CN109960818B - Method and device for generating simulated wind speed data of wind power plant - Google Patents
Method and device for generating simulated wind speed data of wind power plant Download PDFInfo
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Abstract
A method and apparatus for generating simulated wind speed data for a wind farm is provided. The method comprises the following steps: acquiring historical wind speed data of a specific wind power plant in a specific time period, wherein the historical wind speed data comprises historical wind speed values of all time points in the specific time period; counting the differential distribution situation of the historical wind speed data, wherein the differential distribution situation of the historical wind speed data indicates the distribution situation of differential values between the historical wind speed values of adjacent time points; generating random wind speed data conforming to the differential distribution condition of the historical wind speed data based on the historical wind speed data, wherein the random wind speed data comprises random wind speed values of all time points in the specific time period; and taking the generated random wind speed data as the simulated wind speed data of the current wind power plant in the specific time period.
Description
Technical Field
The present invention relates generally to the field of wind power generation technology, and more particularly, to a method and apparatus for generating simulated wind speed data for a wind farm.
Background
With the large-scale development of wind power generation, the total installed capacity of wind power is larger and larger, and the wind power generation gradually becomes an important energy role. In the wind power related field, it is often necessary to generate some random wind speed data similar in characteristics to the actual wind speed data for testing a program related to wind speed, or for drawing some display diagrams related to wind speed data (e.g., curves of wind speed data), or the like.
Currently, the corresponding random wind speed data is usually generated based on the weibull distribution of the actual wind speed data. However, the random wind speed data generated in this way has a similar weibull distribution parameter to that of the actual wind speed data, but the fluctuation characteristics are different greatly from those of the actual wind speed data, and it is difficult to meet the application requirements.
For example, fig. 2 shows a graph of random wind speed data generated based on the weibull distribution parameters of the actual wind speed data shown in fig. 1, and it can be seen that the graph of random wind speed data generated fluctuates more severely locally than the graph of actual wind speed data.
Disclosure of Invention
The invention provides a method and a device for generating simulated wind speed data of a wind power plant, which can solve the problem of large fluctuation characteristic difference between the generated simulated wind speed data and actual wind speed data in the prior art.
According to an exemplary embodiment of the present invention, there is provided a method of generating simulated wind speed data of a wind farm, the method comprising: acquiring historical wind speed data of a specific wind power plant in a specific time period, wherein the historical wind speed data comprises historical wind speed values of all time points in the specific time period; counting the differential distribution situation of the historical wind speed data, wherein the differential distribution situation of the historical wind speed data indicates the distribution situation of differential values between the historical wind speed values of adjacent time points; generating random wind speed data conforming to the differential distribution condition of the historical wind speed data based on the historical wind speed data, wherein the random wind speed data comprises random wind speed values of all time points in the specific time period; and taking the generated random wind speed data as the simulated wind speed data of the current wind power plant in the specific time period.
Optionally, the step of counting the differential distribution of the historical wind speed data includes: dividing a wind speed value range of the historical wind speed data into a plurality of wind speed sections at predetermined intervals; determining a wind speed section to which a historical wind speed value at each time point belongs; and respectively counting the differential distribution condition of each wind speed section, wherein the differential distribution condition of the wind speed section indicates the distribution condition of the differential value between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value.
Optionally, the step of generating random wind speed data according to the differential distribution situation of the historical wind speed data based on the historical wind speed data comprises: for each point in time within the specific time period, the following steps are performed: determining a wind speed section to which a historical wind speed value of a previous time point of the time point belongs; generating a random differential value which accords with the differential distribution condition of the determined wind speed section; and superposing the historical wind speed value of the last time point of the time point and the generated random differential value to obtain the random wind speed value of the time point.
Optionally, the step of separately counting the differential distribution of each wind speed segment includes: for each wind speed segment, the following steps are respectively executed: counting probability distribution of difference values between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value falling in each difference section, wherein the lengths of the difference sections are the same; the statistical probability distribution of the differential values of the wind speed section is converted into the cumulative probability distribution of the differential values of the wind speed section.
Optionally, the step of generating a random differential value that corresponds to the differential distribution of the determined wind speed segments comprises: generating a uniform random number which is more than or equal to 0 and less than or equal to 1; determining a probability interval of cumulative probability distribution of differential values of the determined wind speed sections corresponding to the generated uniform random numbers; and generating a differential value based on the differential segment corresponding to the determined probability interval as a random differential value.
Optionally, the specific wind farm is one of a current wind farm, a neighboring wind farm of the current wind farm, and a wind farm similar to a wind speed characteristic of the current wind farm.
Optionally, the step of using the generated random wind speed data as simulated wind speed data for the specific period of time of the current wind farm comprises: counting the overall distribution condition of the historical wind speed data in the specific time period; counting the overall distribution condition of the random wind speed data in the specific time period; when the difference between the overall distribution condition of the historical wind speed data and the overall distribution condition of the random wind speed data meets a preset condition, the random wind speed data is used as simulated wind speed data in the specific time period of the current wind farm.
Optionally, the overall distribution of the historical wind speed data over the specific time period includes: the Weibull distribution condition of the historical wind speed data in the specific time period; the overall distribution of the random wind speed data over the specific time period includes: the random wind speed data is distributed in Weibull within the specific time period.
According to another exemplary embodiment of the present invention, there is provided an apparatus for generating simulated wind speed data of a wind farm, the apparatus comprising: a historical wind speed data acquisition unit configured to acquire historical wind speed data within a specific time period of a specific wind farm, wherein the historical wind speed data includes historical wind speed values at various time points within the specific time period; a differential distribution statistics unit configured to count differential distribution conditions of the historical wind speed data, wherein the differential distribution conditions of the historical wind speed data indicate distribution conditions of differential values between the historical wind speed values of adjacent time points; a random wind speed data generation unit configured to generate random wind speed data conforming to a differential distribution condition of the historical wind speed data based on the historical wind speed data, wherein the random wind speed data includes random wind speed values at each point in time within the specific period; and the simulated wind speed data determining unit is configured to take the generated random wind speed data as the simulated wind speed data in the specific time period of the current wind farm.
Optionally, the differential distribution statistics unit is further configured to: dividing a wind speed value range of the historical wind speed data into a plurality of wind speed sections at predetermined intervals; determining a wind speed section to which a historical wind speed value at each time point belongs; and respectively counting the differential distribution condition of each wind speed section, wherein the differential distribution condition of the wind speed section indicates the distribution condition of the differential value between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value.
Optionally, the random wind speed data generating unit is further configured to perform the following operations, respectively, for each point in time within the specific time period: determining a wind speed section to which a historical wind speed value of a previous time point of the time point belongs; generating a random differential value which accords with the differential distribution condition of the determined wind speed section; and superposing the historical wind speed value of the last time point of the time point and the generated random differential value to obtain the random wind speed value of the time point.
Optionally, the differential distribution statistics unit is further configured to perform the following operations for each wind speed segment separately: counting probability distribution of difference values between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value falling in each difference section, wherein the lengths of the difference sections are the same; the statistical probability distribution of the differential values of the wind speed section is converted into the cumulative probability distribution of the differential values of the wind speed section.
Optionally, the random wind speed data generating unit is further configured to: generating a uniform random number which is more than or equal to 0 and less than or equal to 1; determining a probability interval of cumulative probability distribution of differential values of the determined wind speed sections corresponding to the generated uniform random numbers; and generating a differential value based on the differential segment corresponding to the determined probability interval as a random differential value.
Optionally, the specific wind farm is one of a current wind farm, a neighboring wind farm of the current wind farm, and a wind farm similar to a wind speed characteristic of the current wind farm.
Optionally, the simulated wind speed data determination unit is further configured to: counting the overall distribution condition of the historical wind speed data in the specific time period; counting the overall distribution condition of the random wind speed data in the specific time period; when the difference between the overall distribution condition of the historical wind speed data and the overall distribution condition of the random wind speed data meets a preset condition, the random wind speed data is used as simulated wind speed data in the specific time period of the current wind farm.
Optionally, the overall distribution of the historical wind speed data over the specific time period includes: the Weibull distribution condition of the historical wind speed data in the specific time period; the overall distribution of the random wind speed data over the specific time period includes: the random wind speed data is distributed in Weibull within the specific time period.
According to another exemplary embodiment of the invention, a computer readable storage medium storing a computer program is provided, which when executed by a processor implements a method of generating simulated wind speed data of a wind farm as described above.
According to another exemplary embodiment of the present invention, there is provided a computing device including: a processor; a memory storing a computer program which, when executed by a processor, implements a method of generating simulated wind speed data for a wind farm as described above.
According to the method and the device for generating the simulated wind speed data of the wind power plant, disclosed by the embodiment of the invention, the generated simulated wind speed data and the historical wind speed data are approximate in overall distribution condition and fluctuation characteristic, and can meet application requirements.
Additional aspects and/or advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.
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The foregoing and other objects and features of exemplary embodiments of the invention will become more apparent from the following description taken in conjunction with the accompanying drawings which illustrate exemplary embodiments in which:
FIG. 1 shows an example of a curve of actual wind speed data;
FIG. 2 shows an example of a curve of random wind speed data generated according to the prior art;
FIG. 3 illustrates a flowchart of a method of generating simulated wind speed data for a wind farm according to an exemplary embodiment of the invention;
FIG. 4 illustrates a flowchart of a method of counting differential distribution of historical wind speed data according to an exemplary embodiment of the invention;
FIG. 5 shows a histogram of differential distribution of different wind speed segments according to an exemplary embodiment of the invention;
FIG. 6 illustrates a flowchart of a method of generating random wind speed data according to an exemplary embodiment of the present invention;
FIG. 7 shows an example of a plot of historical wind speed data;
FIG. 8 illustrates an example of a plot of random wind speed data generated in accordance with an exemplary embodiment of the present invention;
FIG. 9 shows a block diagram of an apparatus for generating simulated wind speed data for a wind farm according to an exemplary embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments will be described below in order to explain the present invention by referring to the figures.
FIG. 3 illustrates a flowchart of a method of generating simulated wind speed data for a wind farm according to an exemplary embodiment of the invention. Here, the wind farm is a wind farm that needs to generate simulated wind speed data, hereinafter referred to as a current wind farm.
Referring to fig. 3, in step S10, historical wind speed data for a specific period of time for a specific wind farm is acquired.
Here, the historical wind speed data includes: historical wind speed values for each point in time within the specified time period. As an example, the historical wind speed value may be an actual detected wind speed value.
It should be appreciated that adjacent points in time within the particular time period are spaced apart by a predetermined time interval. As an example, the predetermined time interval may be 15 minutes.
As an example, the specific wind farm may be one of a current wind farm, a neighboring wind farm of the current wind farm, and a wind farm similar to a wind speed characteristic of the current wind farm.
In step S20, a differential distribution of the historical wind speed data is counted. Here, the differential distribution of the historical wind speed data indicates a distribution of differential values between the historical wind speed values at adjacent points in time.
An exemplary embodiment of a method of counting the differential distribution of the historical wind speed data will be described in detail with reference to fig. 4 and 5.
In step S30, random wind speed data conforming to a differential distribution condition of the historical wind speed data is generated based on the historical wind speed data. Here, the random wind speed data includes: random wind speed values at various points in time within the specified time period.
An exemplary embodiment of a method of generating random wind speed data conforming to the differential distribution of the historical wind speed data will be described in detail below with reference to fig. 6.
In step S40, the generated random wind speed data is taken as simulated wind speed data in the specific time period of the current wind farm. The overall distribution situation over the specified period of time is approximated between the simulated wind speed data and the historical wind speed data generated according to the exemplary embodiment of the present invention, and the fluctuation characteristics over the specified period of time are also approximated.
As an example, the generated random wind speed data may be directly taken as simulated wind speed data within the specific time period of the current wind farm.
As another example, the overall distribution of the historical wind speed data over the specific time period may be counted, and the overall distribution of the generated random wind speed data over the specific time period may be counted; then, when a difference between the overall distribution of the historical wind speed data and the overall distribution of the random wind speed data satisfies a preset condition, the random wind speed data may be used as simulated wind speed data in the specific time period of the current wind farm.
Further, as an example, the overall distribution of the historical wind speed data over the particular time period may include: a weibull distribution (Weibull distribution) profile of the historical wind speed data over the specified period of time; the overall distribution of the random wind speed data over the particular time period may include: the random wind speed data is distributed in Weibull within the specific time period.
From a probabilistic and statistical perspective, a weibull distribution is a continuous probability distribution whose probability density f can be represented by the following formula:
where x is a random variable, λ is a scale parameter (scale parameter), and k is a shape parameter (shape parameter), where λ > 0, k > 0.
For example, when a difference between a weibull distribution parameter λ of the historical wind speed data and a weibull distribution parameter λ of the random wind speed data is less than a first preset threshold, and a difference between a weibull distribution parameter k of the historical wind speed data and a weibull distribution parameter k of the random wind speed data is less than a second preset threshold, the random wind speed data may be regarded as simulated wind speed data within the specific period of time of the current wind farm.
In addition, the overall distribution of the historical wind speed data and the random wind speed data within the specific time period can be measured through other distribution statistical parameters. For example, the distribution statistical parameter may include at least one of: mean, variance, standard deviation.
FIG. 4 illustrates a flowchart of a method of counting differential distribution of historical wind speed data according to an exemplary embodiment of the invention. Here, the method may be performed when step S20 is performed.
In step S201, a wind speed value range of the historical wind speed data is divided into a plurality of wind speed segments at predetermined intervals.
As an example, the predetermined interval may be 1m/s or 0.5 m/s. For example, when the predetermined interval is 1m/s, the wind speed value range of the historical wind speed data is segmented with 1m/s step, for example, divided into: a plurality of wind speed segments of [0, 1), [1, 2), [2, 3), etc.
In step S202, a wind speed segment to which the historical wind speed value at each point in time belongs is determined.
In step S203, the differential distribution of each wind speed segment is counted. Here, the differential distribution of the wind speed section indicates a distribution of differential values between a historical wind speed value within the wind speed section and a historical wind speed value at a next time point of the historical wind speed value. Here, the difference value between the historical wind speed value and the historical wind speed value at the next time point of the historical wind speed value may be: the difference obtained by subtracting the historical wind speed value from the historical wind speed value at the next time point of the historical wind speed value.
As an example, the following steps may be performed separately for each wind speed segment to obtain a differential distribution of each wind speed segment: counting probability distribution of difference values between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value in each difference section; the statistical probability distribution of the differential values of the wind speed section is converted into the cumulative probability distribution of the differential values of the wind speed section. Here, it should be understood that the lengths of the differential segments are the same.
FIG. 5 shows a histogram of differential distribution of different wind speed segments according to an exemplary embodiment of the invention. As shown in fig. 5, the x-axis indicates the historical wind speed value for each data point, the y-axis indicates the difference between the historical wind speed value for the next time point for each data point and the historical wind speed value for that data point, and the z-axis indicates the number of data points within the respective distribution areas, i.e., the number of data points within the area formed by each wind speed segment and each differential segment.
FIG. 6 illustrates a flowchart of a method of generating random wind speed data that conforms to the differential distribution of the historical wind speed data, according to an exemplary embodiment of the invention. Here, the method may be performed when step S30 is performed, and steps S301 to S303 may be performed for each point of time within the specific period of time, respectively, to generate a random wind speed value for each point of time.
In step S301, a wind speed period to which a historical wind speed value at a time point immediately preceding the time point belongs is determined.
In step S302, a random differential value is generated that corresponds to the differential distribution of the determined wind speed segment.
As an example, when the differential distribution condition of the wind speed section is the cumulative probability distribution condition of the differential value of the wind speed section, step S302 may include the steps of: generating a uniform random number which is more than or equal to 0 and less than or equal to 1; then, a probability interval of a cumulative probability distribution of the differential values of the wind speed segments determined in step S301 corresponding to the generated uniform random numbers is determined; next, a differential value is generated as a random differential value based on the differential segment corresponding to the determined probability interval.
As an example, a differential value corresponding to one end point of the differential segment corresponding to the determined probability interval may be used as the random differential value; alternatively, the median value of the differential segment corresponding to the determined probability interval (i.e., the average value of the differential values corresponding to the two endpoints) may be used as the random differential value. Further, as an example, a value belonging to a differential segment corresponding to the determined probability interval may also be randomly generated as the random differential value.
In step S303, the historical wind speed value of the previous time point at the time point and the generated random differential value are superimposed to obtain a random wind speed value at the time point.
For example, when the historical wind speed value at the previous time point within the specific time period is 5m/S, in step S301, it is determined that the wind speed period to which 5m/S belongs is [5,6 ], for example, if 4 historical wind speed values belong to the wind speed period of [5, 6), the difference value between each historical wind speed value and the historical wind speed value at the next time point is: -0.5, 0, 0.5, 1, then [5, 6) the probability distribution of the difference value-0.5, 0, 0.5, 1, falling on the difference segment [ -1, 0), [0, 1), [1, 2) for this wind speed segment is: 0.25, 0.5, 0.25, respectively, [5, 6) the cumulative probability distribution of the differential values, -0.5, 1, falling within the differential segment [ -1, 0), [0, 1), [1, 2), for this wind speed segment is: the probability intervals of the cumulative probability distribution are [0,0.25], (0.25,0.75 ], (0.75,1 ], corresponding to the difference segments [ -1, 0), [0, 1), [1,2 ], respectively, of 0.25,0.75, 1. Then, in step S302, a uniform random number of 0 or more and 1 or less is generated, for example, if the generated uniform random number is 0.4, since the interval of the cumulative probability distribution corresponding to the uniform random number 0.4 is (0.25,0.75) and the difference segment corresponding to this interval is [0, 1], a difference value may be generated as a random difference value based on the difference segment [0, 1], for example, the left end point 0m/S of the difference segment [0, 1] may be used as a random difference value. Next, in step S303, the historical wind speed value of 5m/S at the time point immediately before the time point and the generated random differential value of 0m/S are superimposed, and the random wind speed value of 5m/S at the time point is obtained.
FIG. 8 illustrates a graph of random wind speed data generated based on the historical wind speed data shown in FIG. 7, wherein the Weibull distribution parameter λ of the historical wind speed data shown in FIG. 7 is 8.2033 and k is 1.7307, as can be seen from the general distribution of the two, according to an exemplary embodiment of the invention; the random wind speed data shown in fig. 8 has a weibull distribution parameter λ of 8.6814 and a k of 2.0676, and the difference between the weibull distribution parameters is small and within an acceptable range. The fluctuation characteristics of the wind speed sensor and the wind speed sensor are similar, and the generated random wind speed data has certain fluctuation and quite continuity in a short time.
FIG. 9 shows a block diagram of an apparatus for generating simulated wind speed data for a wind farm according to an exemplary embodiment of the invention. As shown in fig. 9, an apparatus for generating simulated wind speed data of a wind farm according to an exemplary embodiment of the present invention includes: a historical wind speed data acquisition unit 10, a differential distribution statistics unit 20, a random wind speed data generation unit 30, and a simulated wind speed data determination unit 40.
Specifically, the historical wind speed data obtaining unit 10 is configured to obtain historical wind speed data within a specific time period of a specific wind farm, wherein the historical wind speed data includes historical wind speed values at various points in time within the specific time period.
As an example, the specific wind farm may be one of a current wind farm, a neighboring wind farm of the current wind farm, and a wind farm similar to a wind speed characteristic of the current wind farm.
The differential distribution statistics unit 20 is configured to count differential distribution situations of the historical wind speed data, wherein the differential distribution situations of the historical wind speed data indicate distribution situations of differential values between the historical wind speed values of adjacent time points.
As an example, the differential distribution statistics unit 20 may be further configured to: dividing a wind speed value range of the historical wind speed data into a plurality of wind speed sections at predetermined intervals; determining a wind speed section to which a historical wind speed value at each time point belongs; and respectively counting the differential distribution condition of each wind speed section, wherein the differential distribution condition of the wind speed section indicates the distribution condition of the differential value between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value.
As an example, the differential distribution statistics unit 20 may be further configured to perform the following operations for each wind speed segment, respectively: counting probability distribution of difference values between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value falling in each difference section, wherein the lengths of the difference sections are the same; the statistical probability distribution of the differential values of the wind speed section is converted into the cumulative probability distribution of the differential values of the wind speed section.
The random wind speed data generating unit 30 is configured to generate random wind speed data conforming to a differential distribution situation of the historical wind speed data based on the historical wind speed data, wherein the random wind speed data includes random wind speed values at respective time points within the specific time period.
As an example, the random wind speed data generating unit 30 may be further configured to perform the following operations, respectively, for each point in time within the specific time period: determining a wind speed section to which a historical wind speed value of a previous time point of the time point belongs; generating a random differential value which accords with the differential distribution condition of the determined wind speed section; and superposing the historical wind speed value of the last time point of the time point and the generated random differential value to obtain the random wind speed value of the time point.
As an example, the random wind speed data generation unit 30 may be further configured to: generating a uniform random number which is more than or equal to 0 and less than or equal to 1; determining a probability interval of cumulative probability distribution of differential values of the determined wind speed sections corresponding to the generated uniform random numbers; and generating a differential value based on the differential segment corresponding to the determined probability interval as a random differential value.
The simulated wind speed data determination unit 40 is configured to take the generated random wind speed data as simulated wind speed data within the specific time period of the current wind farm.
As an example, the simulated wind speed data determination unit 40 may be further configured to: counting the overall distribution condition of the historical wind speed data in the specific time period; counting the overall distribution condition of the random wind speed data in the specific time period; when the difference between the overall distribution condition of the historical wind speed data and the overall distribution condition of the random wind speed data meets a preset condition, the random wind speed data is used as simulated wind speed data in the specific time period of the current wind farm.
As an example, the overall distribution of the historical wind speed data over the particular time period may include: the Weibull distribution condition of the historical wind speed data in the specific time period; the overall distribution of the random wind speed data over the particular time period may include: the random wind speed data is distributed in Weibull within the specific time period.
It should be appreciated that the specific implementation of the apparatus for generating simulated wind speed data of a wind farm according to an exemplary embodiment of the present invention may be implemented with reference to the related specific implementations described in connection with fig. 3 to 8, and will not be described here again.
According to a computer readable storage medium storing a computer program according to an exemplary embodiment of the present invention, the method of generating simulated wind speed data of a wind farm described in the above exemplary embodiment is implemented when the computer program is executed by a processor.
A computing device according to an exemplary embodiment of the present invention includes: a processor (not shown) and a memory (not shown), wherein the memory stores a computer program which, when executed by the processor, implements the method of generating simulated wind speed data of a wind farm as described in the above exemplary embodiments.
According to the method and the device for generating the simulated wind speed data of the wind power plant, disclosed by the embodiment of the invention, the generated simulated wind speed data and the historical wind speed data are approximate in overall distribution condition and fluctuation characteristic, and can meet application requirements.
Furthermore, it should be understood that the various units in the apparatus for generating simulated wind speed data of a wind farm according to an exemplary embodiment of the present invention may be implemented as hardware components and/or as software components. The individual units may be implemented, for example, using a Field Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC), depending on the processing performed by the individual units as defined.
Furthermore, the method of generating simulated wind speed data of a wind farm according to an exemplary embodiment of the present invention may be implemented as computer code in a computer readable recording medium. The computer code may be implemented by those skilled in the art in light of the description of the above methods. The above-described method of the present invention is implemented when the computer code is executed in a computer.
Although a few exemplary embodiments of the present invention have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the claims and their equivalents.
Claims (18)
1. A method of generating simulated wind speed data for a wind farm, the method comprising:
acquiring historical wind speed data of a specific wind power plant in a specific time period, wherein the historical wind speed data comprises historical wind speed values of all time points in the specific time period;
counting the differential distribution situation of the historical wind speed data, wherein the differential distribution situation of the historical wind speed data indicates the distribution situation of differential values between the historical wind speed values of adjacent time points;
generating random wind speed data conforming to the differential distribution condition of the historical wind speed data based on the historical wind speed data, wherein the random wind speed data comprises random wind speed values of all time points in the specific time period;
and when the difference between the overall distribution condition of the random wind speed data and the overall distribution condition of the historical wind speed data meets a preset condition, taking the random wind speed data as the simulated wind speed data in the specific time period of the current wind farm.
2. The method of claim 1, wherein the step of counting a differential distribution of the historical wind speed data comprises:
dividing a wind speed value range of the historical wind speed data into a plurality of wind speed sections at predetermined intervals;
determining a wind speed section to which a historical wind speed value at each time point belongs;
and respectively counting the differential distribution condition of each wind speed section, wherein the differential distribution condition of the wind speed section indicates the distribution condition of the differential value between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value.
3. The method of claim 2, wherein generating random wind speed data that conforms to a differential distribution of the historical wind speed data based on the historical wind speed data comprises: for each point in time within the specific time period, the following steps are performed:
determining a wind speed section to which a historical wind speed value of a previous time point of the time point belongs;
generating a random differential value which accords with the differential distribution condition of the determined wind speed section;
and superposing the historical wind speed value of the last time point of the time point and the generated random differential value to obtain the random wind speed value of the time point.
4. A method according to claim 3, wherein the step of separately counting the differential distribution of each wind speed segment comprises: for each wind speed segment, the following steps are respectively executed:
counting probability distribution of difference values between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value falling in each difference section, wherein the lengths of the difference sections are the same;
the statistical probability distribution of the differential values of the wind speed section is converted into the cumulative probability distribution of the differential values of the wind speed section.
5. The method of claim 4, wherein the step of generating random differential values that correspond to the differential distribution of the determined wind speed segments comprises:
generating a uniform random number which is more than or equal to 0 and less than or equal to 1;
determining a probability interval of cumulative probability distribution of differential values of the determined wind speed sections corresponding to the generated uniform random numbers;
and generating a differential value based on the differential segment corresponding to the determined probability interval as a random differential value.
6. The method of claim 1, wherein the particular wind farm is one of a current wind farm, a neighboring wind farm to the current wind farm, and a wind farm similar to a wind speed characteristic of the current wind farm.
7. The method as recited in claim 1, further comprising:
counting the overall distribution condition of the historical wind speed data in the specific time period;
and counting the overall distribution condition of the random wind speed data in the specific time period.
8. The method of claim 7, wherein the step of determining the position of the probe is performed,
the overall distribution of the historical wind speed data over the particular time period includes: the Weibull distribution condition of the historical wind speed data in the specific time period;
the overall distribution of the random wind speed data over the specific time period includes: the random wind speed data is distributed in Weibull within the specific time period.
9. An apparatus for generating simulated wind speed data for a wind farm, the apparatus comprising:
a historical wind speed data acquisition unit configured to acquire historical wind speed data within a specific time period of a specific wind farm, wherein the historical wind speed data includes historical wind speed values at various time points within the specific time period;
a differential distribution statistics unit configured to count differential distribution conditions of the historical wind speed data, wherein the differential distribution conditions of the historical wind speed data indicate distribution conditions of differential values between the historical wind speed values of adjacent time points;
a random wind speed data generation unit configured to generate random wind speed data conforming to a differential distribution condition of the historical wind speed data based on the historical wind speed data, wherein the random wind speed data includes random wind speed values at each point in time within the specific period;
and a simulated wind speed data determining unit configured to take the random wind speed data as simulated wind speed data in the specific time period of the current wind farm when a difference between the overall distribution condition of the random wind speed data and the overall distribution condition of the historical wind speed data satisfies a preset condition.
10. The apparatus of claim 9, wherein the differential distribution statistics unit is further configured to:
dividing a wind speed value range of the historical wind speed data into a plurality of wind speed sections at predetermined intervals;
determining a wind speed section to which a historical wind speed value at each time point belongs;
and respectively counting the differential distribution condition of each wind speed section, wherein the differential distribution condition of the wind speed section indicates the distribution condition of the differential value between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value.
11. The apparatus according to claim 10, wherein the random wind speed data generation unit is further configured to perform the following operations, respectively, for each point in time within the specific time period:
determining a wind speed section to which a historical wind speed value of a previous time point of the time point belongs;
generating a random differential value which accords with the differential distribution condition of the determined wind speed section;
and superposing the historical wind speed value of the last time point of the time point and the generated random differential value to obtain the random wind speed value of the time point.
12. The apparatus of claim 11, wherein the differential distribution statistics unit is further configured to, for each wind speed segment, perform the following operations:
counting probability distribution of difference values between the historical wind speed value in the wind speed section and the historical wind speed value at the next time point of the historical wind speed value falling in each difference section, wherein the lengths of the difference sections are the same;
the statistical probability distribution of the differential values of the wind speed section is converted into the cumulative probability distribution of the differential values of the wind speed section.
13. The apparatus of claim 12, wherein the random wind speed data generation unit is further configured to:
generating a uniform random number which is more than or equal to 0 and less than or equal to 1;
determining a probability interval of cumulative probability distribution of differential values of the determined wind speed sections corresponding to the generated uniform random numbers;
and generating a differential value based on the differential segment corresponding to the determined probability interval as a random differential value.
14. The apparatus of claim 9, wherein the particular wind farm is one of a current wind farm, a neighboring wind farm to the current wind farm, and a wind farm similar to a wind speed characteristic of the current wind farm.
15. The apparatus according to claim 9, wherein the simulated wind speed data determination unit is further configured to:
counting the overall distribution condition of the historical wind speed data in the specific time period;
and counting the overall distribution condition of the random wind speed data in the specific time period.
16. The apparatus of claim 15, wherein the device comprises a plurality of sensors,
the overall distribution of the historical wind speed data over the particular time period includes: the Weibull distribution condition of the historical wind speed data in the specific time period;
the overall distribution of the random wind speed data over the specific time period includes: the random wind speed data is distributed in Weibull within the specific time period.
17. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements a method of generating simulated wind speed data of a wind farm as claimed in any of claims 1 to 8.
18. A computing device, the computing device comprising:
a processor;
memory storing a computer program which, when executed by a processor, implements a method of generating simulated wind speed data of a wind farm as claimed in any of claims 1 to 8.
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| CN106251242A (en) * | 2016-08-08 | 2016-12-21 | 东南大学 | A kind of wind power output interval combinations Forecasting Methodology |
| CN106407627A (en) * | 2016-11-23 | 2017-02-15 | 西南石油大学 | Wind speed probability distribution modeling method and system |
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| CN104794325A (en) * | 2015-03-10 | 2015-07-22 | 国家电网公司 | Colony wind power plant output timing sequence simulation method based on random difference equation |
| CN106251242A (en) * | 2016-08-08 | 2016-12-21 | 东南大学 | A kind of wind power output interval combinations Forecasting Methodology |
| CN106407627A (en) * | 2016-11-23 | 2017-02-15 | 西南石油大学 | Wind speed probability distribution modeling method and system |
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