2，a3)…(anVmax), where Vmax is a maximum wind speed of the historical wind speeds, extracting wind speed data for each wind speed interval, calculating a wind speed variation Δ V of each wind speed in each interval and a next time series, thereby obtaining n +1 groups of wind speed variation data, and applying the n +1 groups of wind speed variations to X1、X2、X3…Xn+1And (4) showing. If the wind speed at the moment t is VaThe wind speed at the time of t +1 is VbAnd V isa∈(ai-1，ai) Calculating Δ VXiAs shown in formula (1), Δ VXi∈Xi。
ΔVXi＝Vb-Va(1)
Wherein X1Is the interval (0, a)1) The set of variations between each wind speed point and the next time series, because of Va∈(ai-1，ai) Therefore, is recorded as Δ VXi(ΔVXi∈Xi)。
Step 4, calculating through MATLAB software, and respectively obtaining n +1 groups of wind speed variation data, namely X, by using an ecdf function1、X2、X3…Xn+1The cumulative distribution function of (2);
Fi(Δv)＝P(ΔV≤Δv)(i∈(1，n+1)) (2)
wherein Δ V is an independent variable, and Δ V is a wind speed variation smaller than Δ V. P is the probability that Δ V falls within the interval (0, Δ V).
Fi -1(P)＝Δv(i∈(1，n+1)) (3)
step 6, calculating the average value of the historical data of the whole wind speed as the initial wind speed V1Judgment of V1Wind speed of n +1 groupOf interval (a)j-1，aj) Interval, j ∈ (1, n +1), randomly generating a number b between 0 and 11In b with1As an initial probability P in the inverse function, b1Bring into set XjIn the inverse function of the cumulative distribution function of (1), a wind speed variation Δ V is obtained1(see equation (5)), which is added to V1Upper obtained wind speed V2Then, judging V again2At the position (a) of the wind speed intervalk-1，ak) K ∈ (1, n +1), a number b between 0 and 1 is randomly generated2B is mixing2Bring into set XkTo obtain a wind speed variation DeltaV2Adding it to V2Upper obtained wind speed V3And adding N times in such a way, wherein N is the number of points of the simulated wind speed, so as to generate the simulated wind speed for a period of time.
b1＝rand(1) (4)
A part of historical wind speed data of one year in a region is taken as 5000 wind speed points, and as shown in figure 1, for the sake of more intuitive comparison, a simulated wind speed point N is also taken as 5000. Fig. 2 shows 5000 curves of wind speed points (divided into 6 intervals, i.e. 6 scenes) simulated based on a first-order markov chain according to the selected historical wind speed. Fig. 3 shows 5000 wind speed points (n is 5, and the maximum wind speed is 25.2m/s) simulated by applying the method of the invention according to the selected historical wind speed. Fig. 2 is still a larger difference from the actual wind speed distribution of fig. 1, and fig. 3 is substantially consistent with the actual wind speed distribution by applying the method of the present invention.
Nothing in this specification is said to apply to the prior art.
Claims (4)
1. A time sequence wind speed simulation method is characterized by comprising the following steps:
step 1, obtaining historical wind speed data of the same region for a period of time;
step 2, analyzing the historical wind speed data of the period of time to obtain historical wind speed distribution segmentation of one yearPoint a1、a2、a3…anDetermining the maximum wind speed Vmax of the historical wind speed;
step 3, dividing the historical wind speed data into n +1 wind speed intervals (0, Vmax) according to the wind speed division points, wherein the wind speed intervals are (0, a)1)、(a1,a2)、(a2,a3)…(anVmax); extracting wind speed data of each wind speed interval, calculating wind speed variation delta V of each wind speed and the next time sequence in each wind speed interval so as to obtain n +1 groups of wind speed variation data, and applying the n +1 groups of wind speed variations to X1、X2、X3…Xn+1Represents;
step 4, respectively obtaining n +1 groups of wind speed variation data, namely X1、X2、X3…Xn+1The cumulative distribution function of (3), the cumulative distribution function being represented by equation (2);
Fi(Δv)＝P(ΔV≤Δv)(i∈(1,n+1)) (2)
wherein, Δ V is an independent variable, Δ V is a wind speed variation smaller than Δ V, and P is a probability that Δ V falls in a (0, Δ V) interval; fi(Δ v) is XiA cumulative distribution score of;
step 5, solving a corresponding inverse function of the n +1 groups of wind speed variation data accumulated distribution functions;
step 6, calculating the average value of the historical data of the whole wind speed as the initial wind speed V1Judgment of V1The wind speed interval of the n +1 groups is (a)j-1,aj) J ∈ (1, n +1), a number b between 0 and 1 is randomly generated1In the set XjIn the inverse function of the cumulative distribution function of (1), a wind speed variation Δ V is obtained1Adding it to V1Upper obtained wind speed V2Then, judging V again2At the position (a) of the wind speed intervalk-1,ak) K ∈ (1, n +1), a number b between 0 and 1 is randomly generated2In the set XkTo obtain a wind speed variation DeltaV2Adding it to V2Upper obtained wind speed V3So as to add N times, N being the number of points of the simulated wind speed, thereby generatingSimulated wind speed over a period of time.
2. The method of claim 1, wherein in step 1, the time scale of the historical wind speed data is one year, and the time interval of the historical data points of one year is 10 min.
3. The method as claimed in claim 1, wherein in step 2, a wind speed distribution dividing point is selected from the wind speed distribution historical wind speed distribution dividing points, and a wind speed point with a lower probability of occurrence in the regional historical wind speed data is used as the wind speed distribution dividing point1、a2、a3…anThe numerical value of (c) increases in order.
4. The method of claim 1, wherein n is an integer greater than 2.
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